• This is a collection of slides which I often get asked to share and I thought simply because of its size, it may be easier to make it downloadable from this page(Slides).Mini-version of my slides customised for CDF Students, UNSW-Canberra(CDF-Seminar).



    • Publications: Citations Reports: Google Scholar , SCOPUS , Researcher ID and ORCID

    Journal Papers
    1. Colombo, A., Chiastra, C., Gallo, D., Loh, P.H., Dokos, S., Zhang, M., Keramati, H., Carbonaro, D., Migliavacca, F., Ray, T., Jepson, N., and Beier, S., “Advancements in coronary bifurcation stenting techniques: Insights from computational and bench testing studies,"International Journal for Numerical Methods in Biomedical Engineering , In Press, Accepted 01/2025.
    2. Kapoor, A., Ray, T., Jepson, N., and Beier, S., “Comprehensive geometric parameterization and computationally efficient 3D shape matching optimization of realistic stents,” ASME Journal of Mechanical Design , In Press, Accepted 10/2024.
    3. Kenny, A., Ray, T., and Singh, H.K., “A framework for design optimization across multiple concepts,” Nature Scientific Reports, vol. 14, no. 1, pp. 7858, 2024.
    4. Mortazavi, A., Ghasri, M. and Ray, T., “A linearly decreasing deterministic annealing algorithm for the multi-vehicle dial a ride problem,” PLoS One, vol. 19, no. 2, 2024.
    5. Saha,R.N., Singh, H.K., and Ray, T., “A benchmark test suite for evolutionary multi-objective multi-concept optimization,” Swarm and Evolutionary Computation, vol. 84, pp. 101429, 2024.
    6. Mortazavi, A., Ghasri, M. and Ray, T., “Integrated-demand responsive transport in low-demand areas: A case study of Canberra, Australia,” Transportation Research Part D, vol. 127, pp.104036, 2024.
    7. Kapoor, A.,Jepson, N., Bressloff, N.W., Low, P.H., Ray, T., and Beier, S., “The road to the ideal stent: A review of stent design optimisation methods, findings, and opportunities,” Materials & Design, vol. 237, pp. 112556, 2024.
    8. Rana, M,J., Zaman, M.F., Ray, T., and Sarker, R., “EV hosting capacity enhancement in a community microgrid through dynamic price optimization based demand response,” IEEE Transactions on Cybernetics, issue 12, pp. 7431-7442, 2023.
    9. Rahi, K.H., Singh, H.K. and Ray, T., “A steady-state algorithm for solving expensive multi-objective optimization problems with non-parallelizable evaluations,” IEEE Transactions on Evolutionary Computation, vol. 27, no. 5, pp. 1544-1558, 2023.
    10. Kenny, A., Ray, T., and Singh, H.K., “An iterative two-stage multi-fidelity optimization algorithm for computationally expensive problems,” IEEE Transactions on Evolutionary Computation, vol. 27, no. 3, pp. 520-534, 2023.
    11. Singh, H.K., Ray, T., Rana, M.J., Limmer, S., Rodemann, T., and Olhofer, M., “Investigating the use of linear programming and evolutionary algorithms for multi-objective electric vehicle charging problem,” IEEE Access, vol. 10, pp. 115322-115337, 2022.
    12. Mamun, M., Singh, H.K. and Ray, T., "An approach for computationally expensive multi-objective optimization problems with independently evaluable objectives,"Swarm and Evolutionary Computation, vol. 75, pp. 101146, 2022.
    13. Mohamed, T., and Ray, T., “Approximation schemes for stochastic compliance-based topology optimization with many loading scenarios,” Structural and Multidisciplinary Optimization, vol. 65, 138, 2022.
    14. Mamun, M., Singh, H.K. and Ray, T., “A multifidelity approach for bilevel optimization with limited computing budget,” IEEE Transactions on Evolutionary Computation, vol. 26, issue 2, pp. 392-399, 2022.
    15. Ray, T., Singh, H.K., Rahi, K.H., Rodemann, T., and Olhofer, M., “Towards identification of solutions of interest for multiobjective problems considering both objective and variable space information,” Applied Soft Computing, vol. 119, pp. 108505, 2022.
    16. Wang, B., Singh, H.K. and Ray, T., “Adjusting normalization bounds to improve hypervolume based search for expensive multi-objective optimization,”Complex and Intelligent Systems, pp. 1-17, 2021.
    17. Rana, M,J., Rahi, K.H., Ray, T., and Sarker, R., “An efficient optimization approach for flexibility provisioning in community microgrids with an incentive-based demand response scheme,” Sustainable Cities and Society, vol. 74, pp. 103218, 2021.
    18. Mohamed, T., and Ray, T., “Robust and stochastic compliance-based topology optimization with finitely many loading scenarios ,” Structural and Multidisciplinary Optimization, vol. 64, pp. 3439–3452, 2021.
    19. Rahi, K.H., Singh, H.K. and Ray, T., “Partial evaluation strategies for expensive evolutionary constrained optimization,” IEEE Transactions on Evolutionary Computation, Vol. 25, Issue 6, pp. 1103-1117, 2021.
    20. Gharleghi, R., Wright, H., Luvio, V., Jepson, N., Luo, Z., Senthurnathan, A., Babaei, B., Prusty, G., Ray, T. and Beier S., “A multi-objective optimization of stent geometries,” Journal of Biomechanics, vol. 125, 10575, 2021.
    21. Rahi, K.H., Singh, H.K. and Ray, T., “Feasibility-ratio based sequencing for computationally efficient constrained optimization,” Swarm and Evolutionary Computation , vol. 62, April 2021.
    22. Rana, M,J., Zaman, M.F., Ray, T., and Sarker, R., “Real-time scheduling of community microgrid,” Journal of Cleaner Production, vol. 286, March 2021.
    23. Rahi, K.H., Singh, H.K. and Ray, T., “Evolutionary algorithm embedded with bump-hunting for constrained design optimization,” ASME Journal of Mechanical Design , pp. 1-28, 2020.
    24. Rana, M,J., Zaman, M.F., Ray, T., and Sarker, R.,“Heuristic enhanced evolutionary algorithm for community microgrid scheduling,” IEEE Access , vol. 8, pp. 76500-76515, 2020.
    25. Mohamed, T., and Ray, T., “Adaptive continuation solid isotropic material with penalization for volume constrained compliance minimization,” Computer Methods in Applied Mechanics and Engineering, vol. 363, 2020.
    26. Habib, A., Singh, H., Chugh, T., Ray, T., and Miettinen, K., “A multiple surrogate assisted decomposition based hybrid evolutionary algorithm for expensive multi/many-objective optimization,” IEEE Transactions on Evolutionary Computation, vol. 23, issue 6, pp. 1000-1014, 2019.
    27. Chand, S., Singh, H., and Ray, T., “Evolving rollout-justification based heuristics for resource constrained project scheduling problems,” Swarm and Evolutionary Computation, vol. 50, pp. 100556, 2019.
    28. Singh, H., Bhattacharjee, K.S.,and Ray, T., “Distance based subset selection for benchmarking in evolutionary multi/many-objective optimization,” IEEE Transactions on Evolutionary Computation, vol 23, issue 5, pp. 904-912, 2019.
    29. Habib, A., Singh, H., and Ray, T., “A multiple surrogate assisted multi/many-objective multi-fidelity evolutionary algorithm,” Information Sciences, vol 502, pp. 537-557, 2019.
    30. Singh, H., Islam, M.M., Ryan, M., and Ray, T., “Nested evolutionary algorithms for computationally expensive bilevel optimization problems: Variants and their systematic analysis,” Swarm and Evolutionary Computation, vol. 48, pp. 329-344, 2019.
    31. Liu, C., Zhao, Elsayed, S., Ray, T., and Sarker, R. “Adaptive sorting-based evolutionary algorithm for many-objective optimization,” IEEE Transactions on Evolutionary Computation, vol. 23, issue 2, pp. 247-257, 2019.
    32. Chand, S., Singh, H., and Ray, T., “Evolving heuristics for the resource constrained project scheduling problem with dynamic resource disruptions,” Swarm and Evolutionary Computation, vol. 44, pp. 897-912, 2019.
    33. Liu, Z., Bhattacharjee, K.S., Tian, F.B., Young, J., Ray, T. and Lai, J.C.S., “Kinematic optimization of a flapping foil power generator using a multi-fidelity evolutionary algorithm,” Renewable Energy, vol. 132, pp. 543-557, 2019.
    34. Huynh, Q.N., Chand, S., Singh, H., and Ray, T., “Genetic programming with mixed integer linear programming based library search,” IEEE Transactions on Evolutionary Computation, vol. 22, issue 5, pp. 733-747, 2018.
    35. Elsayed, S., Sarker, R., Coello Coello, C.,and Ray, T., "Adaptation of operators and continuous control parameters in differential evolution for constrained optimization," Soft Computing, vol. 22, issue 19, pp. 6595–6616, 2018.
    36. Asafuddoula, M., Singh, H. and Ray, T. , “An enhanced decomposition based evolutionary algorithm with adaptive reference vectors,” IEEE Transactions on Cybernetics, vol. 48, issue 8, pp. 2321-2334, 2018.
    37. Bhattacharjee, K.S., Singh, H. and Ray, T., “Multiple surrogate assisted many-objective optimization for engineering design,” ASME Journal of Mechanical Design, 40(5), 2018.
    38. Habib, A., Singh, H. and Ray, T., “A multiple surrogate assisted evolutionary algorithm for optimization involving iterative solvers,” Engineering Optimization, vol. 50(9), pp. 1625–1644, 2018.
    39. Chand, S., Huynh, Q.N., Singh, H., Ray, T., and Wagner, M., “On the use of genetic programming to evolve priority rules for resource constrained project scheduling problems,” Information Sciences, vol. 432, pp.146–163, 2018.
    40. Zaman, M.F., Elsayed, S., Sarker, R., and Ray, T., “Evolutionary algorithms for finding Nash equilibria in electricity markets,” IEEE Transactions on Evolutionary Computation, vol. 22, issue 4, pp. 536-549, 2018.
    41. Islam, M.M., Singh, H., Ray, T., Sinha, A., “An enhanced memetic algorithm for single objective bilevel optimization problems,” Evolutionary Computation, vol. 25, Issue 4, pp. 607-642, 2017.
    42. Li, C, Anavatti, S., and Ray, T., “A path-based solution algorithm for dynamic traffic assignment,” Networks & Spatial Economics, vol. 17, Issue 3, pp. 841–860, 2017.
    43. Alam,K., Ray, T., and Anavatti, S., “Design optimization of an unmanned underwater vehicle using low and high fidelity models,” IEEE Transactions on Systems, Man and Cybernetics: Systems, vol. 47, no. 11, pp. 2794-2808, 2017.
    44. Elsayed, S., Sarker, R., Ray, T. and Coello Coello, C., “Consolidated optimization algorithm for resource-constrained project scheduling problems,” Information Sciences, vol. 418-419, pp. 346-362, 2017.
    45. Zhang,Z., Zhan,C., Shankar,K., Morozov,E., Singh,H., and Ray, T., “Sensitivity analysis of inverse algorithms for damage detection in composites,” Composite Structures, vol. 176, pp. 844-859, 2017.
    46. Bhattacharjee, K.S., Singh, H., Ryan, M., and Ray, T., “Bridging the gap: Many-objective optimization and informed decision-making,” IEEE Transactions on Evolutionary Computation, vol. 21, issue 5, pp. 813-820, 2017.
    47. Bhattacharjee, K.S., Singh, H. and Ray, T., “A novel decomposition based evolutionary algorithm for engineering design optimization,” ASME Journal of Mechanical Design, vol. 139(4), 2017.
    48. Zaman, M.F., Elsayed, S., Ray, T., and Sarker, R., “Co-evolutionary approach for strategic bidding in competitive electricity markets," Applied Soft Computing, vol. 51, pp. 1-22, 2017.
    49. Islam, M.M., Singh, H., Ray, T., “A surrogate assisted approach for single-objective bilevel optimization,” IEEE Transactions on Evolutionary Computation, vol. 21, issue 5, pp. 681-696, 2017.
    50. Bhattacharjee, K.S., Singh, H. and Ray, T., “An approach to generate comprehensive piecewise linear interpolation of Pareto outcomes to aid decision making,” Journal of Global Optimization, vol. 68, issue 1, pp. 71–93, 2017.
    51. Branke, J., Asafuddoula, M., Bhattacharjee, K.S., Ray, T.,"Efficient Use of Partially Converged Simulations in Evolutionary Optimization," IEEE Transactions on Evolutionary Computation, vol. 21, issue 1, pp. 52-64, 2017.
    52. Li, C, Anavatti, S., and Ray, T., “A path-based flow formulation for the traffic assignment problem,” Transportation Planning and Technology, vol. 39, no. 6, pp. 597-611, 2016.
    53. Hassanein, O.I., Anavatti, S., Shim, H., and Ray, T., “Model-based adaptive control system for autonomous underwater vehicles,” Ocean Engineering, vol. 127, pp. 58-69, 2016.
    54. Bhattacharjee, K.S., Singh, H. and Ray, T., “Multi-objective optimization with multiple spatially distributed surrogates,” ASME Journal of Mechanical Design, vol. 138, issue 9, 2016.SAMO Code
    55. Zaman, M.F., Elsayed, S., Ray, T., and Sarker, R., “Evolutionary algorithms for power generation planning with uncertain renewable energy," Energy, The international journal, vol. 112, pp. 408-419, 2016.
    56. Singh, H., Bhattacharjee, K.S. and Ray, T., “A projection based approach for constructing piecewise linear Pareto front approximations,” ASME Journal of Mechanical Design, vol. 138, issue 9, 2016.
    57. Zaman, M.F., Elsayed, S., Ray, T., and Sarker, R., “Configuring two-algorithm-based evolutionary approach for solving dynamic economic dispatch problems," Engineering Applications of Artificial Intelligence, vol. 53, no. 2, pp. 105-125, 2016.
    58. Zaman, M.F., Elsayed, S., Ray, T., and Sarker, R., “Evolutionary algorithms for dynamic economic dispatch problems," IEEE Transaction on Power Systems, vol. 31, no. 2, pp. 1486-1495, 2016.
    59. Asafuddoula, M., Ray, T. and Sarker, R., “Differential evolution algorithm with constraint sequencing: An efficient approach for problems with inequality constraints ,” Applied Soft Computing, vol. 36, pp. 101-113, 2015.
    60. Asafuddoula, M., Singh, H. and Ray, T., “Six sigma robust design optimization using a many-objective decomposition based evolutionary algorithm,” IEEE Transactions on Evolutionary Computation, vol. 19, issue 4, pp. 490-507, 2015.
    61. Ray, T., Asafuddoula, M., Singh, H. and Alam, K., “An approach to identify six sigma robust solutions of multi/many-objective engineering design optimization problems,” ASME Journal of Mechanical Design, vol. 137, issue 5, pp. 051404-051404, 2015.
    62. Asafuddoula, M., Ray, T. and Sarker, R., “A decomposition based evolutionary algorithm for many objective optimization,” IEEE Transactions on Evolutionary Computation, vol. 19, issue 3, pp. 445-460, 2015. Reference Sets DBEA:MATLAB Source Code: Version date 07/08/2014.
    63. Asafuddoula, M., Ray, T. and Sarker, R., “Improved self-adaptive constraint sequencing approach for constrained optimization problems,” Applied Mathematics and Computation, vol. 253, pp. 23-29, 2015.
    64. Wang, C., Shankar,K., Ashraf, M., Morozov, E., and Ray, T., “Surrogate assisted optimisation design of composite riser,” Proceedings of the Institution of Mechanical Engineers, Part L: Journal of Materials: Design and Applications, pp. 14644207-14539304, 2014.
    65. Alam,K. Ray, T., and Anavatti, S., “A brief taxonomy of autonomous underwater vehicle design literature,” Ocean Engineering, vol. 88, pp. 627-630, 2014.
    66. Liu, M., Singh, H., and Ray, T., “Application specific instance generators for capacitated arc routing problem variants,” Transportation Research Part C, vol. 43, part. 3, pp. 249-266, 2014.Instance generator
    67. Ihesiulor, O.K, Shankar,K.,Zhang,Z., and Ray, T., “Validation of algorithms for delamination detection in composite structures using experimental data,” Journal of Composite Materials, vol. 48, no. 8, pp. 969-983, 2014.
    68. Alam, K. , Ray, T. and Anavatti, S., “Design and construction of an autonomous underwater vehicle,” Neurocomputing, vol.142, pp. 16-29, 2014.
    69. Sarker, R., Elsayed, S., and Ray, T., “Differential evolution with dynamic parameters selection for optimization problems,” IEEE Transactions on Evolutionary Computation, vol. 18, issue 5, pp. 689-707, 2014. Paper
    70. Asafuddoula, M.,Ray, T. and Sarker, R., “An adaptive hybrid differential evolution algorithm for single objective optimization,” Applied Mathematics and Computation, vol. 231, pp. 601–618, 2014.
    71. Li, C, Anavatti, S., and Ray, T., “Analytical hierarchy process using fuzzy inference technique for real time route guidance system,” IEEE Transactions on Intelligent Transportation Systems, vol. 15, no. 1, pp. 84-93, 2014.
    72. Ihesiulor, O.K, Shankar,K.,Zhang,Z., and Ray, T., “Delamination detection with error and noise polluted natural frequencies using computational intelligence concepts,” Composites Part B:Engineering, vol. 56, pp. 906–925, 2014.
    73. Khan, M. , Mohamad, A.F.A. and Ray, T., “An efficient memetic algorithm for 3D shape matching problems,” Engineering Optimization, vol. 46, no. 5, pp. 687-703, 2014. Benchmark Data
    74. Ihesiulor, O.K, Shankar,K.,Zhang,Z., and Ray, T., “Efficiencies of algorithms for vibration-based delamination detection: A comparative study,” Journal of Mechanics of Materials and Structures, vol. 8, no. 5-7, pp. 247-282, 2013.
    75. Hassanein, O.I, Anavatti, S., and Ray, T., “Black-box tool for nonlinear system identification based upon fuzzy system,” International Journal of Computational Intelligence and Applications, vol. 12, no. 2, pp. 1350009-1(21), 2013.
    76. Zhang, Z., Shankar,K., Ray, T., Morozov,E.V. and Tahtali, M., “Vibration based inverse algorithms for detection of delamination in composites,” Composite Structures, vol. 102, pp. 226-236,2013.
    77. Singh, H. , Ray, T. and Sarker, R.,“Optimum oil production planning using infeasibility driven evolutionary algorithm,” Evolutionary Computation, vol. 21, no. 1, pp. 65-82, 2013. IDEA Code , Oilwell Data
    78. Hassanein, O.I., Anavatti, S., and Ray, T., “Improved fuzzy neural modeling for underwater vehicles,” World Academy of Science, Engineering and Technology, vol. 71, pp. 1208-1214, 2012.
    79. Mohamad, A.F.A. , Ray, T. , and Smith, W., “Beyond hydrodynamic design optimization of planing craft,” Transactions of the Society of Naval Architects and Marine Engineering, pp. 473-485, 2012.
    80. Mohamad, A.F.A. , Ray, T. , and Smith, W., “A hydrodynamic preliminary design optimization framework for high speed planing craft,” Journal of Ship Research, vol. 56, no. 1, pp. 35–47, 2012.
    81. Khan, M. , Mohamad, A.F.A. , Isaacs, A. , and Ray, T., “A smart repair embedded memetic algorithm for 2D shape matching problems,” Engineering Optimization, vol. 44, no. 10, pp. 1229-1243, 2012. MATLAB Repair Function , Fish-1 , Fish-2, Turbine Blade
    82. Alam, K. , Ray, T. and Anavatti, S., “A new robust design optimization approach for unmanned underwater vehicle design,” Journal of Engineering for the Maritime Environment, vol. 226, pp. 235-249, 2012.
    83. Mohamad, A.F.A. , Ray, T. , and Smith, W., “Uncovering secrets behind low resistance planing craft hull forms through optimization,” Engineering Optimization, vol. 43, no. 11, November, pp. 1161–1173, 2011.
    84. Mohamad, A.F.A. , Ray, T. , and Smith, W., “Beyond hydrodynamic design optimization of planing craft,” Journal of Ship Production and Design, vol. 27, no. 1, pp. 1–13, 2011.
    85. Sul, J.H. , Prusty, G. , and Ray, T., “Prediction of low cycle fatigue life of short fibre composites at elevated temperatures using surrogate modelling,” Composites Part B:Engineering, vol. 42, issue 6, pp. 1453–1460, 2011.
    86. Saha, A. and Ray, T., “Practical robust design optimization using evolutionary algorithms,” ASME Journal of Mechanical Design, vol. 133, pp. 101012-1–101012-19, 2011.
    87. Singh, H. ,Isaacs, A. , and Ray, T., “A Pareto corner search evolutionary algorithm and dimensionality reduction in many-objective optimization problems,” IEEE Transactions on Evolutionary Computation, vol. 15, issue 4, pp. 539–556 2011.PCSEA Code
    88. Hassanein, O.I , Anavatti, S. and Ray, T., “Robust position control for two-link manipulator,” International Journal of Artificial Intelligence, Oct, vol. 7, pp. 347–359, 2011.
    89. Ogawa, H. , Boyce, R.R. , Isaacs, A. , and Ray, T., “Multi-objective design optimisation of inlet and combustor for axisymmetric scramjets,” Open Thermodynamics Journal, vol. 4, pp. 86–91, 2010.
    90. Samal, M. , Sreenatha, A. , Ray, T. , and Garrat, M., “A computationally efficient approach for NN based identification of a rotary wing UAV,” International Journal of Control, Automation and Systems, vol. 8, no. 4, pp. 727–734, 2010.
    91. Singh, H. and Ray, T. , “C-PSA: Constrained Pareto simulated annealing for constrained multi-objective optimization,” Information Sciences, vol. 180, no. 13, pp. 2499–2513, 2010.
    92. Isaacs, A. , Ray, T., and Smith, W., “Multiobjective design optimization using multiple adaptive spatially distributed surrogates,” International Journal of Product Development, vol. 9, no. 1-3, pp. 188–217, 2009.
    93. Sarker, R. and Ray, T., “An improved evolutionary algorithm for solving multi-objective crop planning models,” Computers and Electronics in Agriculture, vol. 68, issue 2, pp. 191–199, 2009.
    94. Ramanath, D. , Ray, T., and Boyce, R.R. , “Evolutionary algorithm shape optimization of a hypersonic flight experiment nose cone,” Journal of Spacecraft and Rockets, vol. 45, no. 3, pp. 428–437, 2008.
    95. Ray, T. and Sarker, R., “EA for solving combined machine layout and job assignment problems,” Journal of Industrial and Management Optimization, vol. 4, pp. 631-646, Aug. 2008.
    96. Briggs, G.P. , Ray, T., and Milthorpe, J.F., “Optimal design of an Australian medium launch vehicle,” Innovations in Systems and Software Engineering, vol. 3, pp. 105–116, 2007.
    97. Ray, T. and Sarker, R., “Genetic algorithm for solving a gas lift optimization problem,” Journal of Petroleum Science and Engineering, vol. 59, pp. 84–96, 2007.
    98. Liew, K. M. , Ray, T. , and Tan, P.K., “Computational swarm strategies for single objective design optimization problems,” International Journal of Computational Methods in Engineering Science and Mechanics, vol. 8, no. 1, pp. 9–18, 2007.
    99. Ray, T. and Smith, W., “A surrogate assisted parallel multi-objective evolutionary algorithm for robust engineering design,” Engineering Optimization, vol. 38, no. 9, pp. 997–1011, 2006.
    100. Venkatarayalu, N. , Ray, T. and Gan, Y.B., ”Multilayer dielectric filter design using a multi-objective evolutionary algorithm,” IEEE Transactions On Antennas and Propagation, vol. 53, no. 11, pp. 3625–3632, 2005.
    101. Won, K.S. and Ray, T., ”A framework for design optimization using surrogates,” Engineering Optimization, vol. 37, no. 7,pp. 685–703, 2005.
    102. Liew, K.M. , Ray, T., Tan, H. and Tan, M.J., “Optimal process design of sheet metal forming for minimum spring-back via an integrated neural network evolutionary algorithm,” Structural and Multidisciplinary Optimization, vol. 26, no. 3-4, February, pp. 284–294, 2004.
    103. Liew, K.M. , Tan, P.K. and Ray, T., “Leader identification and leader selection: Its effect on a swarms performance for multi-objective design optimization problems,” Structural and Multidisciplinary Optimization, vol. 28, no. 2-3, pp. 156–169, 2004.
    104. Venkatarayalu, N. and Ray, T., “Optimum design of Yagi-Uda antennas using computational intelligence,” IEEE Transactions On Antennas and Propagation, vol. 52, no. 7, pp. 1811–1818, 2004.
    105. Liew, K.M. , He, X.Q and Ray, T., “On the use of computational intelligence in optimal shape control of functionally graded smart plates,” Computer Methods in Applied Mechanics and Engineering, vol. 193, issues 42-44, pp. 4475–4492, 2004.
    106. Ray, T. and Tsai H. M., “A swarm algorithm for single and multi-objective airfoil design optimization,” AIAA Journal, vol. 42, no. 2, pp. 366–373, 2004.
    107. Ray, T., “Golinski's speed reducer problem revisited,” AIAA Journal, vol. 41, no. 3, pp. 556–558, 2003.
    108. Ray, T ,and Liew, K.M., “Society and Civilization: An optimization algorithm based on the simulation of social behaviour,” IEEE Transactions On Evolutionary Computation, vol. 7, issue. 4, pp. 3 86–396, 2003.
    109. Akhtar, S. , Kang, T. and Ray, T., “A socio-behavioural simulation model for engineering optimization,” Engineering Optimization, vol. 34, no. 4, pp. 341–354, 2002.
    110. Liew, K.M. , Ray, T. , Tan, H. and Tan, M.J., “Evolutionary optimization and use of neural network for optimum stamping process design for minimum spring-back,” Journal of Computing and Information Science in Engineering, vol. 2, issue 1, pp. 38–44, 2002.
    111. Kang, T. , Guang, Y.C. and Ray, T., “Design synthesis of path generating compliant mechanisms by evolutionary optimization of topology and shape,” ASME Journal of Mechanical Design,vol. 124, pp. 492–500, 2002.
    112. Ray, T., and Liew, K.M., “A swarm metaphor for multi-objective design optimization,” Engineering Optimization, vol. 34, no. 2, pp. 141–153, 2002.
    113. Ray, T.,Liew, K.M. and Saini, P., “An intelligent information sharing strategy within a swarm for unconstrained and constrained optimization problems,” Soft Computing:A Fusion of Foundations, Methodologies and Applications, vol. 6, pp. 38–44, 2002.
    114. Ray, T.,and Saini, P., “Engineering design optimization using a swarm with an intelligent information sharing among individuals,” Engineering Optimization, vol. 33, no. 6,pp. 73 5–748, 2001.
    115. Ray, T., and Kang, T., “An evolutionary algorithm with a multilevel pairing strategy for single and multi-objective optimization,” Foundations of Computing and Decision Sciences, vol. 26, pp. 75–98, 2001.
    116. Ray, T., Kang, T. and Seow, K.C., “Multi-objective design optimization by an evolutionary algorithm,” Engineering Optimization, vol. 33, no. 4, pp. 399–424, 2001.
    117. Ray, T., “Intelligent concept exploration using artificial neural network model,” Transactions of Society of Naval Architects and Marine Engineers, 1998.
    118. Ray, T., Gokarn, R.P. and Sha, O.P., “Neural network applications in naval architecture and marine engineering,” Artificial Intelligence in Engineering, vol. 1, pp. 213–226, 1996.
    119. Ray, T., Gokarn, R.P. and Sha, O.P., “A global optimization model for ship design,” Computers in Industry, vol. 26, pp. 175–192, 1995.
    120. Ray, T., Gokarn, R.P. and Sha, O.P., “A partial discrete optimization model for preliminary ship design,” Journal of Institution of Naval Architects, September, 1994.
    121. Ray, T., and Sha, O.P., “Multicriteria optimization model for a containership design,” Marine Technology, vol. 31, no. 4, pp. 258–268, 1994.
    Books
    1. Prusty, B. G., Sul, J., and Ray, T.,, "Fatigue behaviour of short fibre composites," Nova Science publishers, New York, USA, 2011.
    Magazines
    1. Sarker, R., and Ray, T.,, “Agent-based evolutionary algorithms: emerging paradigm or buzzwords?”, OR/MS Today, vol. 38, no. 5, October, 2011.
    2. Ray, T., and Sarker, R., “Evolutionary algorithms deliver promising results to gas lift optimization problems,” World Oil, vol. 229, no. 4, April, pp. 141–142, 2008.
    Book Chapters
    1. Bhattacharjee, K.S., Singh, H. and Ray, T., “Many-Objective optimization with limited computing budget,” in High-Performance Simulation Based Optimization, (Bartz-Beielsten, T., Filipic, B., Korosec, P., Talbi, E.G. eds.), Studies in Computational Intelligence, Springer, Studies in Computational Intelligence, pp. 17--46, Springer Berlin Heidelberg, 2020.
    2. Hassanein, O.I., Anavatti, S., Pratama, M., and Ray, T., “Autonomous underwater vehicles,” in Intelligent Marine Vehicles: Theory and Applications, (Joo, E.M., Wang, N., Zhichao, L., Pratama, M. and Sharma, S., eds.), Nova Science Publishers, 2018.
    3. Bhattacharjee, K.S., Isaacs, A., and Ray, T., “Multiobjective optimization using an evolutionary algorithm embedded with multiple spatially distributed surrogates,” Multi-objective optimization: Techniques and Application in Chemical Engineering, pp. 135-155, 2017. SAMO Code
    4. Hassanein, O.I., Anavatti, S., and Ray, T., “On-line adaptive fuzzy modeling and control for autonomous underwater vehicle,” in Recent Advances in Robotics and Automation, (Sen Gupta, G.,Bailey, D., Demidenko, S. and Carnegie, D. eds.), Studies in Computational Intelligence, vol. 480, pp. 57-70, Springer, 2013.
    5. Ray, T. , and Sarker, R., “Memetic algorithms in constrained optimization,” in Handbook of Memetic Algorithms, (Neri, F. , Moscato, P. and Cotta, C. eds.), Studies in Computational Intelligence, vol. 379, pp. 135–151, Springer, 2012.
    6. Sarker, R. and Ray, T., “Agent based evolutionary search: An introduction,” in Agent-Based Evolutionary Search (Hiot, L.M. , Ong, Y.S. , Sarker, R. and Ray, T. eds.), Adaptation, Learning, and Optimization, vol. 5, pp. 1–12, Springer, 2010.
    7. Singh, H. and Ray, T., “Divide and conquer in coevolution: A difficult balancing act,” in Agent-Based Evolutionary Search ,(Lim, M.H. , Ong, Y.S. , Sarker, R. and Ray, T. eds.), Adaptation, Learning, and Optimization, vol. 5, pp. 117–138, Springer, 2010. CCEA Code.
    8. Ray, T., Isaacs, A. , and Smith, W., “Multi-objective optimization using surrogate assisted evolutionary al-gorithm,” in Multi-objective Optimization: Techniques and Applications in Chemical Engineering (Rangaiah, G.P. ed.), pp. 131–151, World Scientific, 2009.Book Code
    9. Ray, T., Singh, H. , Isaacs, A. , and Smith, W., “Infeasibility driven evolutionary algorithm for constrained optimization,” in Constraint Handling in Evolutionary Optimization (Mezura-Montes, E. ed.), Studies in Computational Intelligence, vol. 198, pp. 147–167, Springer, 2009.IDEA Code
    10. Isaacs, A. , Ray, T., and Smith, W., “Set representation and multi-parent learning within an evolutionary algorithm for optimal design of trusses,” in Linkage in Evolutionary Computation (Chen, Y.P. and Lim, M.H. eds.), vol. 157, Studies in Computational Intelligence, vol. 157, pp. 419–439, Springer, 2008.
    11. Ray, T., Isaacs, A. , and Smith, W., “A memetic algorithm for dynamic multi-objective optimization,” in Multi-objective Memetic Algorithms (Goh, C.K. , Tan, C.K. and Ong, Y.S. eds.), Studies in Computational Intelligence, vol. 171, pp. 353–367, Springer, 2008.
    12. Ray, T. and Sarker, R., “Optimum oil production planning using an evolutionary approach,” in Evolutionary Scheduling, vol. 49, (Dahal, K. , Tan, K.C. , and Cowling, P.I. , eds.), Studies in Computational Intelligence, vol. 49, pp. 273–292, Springer, 2007.
    13. Ray, T., “A neural network assisted optimization framework and its use for optimum process parameter identification in sheet metal forming,” Artificial Neural Networks in Finance and Manufacturing, (Kamruzzaman, J. , Begg, R. and Sarker, R. , eds.), pp. 221–235, Idea Group, 2006.
    14. Venkatarayalu, N. and Ray, T., “Application of multi-objective optimization in electromagnetic design,” Real World Multi-objective Systems Engineering: Methodology and Applications (Nedjah, N. ed.), pp. 77–100, Nova Science, 2005.
    15. Ray, T., “Applications of multi-objective evolutionary algorithms in engineering design,” Applications of Multiobjective Evolutionary Algorithms (Carlos, C.A.C. and Lamont, G.B. , eds.), pp. 29–52, World Scientific, 2004.
    Lecture Notes
    1. Wang, B., Singh, H., and Ray, T., “Selective evaluations for expediting multi-objective bilevel optimization,” in Proceedings of EMO 2025: Evolutionary Multi-Criterion Optimization, (Canberra, Australia), Lecture Notes in Computer Science, Springer, 2025. In Press, Accepted 11/2024.
    2. Kenny, A., Ray, T., and Singh, H., “An extension of the welded beam problem that includes multiple interacting design concepts,” in Proceedings of EMO 2025: Evolutionary Multi-Criterion Optimization, (Canberra, Australia), Lecture Notes in Computer Science, Springer, 2025. In Press, Accepted 11/2024.
    3. Kenny, A., Ray, T., Limmer,S., Singh, H., Rodemann, T and Olhofer, M., “A hierarchical dissimilarity metric for automated machine learning pipelines, and visualizing search behaviour,” in Proceedings of EvoStar 2024, (Aberystwyth, UK), pp. 115-129,Springer Nature Switzerland, 2024.
    4. Kenny, A., Ray, T., Singh, H., and Li, X., “A test suite for multi-objective multi-fidelity optimization,” in Proceedings of EMO 2023: Evolutionary Multi-Criterion Optimization, (Leiden, Netherlands), vol. 13970 of Lecture Notes in Computer Science, pp. 361-373, Springer, 2023.
    5. Chen, Y., Singh, H., Zhou, A., and Ray, T., “A fast converging evolutionary algorithm for constrained multiobjective portfolio optimization,” in Proceedings of EMO 2021: Evolutionary Multi-Criterion Optimization, (Shenzhen, China), vol. 12654 of Lecture Notes in Computer Science, pp. 283-295, Springer, 2021.
    6. Wang, B., Singh, H., and Ray, T., “Investigating normalization bounds for hypervolume-based infill criterion for expensive multiobjective optimization,” in Proceedings of EMO 2021: Evolutionary Multi-Criterion Optimization, (Shenzhen, China), vol. 12654 of Lecture Notes in Computer Science, pp. 519-530, Springer, 2021.
    7. Gharleghi R., Wright H., Khullar S., Liu J., Ray, T. and Beier S., “Advanced multiobjective design analysis to identify ideal stent design,” in Proceedings of Joint MICCAI-Workshops on Computing and Visualization for Intravascular Imaging and Computer Assisted Stenting, (Shenzhen, China), vol. 11794 of Lecture Notes in Computer Science, pp. 193-200, Springer, 2019.
    8. Bhattacharjee, K.S., Singh, H., and Ray, T. , “Optimum wind farm layouts: A many-objective perspective and case study,” in Proceedings of EMO 2019: Evolutionary Multi-Criterion Optimization, (Michigan, USA), vol. 11411 of Lecture Notes in Computer Science, pp. 707-718, Springer, 2019.
    9. Ray, T. , Habib, A., Singh, H., and Ryan, M., “Uncovering performance envelopes through optimum design of tests,” in Proceedings of AI 2018: Advances in Artificial Intelligence,(Wellington, New Zealand), vol. 11320 of Lecture Notes in Computer Science, pp. 445-457, Springer, 2018.
    10. Singh, H., Bhattacharjee, K.S., Ray, T. , and Mostaghim, S.,“Investigation of a simple distance based ranking metric for decomposition-based multi/many-objective evolutionary algorithms,” in Proceedings of AI 2018: Advances in Artificial Intelligence,(Wellington, New Zealand), vol.~11320 of Lecture Notes in Computer Science, pp. 384-396, Springer, 2018.
    11. Bhattacharjee, K.S., Singh, H., and Ray, T. , “Enhanced Pareto Interpolation Method to Aid Decision Making for Discontinuous Pareto Optimal Fronts,” in Proceedings of AI 2017: Advances in Artificial Intelligence, vol. 10400 of Lecture Notes in Computer Science, (Melbourne, Australia), pp. 93-105, Springer, 2017.
    12. Islam, M.M. Singh, H., and Ray, T. , “Use of a non-nested formulation to improve search for bilevel optimization,” in Proceedings of AI 2017: Advances in Artificial Intelligence, (Melbourne, Australia), vol. 10400 of Lecture Notes in Computer Science, pp. 106-118, Springer, 2017.
    13. Habib, A., Singh, H. and Ray, T., A Batch Infill Strategy for Computationally Expensive Optimization Problems,” in Proceedings of ACALCI 2017: Artificial Life and Computational Intelligence, (Geelong, Australia), vol. 10142 of Lecture Notes in Computer Science, pp. 74-85, Springer, 2017
    14. Lu, K, Branke, J., and Ray, T. , "Improving efficiency of bi-level worst case optimization,” in Proceedings of PPSN 2016: Parallel Problem Solving from Nature, (Edinburgh, UK), vol. 9921 of Lecture Notes in Computer Science, pp. 410-420, Springer, 2016.
    15. Singh, H., Alam, K., and Ray, T. , "Use of infeasible solutions during constrained evolutionary search: A short survey,” in Proceedings of Artificial Life and Computational Intelligence, (Canberra, Australia), vol. 9592 of Lecture Notes in Computer Science, pp. 193-205, Springer, 2016.
    16. Zaman, M.F., Elsayed, S., Sarker, R., Ray, T., "A double action genetic algorithm for scheduling the wind-thermal generators,” in Proceedings of Artificial Life and Computational Intelligence, (Canberra, Australia), vol. 9592 of Lecture Notes in Computer Science, pp. 258-269, Springer, 2016.
    17. Bhattacharjee, K.S., Singh, H., and Ray, T. , "A study on performance metrics to identify solutions of interest from a trade-off set,” in Proceedings of Artificial Life and Computational Intelligence, (Canberra, Australia), vol. 9592 of Lecture Notes in Computer Science, pp. 66-77, Springer, 2016.
    18. Ismail, M.A., Elsayed, S., Ray, T. , and Sarker, R., , "A differential evolution algorithm for solving resource constrained project scheduling problems,” in Proceedings of Artificial Life and Computational Intelligence, (Canberra, Australia), vol. 9592 of Lecture Notes in Computer Science, pp. 209-220, Springer, 2016.
    19. Islam, M.M. Singh, H., and Ray, T. , "A nested differential evolution based algorithm for solving multi-objective bilevel optimization problems,” in Proceedings of Artificial Life and Computational Intelligence, (Canberra, Australia), vol. 9592 of Lecture Notes in Computer Science, pp. 101-112, Springer, 2016.
    20. Bhattacharjee, K.S., and Ray, T. , "Cost to evaluate versus Cost to learn ? Performance of selective evaluation strategies in multiobjective optimization,” in Proceedings of AI 2015: Advances in Artificial Intelligence, (Canberra, Australia), vol. 9457 of Lecture Notes in Computer Science, pp. 63-75, Springer, 2015.
    21. Bhattacharjee, K.S., and Ray, T. , "An evolutionary algorithm with classifier guided constraint evaluation strategy for computationally expensive optimization problems ,” in Proceedings of AI 2015: Advances in Artificial Intelligence, (Canberra, Australia), vol. 9457 of Lecture Notes in Computer Science, pp. 49-62, Springer, 2015. Supplementary
    22. Singh, H, Asafuddoula, M., Alam, K., and Ray, T., “Re-design for robustness: An approach based on many objective optimization,” in Proceedings of EMO 2015: Evolutionary Multi-Criterion Optimization, (Guimaraes, Portugal), vol. 9019 of Lecture Notes in Computer Science, pp. 343-357, Springer, 2015.
    23. Hassanein, O., Anavatti, S., and Ray, T., “Hybrid neuro-fuzzy network identification for autonomous underwater vehicle,” in Proceedings of SEMCCO 2013: Swarm, Evolutionary, and Memetic Computing, (Chennai, India), vol. 8298 of Lecture Notes in Computer Science, pp. 287-297, Springer, 2013.
    24. Kirsanov, A., Anavatti, S., and Ray, T., “Path planning with respect of flows and dynamic obstacles for the autonomous underwater vehicle,” in Proceedings of SEMCCO 2013: Swarm, Evolutionary, and Memetic Computing, (Chennai, India), vol. 8298 of Lecture Notes in Computer Science, pp.~476-486, Springer, 2013.
    25. Asafuddoula, M.,Ray, T. and Sarker, R., “A decomposition based evolutionary algorithm for many objective optimization with systematic sampling and adaptive epsilon control,” in Proceedings of EMO 2013: Evolutionary Multi-Criterion Optimization, (Sheffield, UK), vol. 7811 of Lecture Notes in Computer Science, pp. 312-427, Springer, 2013.
    26. Alam, K., Ray, T. and Anavatti, S., “An evolutionary approach for the design of autonomous underwater vehicles,” in Proceedings of AI 2012: Advances in Artificial Intelligence, (Sydney, Australia), vol. 7691 of Lecture Notes in Computer Science, pp. 279-290, Springer, 2012.
    27. Asafuddoula, M.,Ray, T. and Sarker, R., “A self-adaptive differential evolution algorithm with constraint sequencing,” in Proceedings of AI 2012: Advances in Artificial Intelligence, (Sydney, Australia), vol. 7691 of Lecture Notes in Computer Science, pp. 182-193, Springer, 2012.
    28. Khan, M., and Ray, T., “A memetic algorithm for efficient solution of 2D and 3D shape matching problems,” in Proceedings of AI 2012: Advances in Artificial Intelligence, (Sydney, Australia), vol. 7691 of Lecture Notes in Computer Science}, pp. 362-372, Springer, 2012.
    29. Liu, M., and Ray, T., “Efficient solution of capacitated arc routing problems with a limited computational budget,” in Proceedings of AI 2012: Advances in Artificial Intelligence, (Sydney, Australia), vol. 7691 of Lecture Notes in Computer Science, pp. 791-802, Springer, 2012.
    30. Singh, H, Ray, T. and Smith, W., “Performance of infeasibility empowered memetic algorithm(IEMA) on engineering design problems,” in Proceedings of AI 2010: Advances in Artificial Intelligence, (Adelaide, Australia), vol. 6464 of Lecture Notes in Computer Science, pp. 425-434, Springer, 2011. IEMA Code
    31. Isaacs, A., Ray, T. , and Smith, W., “An efficient hybrid algorithm for optimization of discrete structures,” in Proceedings of SEAL 2008: Simulated Evolution and Learning, (Melbourne, Australia), vol. 5361 of Lecture Notes in Computer Science, pp. 625-634, Springer, 2008.
    32. Singh, H., Isaacs, A., Ray, T. , and Smith, W., “A study on the performance of substitute distance based approaches for evolutionary many objective optimization,” in Proceedings of SEAL 2008: Simulated Evolution and Learning, (Melbourne, Australia), vol. 5361 of Lecture Notes in Computer Science, pp. 401-410, Springer, 2008.
    33. Singh, H., Isaacs, A., Ray, T., and Smith, W., “Infeasibility driven evolutionary algorithm (IDEA) for engineering design optimization,” in Proceedings of AI 2008: AI 2008: Advances in Artificial Intelligence, (Auckland, New Zealand), vol. 5360 of Lecture Notes in Computer Science, pp. 104-115, Springer, 2008.
    34. Isaacs, A., Ray, T., and Smith, W., “An evolutionary algorithm with spatially distributed surrogates for multi-objective optimization,” in Proceedings of ACAL 2007: Progress in Artificial Life, (Gold Coast, Australia), vol. 4828 of Lecture Notes in Computer Science, pp.~257-268, Springer, 2007.
    35. Puttige, V., Sreenatha, A., and Ray, T., “Comparative analysis of multiple neural networks for online identification of a UAV,” in Proceedings of AI 2007: Advances in Artificial Intelligence, (Gold Coast, Australia), vol. 4830 of Lecture Notes in Computer Science, pp. 120-129, Springer, 2007.
    36. Ray, T., and Won, K.S., “An evolutionary algorithm for constrained biobjective constrained optimization using radial slots,” in Proceedings of International Conference on Knowledge-Based and Intelligent Information and Engineering Systems, (Melbourne, Australia), vol. 3684 of Lecture Notes in Computer Science, pp. 49-56, Springer, 2005.
    Conference Papers
    1. Kenny, A., Ray, T., Limmer, S., Singh, H.K., Rodemann, T., and Olhofer, M., , “Using Bayesian optimization to improve hyperparameter search in TPOT,” in Proceedings of the Genetic and Evolutionary Computation Conference,(Melbourne, Australia), pp. 340-348, 2024.
    2. Wang, B., Singh, H.K. and Ray, T., “Improving the performance of bilevel evolutionary algorithms using variable associations,” in Proceedings of IEEE Congress on Evolutionary Computation, (Yokohama, Japan), pp. 1-8, 2024.
    3. Saha, R.N., Singh, H.K. and Ray, T., “A brief review of multi-concept multi-objective optimization problems,” in Proceedings of IEEE Symposium Series on Computational Intelligence, (Mexico City, Mexico), pp. 1511-1517, 2023.
    4. Rahi, K.H., Singh, H.K. and Ray, T., “A generalized surrogate-assisted evolutionary algorithm for expensive multi-objective problems,” in Proceedings of IEEE Congress on Evolutionary Computation, (Chicago, USA), pp. 1-8, 2023.
    5. Wang, B., Singh, H.K. and Ray, T., “An evaluation of simple solution transfer strategies for bilevel multiobjective optimization,” in Proceedings of IEEE Congress on Evolutionary Computation, (Chicago, USA), pp. 1-8, 2023, 2023.
    6. Kenny, A., Ray, T., Limmer, S., Singh, H.K., Rodemann, T., and Olhofer, M., , “Hybridizing TPOT with Bayesian optimization,” in Proceedings of the Genetic and Evolutionary Computation Conference,(Lisbon Portugal), pp. 502–510, 2023.
    7. Rahi, K.H., Lette, M., Singh, H.K. and Ray, T., “Vertical-axis wind turbine design using surrogate-assisted optimization with physical experiments in-loop,” in Proceedings of the Genetic and Evolutionary Computation Conference, (Lisbon Portugal), pp. 1391–1399, 2023.
    8. Huynh, Q.N., Singh, H. and Ray, T., and Oyama, A., “Improved genetic programming for symbolic regression: Case studies on practical applications,” in Proceedings of 2022 IEEE Symposium Series On Computational Intelligence, (Singapore), pp.1135-1142, 2022.
    9. Ray, T., Mamun, M., and Singh, H.K., “A simple evolutionary algorithm for multi-modal multi-objective optimization,” in Proceedings of IEEE Congress on Evolutionary Computation, (Padua, Italy), pp. 1-8, 2022.
    10. Wang, B., Singh, H.K. and Ray, T., “Investigating neighborhood solution transfer schemes for bilevel optimization,” in Proceedings of IEEE Congress on Evolutionary Computation, (Padua, Italy), pp. 1-8, 2022.
    11. Huynh, Q.N., Singh, H. and Ray, T., “Discovery of implicit relationships from data using linear programming and mixed integer linear programming,” in Proceedings of the Genetic and Evolutionary Computation Conference, Companion, (Boston, USA), pp. 558-561, 2022.
    12. Wang, B., Singh, H.K. and Ray, T., “Comparing expected improvement and Kriging believer for expensive bilevel optimization,” in Proceedings of IEEE Congress on Evolutionary Computation, (Krakow, Poland), pp. 1635-1642, 2021.
    13. Parker, B., Singh, H.K. and Ray, T., “Multi-objective optimization across multiple concepts: A case study on lattice structure design,” in Proceedings of the Genetic and Evolutionary Computation Conference, (Lille, France), pp. 1035-1042, 2021.
    14. Wang, B., Singh, H.K. and Ray, T., “Bridging Kriging believer and expected improvement using bump hunting for expensive black-box optimization,” in Proceedings of the Genetic and Evolutionary Computation Companion, (Lille, France), pp. 211-212, 2021.
    15. Rana, M,J., Zaman, M.F., Ray, T., and Sarker, R., “Economic environmental scheduling of community microgrid using evolutionary algorithm,” in Proceedings of IEEE Symposium Series on Computational Intelligence,(Canberra, Australia). pp. 2770-2777, 2020.
    16. Rahi, K.H., Habib, A., Singh, H.K. and Ray, T., “Expediting the convergence of evolutionary algorithms by identifying promising regions of the search space,” in Proceedings of the Genetic and Evolutionary Computation Conference, Companion, (Cancun, Mexico), pp. 201-202, 2020.
    17. Habib, A., Singh, H.K., Rahi, K.H., and Ray, T., “Wind-turbine design optimization using a many-objective evolutionary algorithm,” in Proceedings of the Genetic and Evolutionary Computation Conferenence, Companion, (Cancun, Mexico), pp. 267-268, 2020.
    18. Ray, T., Singh, H.K., Habib, A., Rodemann, T., and Olhofer, M. “Online intensification of search around solutions of interest for multi/many-objective optimization,” in Proceedings of the IEEE Congress on Evolutionary Computation, (Glasgow, UK), pp. 1-8, 2020.
    19. Singh, H.K., Ray, T., Rodemann, T., and Olhofer, M. “Identifying solutions of interest for practical many-objective problems using recursive expected marginal utility,” in Proceedings of the Genetic and Evolutionary Computation Conference, Companion, (Prague, Czech Republic), pp. 1734-1741, 2019.
    20. Habib, A., Singh, H. and Ray, T., “A component-wise study of K-RVEA: Observations and potential future developments,” in Proceedings of the Genetic and Evolutionary Computation Conference, Companion, (Prague, Czech Republic), pp. 201-202, 2019.
    21. Huynh, Q.N., Singh, H. and Ray, T., “Investigating the use of linear programming to solve implicit symbolic regression problems,” in Proceedings of the Genetic and Evolutionary Computation Companion, (Prague, Czech Republic), pp. 344-345, 2019.
    22. Singh, H., Ray, T., Rodemann, T., and Olhofer, M. “Identifying solutions of interest for practical many-objective problems using recursive expected marginal utility,” in Proceedings of the Genetic and Evolutionary Computation Companion, (Prague, Czech Republic), pp. 1734-1741, 2019.
    23. Chand, S., Singh, H. and Ray, T., “Rollout based heuristics for the quantum circuit compilation problem,” in Proceedings of the IEEE Congress on Evolutionary Computation, (Welington, New Zealand), pp. 951-958, 2019.
    24. Rahi, K.H., Singh, H. and Ray, T., “Investigating the use of sequencing and infeasibility driven strategies for constrained optimization,” in Proceedings of the IEEE Congress on Evolutionary Computation, (Welington, New Zealand), pp. 1643-1650, 2019.
    25. Chand, S., Singh, H. and Ray, T., “Team selection using multi-/many-objective optimization with integer linear programming,” in Proceedings of the IEEE Congress on Evolutionary Computation, (Rio de Janeiro, Brazil), pp. 2523-2530, 2018.
    26. Islam, M.M., Singh, H. and Ray, T., “Efficient global optimization for solving computationally expensive bilevel optimization problems,” in Proceedings of the IEEE Congress on Evolutionary Computation, (Rio de Janeiro, Brazil), pp. 181-188, 2018.
    27. Chand, S., Singh, H. and Ray, T., “A Heuristic algorithm for solving resource constrained project scheduling problems,” in Proceedings of the IEEE Congress on Evolutionary Computation, (San Sebastián, Spain), pp. 225-232, 2017. Extended Paper
    28. Bhattacharjee, K.S., Singh, H., Ray, T. and Zhang, Q., “Decomposition based evolutionary algorithm with a dual set of reference vectors,” in Proceedings of the IEEE Congress on Evolutionary Computation, (San Sebastián, Spain), pp. 105-112, 2017.Performance on Normal Front Problems
    29. de Baar, J.H.S., Leylek, Z, Habib, A., Neely, A.J., and Ray, T., Multi-fidelity efficient global optimisation of the geometry of a transonic axial compressor,” in Proceedings of International Society of Air-breathing Engines Conference,(Manchester, UK). Accepted 10/2016
    30. Huynh, Q.N., Singh, H. and Ray, T., Improving symbolic regression through a semantics driven framework,” in Proceedings of IEEE Symposium Series on Computational Intelligence,(Athens, Greece). pp.1-8, 2016.
    31. Habib, A., Singh, H. and Ray, T., A study on the effectiveness of constraint handling schemes within efficient global optimization framework,” in Proceedings of IEEE Symposium Series on Computational Intelligence,(Athens, Greece). pp.1-8, 2016.
    32. Milowski, J., Bhattacharjee, K.S., Singh, H. and Ray, T., Electric vehicles for Australia: A cost-benefit analysis,” in Proceedings of 24th National Conference of the Australian Society for Operations Research, (Canberra, Australia). Data and Decision Sciences in Action, Lecture Notes in Management and Industrial Engineering, pp. 163-173, 2017.
    33. Li, C., Anavatti, S.G., Ray, T.,and Shim, H., A game-theoretic approach to the analysis of traffic assignment,” in Proceedings of the 20th Asia-Pacific Symposium on Intelligent and Evolutionary Systems, (Canberra, Australia), pp.17-30, 2016.
    34. Zaman, M.F., Elsayed,S.M., Ray, T.,and Sarker, R., “An evolutionary framework for the bi-objectives dynamic economic and environmental dispatch problems,” in Proceedings of the 20th Asia-Pacific Symposium on Intelligent and Evolutionary Systems, (Canberra, Australia), pp.495-508, 2016.
    35. Huynh, Q.N., Singh, H. and Ray, T., “A semantics based symbolic regression framework for mining explicit and implicit equations from data,” in Proceedings of the Genetic and Evolutionary Computation, (Denver, USA), pp. 103-104, 2016.
    36. Asafuddoula, M., Singh, H. and Ray, T., “A CUDA implementation of an improved decomposition based evolutionary algorithm for multi-Objective optimization,” in Proceedings of the Genetic and Evolutionary Computation, (Denver, USA),pp. 71-72, 2016.
    37. Zaman, M.F., Elsayed,S.M., Ray, T. and Sarker, R., “A Co-evolutionary approach for optimal bidding strategy of multiple electricity suppliers,” in Proceedings of the IEEE Congress on Evolutionary Computation, (Vancouver, Canada), pp. 3507-3514, 2016.
    38. Habib, A., Singh, H. and Ray, T., “A multi-objective formulation based batch infill strategy for efficient global optimization,” in Proceedings of the IEEE Congress on Evolutionary Computation, (Vancouver, Canada), pp. 4336-4343, 2016.
    39. Islam, M.M., Singh, H. and Ray, T., “A memetic algorithm for solving bilevel optimization problems with multiple followers,” in Proceedings of the IEEE Congress on Evolutionary Computation, (Vancouver, Canada), pp.~1901-1908, 2016.
    40. Huynh, Q.N., Singh, H. and Ray, T., “Optimum redesign of scale-free networks with robustness and cost considerations,” in Proceedings of the IEEE Congress on Evolutionary Computation, (Vancouver, Canada), pp.~529-536, 2016.
    41. Chand, S., Singh, H. and Ray, T., “Finding robust Solutions for resource constrained project scheduling problems involving uncertainties,” in Proceedings of the IEEE Congress on Evolutionary Computation, (Vancouver, Canada), pp. 225-232, 2016.
    42. Bhattacharjee, K.S., Singh, H., Ray, T. and Branke, J., “Multiple surrogate assisted multiobjective optimization using improved preselection,” in Proceedings of the IEEE Congress on Evolutionary Computation, (Vancouver, Canada), pp.~4328-4335, 2016. SAMO-IS Code
    43. Zaman, M.F., Elsayed, S., Sarker, R., Ray, T., “An evolutionary approach for scheduling solar-thermal power generation system,” in Proceedings of the International Conference on Computers & Industrial Engineering (CIE), (Metz, France), 2015.
    44. Asafuddoula, M.,Ray, T., and Singh, H., "Characterizing Pareto front approximations in many-objective optimization,” in Proceedings of the Genetic and Evolutionary Computation Conference, (Madrid, Spain), pp. 607-614, 2015.
    45. Asafuddoula, M., Ray, T., , Isaacs, A., and Singh, H., "Performance of a steady state quantum genetic algorithm for multi/many-objective engineering optimization problems,” in Proceedings of the IEEE Congress on Evolutionary Computation, (Sendai, Japan), pp. 893-899, 2015.
    46. Bhattacharjee, K.S., and Ray, T. , "Selective evaluation in multiobjective optimization: A less explored avenue,” in Proceedings of the IEEE Congress on Evolutionary Computation, (Sendai, Japan), pp. 1893-1900, 2015.
    47. Islam, M.. Singh, H., and Ray, T. , "A memetic algorithm for the solution of single objective bilevel optimization problems,” in Proceedings of the IEEE Congress on Evolutionary Computation, (Sendai, Japan), pp. 1643-1650, 2015..
    48. Wang, B., Singh, H., and Ray, T. , "A multi-objective genetic programming approach to uncover explicit and implicit equations from data,” in Proceedings of the IEEE Congress on Evolutionary Computation, (Sendai, Japan), pp. 1129-1136, 2015.
    49. Ismail, M.A., Elsayed, S., Ray, T. , and Sarker, R., "Memetic algorithm for solving resource constrained project scheduling problems,” in Proceedings of the IEEE Congress on Evolutionary Computation, (Sendai, Japan), pp. 2761-2767, 2015..
    50. Tajbaksh, S.E., Ray, T., , Reed. M., Liu, Y., "Joint power control and resource scheduling in wireless heterogeneous networks,” in Proceedings of the 22nd International Conference on Telecommunications, (Sydney, Australia), 2015.
    51. Bhattacharjee, K.S., and Ray, T., “A novel constraint handling strategy for expensive optimization problems,” in Advances in Structural and Multidisciplinary Optimization, Proceedings of the Eleventh World Congress of Structural and Multidisciplinary Optimization,(Sydney, Australia), pp. 89=94, 2015.
    52. Singh, H., and Ray, T., “Many-objective optimization in engineering design: Case studies using a decomposition based evolutionary algorithm,” in Advances in Structural and Multidisciplinary Optimization, Proceedings of the Eleventh World Congress of Structural and Multidisciplinary Optimization,(Sydney, Australia), pp. 106-111, 2015.
    53. Leylek, Z., Neely, A.J, and Ray, T., “Global surrogate modelling of gas turbine aerodynamic performance,” in Proceedings of the Ninteenth Australasian Fluid Mechanics Conference,(Melbourne, Australia), 2014.
    54. Zaman, M.F., Sarker, R., Ray, T., “Solving an economic and environmental dispatch problem using evolutionary algorithm,” in Proceedings of IEEE International Conference on Industrial Engineering and Engineering Management (IEEM)), (Kuala Lumpur, Malaysia), 2014.
    55. Liu, M., Singh, H., and Ray, T., “A benchmark generator for dynamic capacitated arc routing problems,” in Proceedings of the IEEE Congress on Evolutionary Computation, (Beijing, China), pp. 579-586, 2014.DCARP Benchmarks
    56. Liu, M., Singh, H., and Ray, T., “A memetic algorithm with a new split scheme for solving dynamic capacitated arc routing problems,” in Proceedings of the IEEE Congress on Evolutionary Computation, (Beijing, China), pp. 595-602, 2014.
    57. Alam, K., Ray, T., and Anavatti, S., “Practical application of an evolutionary algorithm for the design and construction of a six-inch submarine,” in Proceedings of the IEEE Congress on Evolutionary Computation, (Beijing, China), pp. 2825-2832, 2014.
    58. Elsayed, S., Ray, T., and Sarker, R., “A surrogate-assisted differential evolution algorithm with dynamic parameters selection for solving expensive optimization problems,” in Proceedings of the IEEE Congress on Evolutionary Computation, (Beijing, China), pp. 1062-1068, 2014.
    59. Singh, H., Isaacs, A., and Ray, T., “A hybrid surrogate based algorithm (HSBA) to solve computationally expensive optimization problems,” in Proceedings of the IEEE Congress on Evolutionary Computation, (Beijing, China), pp. 1069-1075, 2014.
    60. Singh, H., Asafuddoula, M., and Ray, T., “Solving problems with a mix of hard and soft constraints using modified infeasibility driven evolutionary algorithm (IDEA-M),” in Proceedings of the IEEE Congress on Evolutionary Computation, (Beijing, China), pp. 983-990, 2014.
    61. Asafuddoula, M., Ray, T., and Sarker, R., “An efficient constraint handling approach for optimization problems with limited feasibility and computationally expensive constraint evaluations,” in Proceeding of the 2013 Genetic and Evolutionary Computation Conference (GECCO 2013), (Amsterdam, The Netherlands), pp. 113-114, 2013.
    62. Kirsanov, A., Ray, T., Anavatti, S., “3D tools for the robust design optimization of an autonomous underwater vehicle,” in Proceedings of the IEEE International Symposium on Control, Automation, Industrial Informatics and Smart Grid, (Mysore, India), pp. 1730-1737, 2013.
    63. Li, C., Anavatti, S., and Ray, T., “Application of a non-cooperative game theory based traffic assignment,” in Proceedings of the IEEE International Symposium on Control, Automation, Industrial Informatics and Smart Grid, (Mysore, India), pp. 1124-1128,2013.
    64. Ray, T., Asafuddoula, M. and Isaacs, A., “A steady state decomposition based quantum genetic algorithm for many objective optimization,” in Proceedings of the IEEE Congress on Evolutionary Computation, (Cancun, Mexico), pp. 2817-2824,2013.
    65. Elsayed, S. ,Sarker, R. and Ray, T., “Differential evolution with automatic parameter configuration for solving the CEC2013 Competition on real-parameter optimization,” in Proceedings of the IEEE Congress on Evolutionary Computation, (Cancun, Mexico), pp. 1932-1937,2013.
    66. Asafuddoula, M. ,Ray, T. and Sarker, R., “Evaluate till you Violate: A differential evolution algorithm based on partial evaluation of the constraint Set,” in Proceedings IEEE Symposium Series on Computational Intelligence,(Singapore, Singapore), pp. 31-37,2013.
    67. Zhang,Z., Shankar, K. Ray, T. and Tahtali, M., “Delamination monitoring of composite plates using vibration-based surrogate assisted optimisation,” In Proceedings of 15th Australian International Aerospace Congress, (Melbourne, Australia), 2013.
    68. Asafuddoula, M., Ray, T., and Sarker, R., “A Differential evolution algorithm with constraint sequencing,” in Proceeding of the 2012 Third Global Congress on Intelligent Systems (GCIS), (Wuhan, China), pp. 68-71, 2012. saDE_CS Code
    69. Hassanein, O., Salman, S.A., Anavatti, S., and Ray, T., “ANFN controller based On differential evolution for autonomous underwater vehicles,” in Proceedings of the First International Conference on Innovative Engineering Systems (ICIES), (Alex, Egypt), pp. 184-189, 2012.
    70. Ihesiulor, O.K, Shankar,K., Zhang,Z., and Ray, T., “Delamination detection using methods of computational intelligence,” in Proceedings of Sixth Global Conference on Power Control and Optimization, (Las Vegas, USA), pp. 303-310, 2012.
    71. Ihesiulor, O.K, Shankar,K.,Zhang,Z., and Ray, T., “Effectiveness of artificial neural networks and surrogate-assisted optimization techniques in delamination detection for structural health monitoring,” in Procedings of The 23rd IASTED International Conference on Modelling and Simulation, (Banff, Canada), 2012.
    72. Alam, K. , Ray, T. and Anavatti, S., “A study on the drag estimation of an AUV based on numerical methods,” in Proceedings of the Advances in Control and Optimization of Dynamical Systems, (Bangalore, India,), pp. 1–6, 2012.
    73. Asafuddoula, M., Ray, T. , Sarker, R. and Alam, K., “An adaptive constraint handling approach embedded MOEA/D,” in Proceedings of the IEEE Congress on Evolutionary Computation, (Brisbane, Australia), pp. 2516-2513, 2012.
    74. Elsayed, S., Sarker, R., and Ray, T., “Parameters adaptation in differential evolution,” in Proceedings of the IEEE Congress on Evolutionary Computation, (Brisbane, Australia), pp. 2989-2996, 2012.
    75. Hassanein,O., Anavatti, S, Ray, T., “Improved fuzzy neural modeling based on differential evolution for underwater vehicles,” in Proceedings of the 14th International Conference on Artificial Intelligence, (Las Vegas, USA), pp. 1-7, 2012.
    76. Khan, M. and Ray, T., “Shape representation and a morphing scheme to support flapping wing research,” in Proceedings of the 1st International Conference on Pattern Recognition Methods and Applications, (Algarve, Portugal), pp. 494–499, 2012.
    77. Li, C., Anavatti, S., and Ray, T., “Adaptive route guidance system with real-time traffic information,” Proceedings of 15th International IEEE Conference on Intelligent Transportation Systems, (Anchorage, USA), pp. 367-372, 2012.
    78. Li, C., Anavatti, S., and Ray, T., “Implementing analytical hierarchy process using fuzzy inference technique in route guidance system,” The 2012 International Conference on Artificial Intelligence ICAI'12 , (Las Vegas, USA), pp. 1-5, 2012.
    79. Li, C., Anavatti, S., and Ray, T., “An AHP-Fuzzy approach for incorporation of drivers requirement in route guidance system,” in Proceedings of the Advances in Control and Optimization of Dynamical Systems, (Bangalore, India), pp. 1–5, 2012.
    80. Mohamad, A.F.A., Ray, T., and Smith, W., “The quest for optimum high speed planing craft design,” in Proceedings of the Pacific 2012 International Maritime Conference, (Sydney, Australia), pp. 507-516, 2012.
    81. Saha, A. and Ray, T., “A repair mechanism for active inequality constraint handling,” in Proceedings of the IEEE Congress on Evolutionary Computation, (Brisbane, Australia), pp. 1240-1247, 2012.
    82. Saha, A. and Ray, T., “Equality constrained multi-objective optimization,” in Proceedings of the IEEE Congress on Evolutionary Computation, (Brisbane, Australia), pp. 47-53, 2012.
    83. Saha, A., Ray, T. , Ogawa, H. and Boyce, R.R., “Robust design optimization of high performance axisymmetric scramjets based on surrogate assisted evolutionary algorithms,” in Proceedings of the 28th International Congress of the Aeronatical Sciences, (Brisbane, Australia), 2012.
    84. Smith, W., Mohamad, A. and Ray, T., “The design of high speed planing craft using an optimization framework,” in Proceedings of the 2012 ASME International Mechanical Engineering Congress and Exposition, (Houston, USA), IMECE2012-85844, 2012.
    85. Zhang,Z., Ihesiulor, O., Shankar, K., and Ray, T., “Comparison of inverse algorithms for delamination detection in composite laminates,” in Proceedings of the ASME 2012 Conference on Smart Materials, Adaptive Structures and Intelligent Systems, (Stone Mountain, Georgia, USA), pp. 577-585, 2012.
    86. Liu, M. and Ray, T., “A memetic algorithm with random key crossover and modified neighborhood search for the solution of capacitated arc routing problems,” The Sixth International Conference on Genetic and Evolutionary Computing, (Kitakyushu, Japan), pp. 433-436, 2012.
    87. Saha, A., Ray, T., Ogawa, H., and Boyce, R.R., “Learning from evolutionary algorithm based design optimization of axisymmetric scramjet inlets,” in Proceedings of the 11th Australian Space Science Conference, Canberra, Australia, pp. 351-357, 2011.
    88. Alam, K. , Ray, T. , and Sreenatha, A., “Design of a toy submarine using underwater vehicle design optimization framework,” in Proceedings of the Symposium on Computational Intelligence in Vehicles and Transportation Systems, (Paris, France), pp. 23–29, 2011.
    89. Alam, K. , Singh, H. , Isaacs, A. , Ray, T. and Sreenatha, A., “Design optimization of a model submarine: A reverse engineering approach,” in Proceedings of the International Conference on Evolutionary and Deterministic Methods for Design, Optimization and Control with Applications to Industrial and Social Problems, (Capua,Italy), pp. 200–202, 2011.
    90. Asafuddoula, M. ,Ray, T. and Sarker, R., “An adaptive differential evolution algorithm and its performance on real world optimization problems,” in Proceedings of the IEEE Congress on Evolutionary Computation, (New Orleans,USA), pp. 1057–1062, 2011.
    91. Hassanein, O.I , Anavatti, S. and Ray, T., “Fuzzy modeling and control for autonomous underwater vehicle,” in Proceedings of the 5th International Conference on Automation, Robotics and Applications, (Wellington, New Zealand), pp. 169–174, 2011.
    92. Hassanein, O. , Anavatti, S. and Ray, T., “Genetic fuzzy controller for robot manipulator position control based upon inverse dynamics,” in Proceedings of the IEEE Congress on Evolutionary Computation, (New Orleans,USA), pp. 4202–4209, 2011.
    93. Khan, M. , Mohamad, A.F.A, Isaacs, A. , and Ray, T., “A novel evolutionary approach for 2D shape matching based on B-Spline modeling,” in Proceedings of the IEEE Congress on Evolutionary Computation, (New Orleans,USA),pp. 655–661 ,2011.
    94. Li, C. , Anavatti, S. and Ray, T., “Short-term traffic prediction using different techniques,” in Proceedings of the 37th Annual Conference on IEEE Industrial Electronics Society, (Melbourne, Australia), pp. 2423–2428, 2011.
    95. Mohamad, A. , Ray, T., and Smith, W., “Scenario-based hydrodynamic design optimization of high speed planing craft for coastal surveillance,” in Proceedings of the IEEE Congress on Evolutionary Computation, (New Orleans, USA), pp. 354–361, 2011.
    96. Ogawa, H. , Alazet, Y. , Boyce, R.R. , Isaacs, A. , and Ray, T., “Design optimisation of axisymmetric scramjets for access-to-space,” in Proceedings of the 9th Australian Space Science Conference, (Sydney, Australia), pp. 1–10, 2011.
    97. Saha, A. and Ray, T. and Smith. W., “Towards practical evolutionary robust multi-objective optimization,” in Proceedings of the IEEE Congress on Evolutionary Computation, (New Orleans, USA), pp. 2123–2130, 2011.
    98. Saha, A. and Ray, T., “How does the good old Genetic Algorithm fare at Real World Optimization?,” in Proceedings of the IEEE Congress on Evolutionary Computation,(New Orleans,USA), pp. 1049–1056, 2011.
    99. Singh, H. , Ray, T., “Performance of a hybrid EA-DE-Memetic algorithm on CEC 2011 real world optimization problems,” in Proceedings of the IEEE Congress on Evolutionary Computation, (New Orleans,USA), pp. 1322–1326, 2011. Code
    100. Liu, M. and Ray, T., “A memetic algorithm with a novel split approach for efficient solution of modified capacitated arc routing problems,” in Proceedings of the International Conference on Operations Research, (Zurich, Switzerland), pp. 128–134, 2011. CARP Data
    101. Ogawa, H. , Brown, L. , Boyce, R.R. , and Ray, T., “Multiobjective design optimization of axisymmetric scramjet nozzle and external components considering static stability by using surrogate assisted evolutionary algorithms,” in Proceedings of the International Society of Air-breathing Engines, (Gothenburg, Sweeden), pp. 1–14, 2011.
    102. Mohamad, A.F.A., Nik, W.B.W., Ray, T. , and Smith, W., “Hull surface information retrieval and optimization of high speed planing craft,” in Proceedings of the First International Conference in Mechanical Engineering Research, (Pahang, Malaysia), 2011.
    103. Fearnley, J. and Ray, T., “Design and development of a six inch sub,” in Proceedings of the 1st Submarine Institute ofAustralia Technology Conference, (Adelaide, Australia), 2011.
    104. Gover, N. , Hill, C. , Alam, K. , Ray, T., Anavatti, S., “Design and development of a small, low-cost UUV for shallow water operations,” in Proceedings of the 1st Submarine Institute of Australia Technology Conference, (Adelaide, Australia), 2011.
    105. Ogawa, H., Alazet, Y., Pudsey, A., Boyce, R.R., Isaacs, A., and Ray, T., “Full flow-path optimization of axisymmetric scramjet engines,” in Proceedings of the 17th AIAA International Space Planes and Hypersonic Systems and Technologies Conference, (San Francisco, CA, USA), pp. 1762-1775, 2011.
    106. Mohamad, A.F.A., Ray, T., and Smith, W., “A framework for scenario-based hydrodynamic design optimization of hard chine planing craft,” in Proceedings of the 9th International Conference on Computer and IT Applications in the Maritime Industries, (Gubbio, Italy), pp. 7-19, 2010.
    107. Hassanein, O., Anavatti, S., and Ray, T., “Genetic PD control for two-link manipulator using inverse dynamics,” in Proceedings of the International Conference on Intelligent Unmanned System, (Bali, Indonesia), 2010.
    108. Mohamad, A.F.A., Ray, T., and Smith, W., “Hydrodynamic design optimization of a hard chine planing craft for coastal surveillance,” in Proceedings of the International Maritime Conference, (Sydney, Australia), pp. 72–82, 2010.
    109. Singh, H., Ray, T., and Smith, W., “Performance of infeasibility empowered memetic algorithm for CEC 2010 constrained optimization problems,” in Proceedings of the IEEE Congress on Evolutionary Computation, (Barcelona, Spain), pp. 3770–3777, 2010.IEMA Code
    110. Singh, H., Ray, T., and Smith, W., “Surrogate assisted simulated annealing (SASA) for constrained multi-objective optimization,” in Proceedings of the IEEE Congress of Evolutionary Computation, (Barcelona, Spain), pp. 4202–4209, 2010.
    111. Ashraf, M.A., Isaacs, A., Young, J., Lai, J.C.S., and Ray, T., “Numerical simulation and multi-objective design of flow over oscillating airfoil for power extraction,” in Proceedings of the Conference on Modelling Fluid Flow, (Budapest, Hungary), pp. 221–228, 2009.
    112. Isaacs, A., Ray, T., and Smith, W., “Memetic algorithm for dynamic bi-objective optimization problems,” in Proceedings of the IEEE Congress on Evolutionary Computation, (Trondheim, Norway), pp. 1707–1713, 2009.
    113. Ray, T., and Yao, X., “A cooperative coevolutionary algorithm with correlation based adaptive variable partitioning,” in Proceedings of the IEEE Congress on Evolutionary Computation, (Trondheim, Norway), pp. 983– 989, 2009.
    114. Saxena, D., Ray, T., Deb, K., and Tiwari, A., “Constrained many objective optimization: A way forward,” in Proceedings of the IEEE Congress on Evolutionary Computation,(Trondheim, Norway), pp. 545–5 52, 2009.
    115. Singh, H., Isaacs, A., Ray, T., and Smith, W., “An improved secondary ranking for many objective opti¬mization problems,” in Proceedings of the 11th Annual Conference on Genetic and Evolutionary Computation Conference, (Montreal, Canada), pp. 1837–1838, 2009.
    116. Singh, H., Isaacs, A., Nguyen, T.T., Ray, T., and Yao, X., “Performance of infeasibility driven evolutionary algorithm (IDEA) on constrained dynamic single objective optimization problems,” in Proceedings of the IEEE Congress on Evolutionary Computation, (Trondheim, Norway), pp. 3127–3134, 2009.
    117. Ogawa, H., Boyce, R.R., Isaacs, A., and Ray, T., “Multi-objective design optimisation of inlet and combustor for axisymmetric scramjets,” in Proceedings of the Australian Combustion Symposium, (Brisbane, Australia), pp. 1–5, 2009.
    118. Mohamad, A.F.A., Ray, T., and Smith, W., “An Optimization Framework for the design of Planning Craft,” in Proceedings of the International Conference on Computer Applications in Shipbuilding, (Shanghai, China), 2009.
    119. Isaacs, A., Puttige, V., Ray, T., Smith, W., and Sreenatha, A., “Development of a memetic algorithm for dynamic multi-objective optimization and its application to online system identification,” in Proceedings of the IEEE International Joint Conference on Neural Networks, (Hong Kong), pp. 548–554, 2008.
    120. Isaacs, A., Ray, T., and Smith, W., “Blessings of maintaining infeasible solutions for constrained multi-objective optimization problems,” in Proceedings of the IEEE Congress on Evolutionary Computation, (Hong Kong), pp. 2780–2787, 2008.
    121. Ray, T., and Smith, W., “Robust ship designs - balancing uncertainty - impacts of future trends,” in Proceedings of the International Maritime Conference, (Sydney, Australia), pp. 1–10, 2008.
    122. Sidek, M., Prusty, G., and Ray, T., “Determination of ship grounding and accident scenarios of a geographical area using risk analysis,” in Proceedings of the International Maritime Conference, (Sydney, Australia), pp. 1–10, 2008.
    123. Singh, H., Isaacs, A., Ray, T., and Smith, W., “A simulated annealing algorithm for single objective trans-dimensional optimization problems,” in Proceedings of the 8th International Conference on Hybrid Intelligent Systems, (Barcelona, Spain), pp. 19–24, 2008.
    124. Singh, H., Isaacs, A., Ray, T., and Smith, W., “A simulated annealing algorithm for constrained multi-objective optimization problems,” in Proceedings of the IEEE Congress on Evolutionary Computation, (Hong Kong), pp. 1655–1662, 2008.
    125. Smith, W., and Ray, T., “Preliminary frigate design using a multi-objective evolutionary algorithm,” in Proceedings of the International Maritime Conference, (Sydney, Australia), pp. 1–10, 2008.
    126. Isaacs, A., Ray, T., and Tsai, H.M., “Constrained aerodynamic shape optimization using an evolutionary algorithm with spatially distributed surrogates,” in Proceedings of the 26th AIAA Applied Aerodynamic Conference, (Honolulu, Hawaii), 2008.
    127. Isaacs, A., Ray, T., and Smith, W., “A hybrid evolutionary algorithm with simplex local search,” in Proceedings of the IEEE Congress on Evolutionary Computation, (Singapore), pp. 1701–1708, 2007.
    128. Isaacs, A., Ray, T., and Smith, W., “Novel evolutionary algorithm with set representation scheme for truss design,” in Proceedings of the IEEE Congress on Evolutionary Computation, (Singapore), pp. 3902–3908, 2007.
    129. Sanders, G., and Ray, T., “Optimal offline path planning of a fixed wing unmanned aerial vehicle (UAV) using an evolutionary algorithm,” in Proceedings of the IEEE Congress on Evolutionary Computation, (Singapore), pp. 4410–4416, 2007.
    130. Sarker, R., Ray, T., and da Fonseca, J.B., “An evolutionary algorithm for machine layout and job assignment problems,” in Proceedings of the IEEE Congress on Evolutionary Computation, (Singapore), pp. 3991–3997, 2007.
    131. Briggs, G.P., Ray, T., and Milthorpe, J., “Evolutionary algorithm’s use in optimization of a launch vehicle stack model,” in Proceedings of the 45th AIAA Aerospace Sciences Meeting and Exhibit, (Reno, USA), AIAA-2007-364, 2007.
    132. Ray, T., and Sarker, R., “Multi-objective evolutionary approach to the solution of gas lift optimization problems,” in Proceedings of the IEEE Congress on Evolutionary Computation, (Vancouver, Canada), pp. 3182– 3188, 2006.
    133. Briggs, G., Ray, T., and Milthorpe, J., “Optimization of a launch vehicle stack model using an evolutionary algorithm,” in Proceedings of the Systems Engineering, Test and Evaluation Conference, (Melbourne, Australia), 2006.
    134. Brown, M, Mudford, N.R., Neely, A.J., and Ray, T., “Robust design optimization of two-dimensional scramjet inlets,” in Proceedings of the 14th AIAA/AHI International Space Planes and Hypersonic Systems and Technologies Conference, (Canberra, Australia), AIAA 2006-8 140, 2006.
    135. Deepak, R., Ray, T., and Boyce, R.R., “Nose cone design optimization for a hypersonic flight experimental trajectory,” in Proceedings of the 14th AIAA/AHI International Space Planes and Hypersonic Systems and Technologies Conference, (Canberra, Australia), AIAA 2006-7998, 2006.
    136. Sarker, R., Kara, S., Kayis, S., Abbass, H., Freeman, G., and Ray, T., “A Multi-agent approach for analysing material flow in a manufacturing supply chain,” in Proceedings of the 2nd International Intelligent Logistics Systems Conference, (Brisbane, Australia), pp. 20.1–20.11, 2006.
    137. Ray, T., and Smith, W., “Surrogate assisted evolutionary algorithm for multi-objective optimization,” in Proceedings of the AIAA Specialist MDO Conference, (Rhode Island, USA), AIAA 2006-2050, 2006.
    138. Sarker, R., and Ray, T., “Multi-objective evolutionary algorithms for solving constrained optimization problems,” in Proceedings of the 2005 International Conference on Computational Intelligence for Modelling, Control and Automation, and International Conference on Intelligent Agents, Web Technologies and Internet Commerce (CIMCA-IAWTIC’05), (Vienna, Austria), vol. 2, pp. 197–202, 2005.
    139. Ray, T., “A comprehensive framework for multidisciplinary design optimization,” in Proceedings of the 15th International Conference on Engineering Design (ICED'05), The Practice of Engineering Design, pp. 462-463, 2005.
    140. Ray, T., and Won, K.S., “A comprehensive framework for multidisciplinary design optimization,” in Proceedings of the 3rd International Conference on Computational Intelligence, Robotics and Autonomous Systems, (Singapore), 2005.
    141. Chan, K.P., and Ray, T., “An evolutionary algorithm to maintain diversity in the parametric and the objective space,” in Proceedings of the 3rd International Conference on Computational Intelligence, Robotics and Autonomous Systems, (Singapore), 2005.
    142. Liu, Y., and Ray, T., “Performance of different surrogate models within a robust optimal design framework,” in Proceedings of the 15th International Conference on Engineering Design, (Melbourne, Australia), pp. 2913–2924, 2005.
    143. Sarker, R., and Ray, T., “Simultaneous optimisation of multi-objective linear and nonlinear problems,” in Proceedings of the 18th National Conference of the Australian Society for Operations Research, (Perth, Australia), pp. 163–170, 2005.
    144. Sarker, R., Kara, S., Kayis, S., Abbass, H., Freeman, G., and Ray, T., “A multi-agent simulation study for supply chain operation,” in Proceedings of the International Conference on Computational Intelligence for Modelling, Control and Automation, (Vienna, Austria), vol. 1, pp. 728–733, 2005.
    145. Won, K.S., Tsai, H.M., and Ray, T., “Flutter simulation and prediction via identification of nonlinear impulse response,” in Proceedings of 43rd AIAA Aerospace Sciences Meetings & Exhibit, AIAA 2005-834, (Reno, USA), 2005.
    146. Won, K.S., and Ray, T., “Performance of kriging and cokriging based surrogate models within the unified framework for surrogate assisted optimization,” in Proceedings of the IEEE Congress of Evolutionary Computation, (Portland, USA), pp. 1577–1585, 2004.
    147. Ray, T., and Tsai, H.M., “A parallel hybrid optimization algorithm for robust airfoil design,” in Proceedings of the 42nd Aerospace Sciences Meeting, AIAA 2004-905, (Reno, USA), 2004.
    148. Rao, C.S., Tsai, H.M., and Ray, T., “Aircraft configuration design using a multidisciplinary optimization approach,” in Proceedings of the 42nd AIAA Aerospace Sciences Meeting, AIAA-2004-536, (Reno, USA), 2004.
    149. Ray, T., Venkatarayalu, N., Won, K.S., and Chan, K.P., “Study on the behaviour and implementation of parent centric crossover within the generalized generation gap model,” in Proceedings of the IEEE Congress on Evolutionary Computation, (Canberra, Australia), pp. 1996–2003, 2003.
    150. Venkatarayalu, N.V., and Ray, T., “Single and multi-objective design of Yagi-Uda antennas using computational intelligence,” in Proceedings of the IEEE Congress on Evolutionary Computation, (Canberra, Australia), pp. 1237–1242, 2003.
    151. Won, K.S., Ray, T., and Kang, T., “A framework for optimization using approximate functions,” in Proceedings of the IEEE Congress on Evolutionary Computation, (Canberra, Australia), pp. 1520–1527, 2003.
    152. Won, K.S., Ray, T., and Kang, T., “Modelling and managing approximate functions within an optimization framework,” in Proceedings of the Computational Intelligence, Robotics and Autonomous Systems, (Singapore), 2003.
    153. Ng, K.Y., Ray, T., and Tsai, H.M., “Single and multi-objective wing planform and airfoil shapes optimization using a swarm algorithm,” in Proceedings of the 41st Aerospace Sciences Meeting, AIAA 2003-45, (Reno, USA), 2003.
    154. Tan, C.M., Ray, T., and Tsai, H.M., “A comparative study of evolutionary algorithm and swarm algorithm for airfoil shape optimization problems,” in Proceedings of the 41st Aerospace Sciences Meeting, AIAA 2003-102, (Reno, USA), 2003.
    155. Ray, T., “A behavioural model for single and multi-objective design optimization,” in Proceedings of International Conference on Ship and Ocean Technology, (Kharagpur, India), 2002.
    156. Ray, T., “Constrained robust optimal design using a multi-objective evolutionary algorithm,” in Proceedings of the IEEE Congress on Evolutionary Computation, (Hawaii, USA), pp. 419–424, 2002.
    157. Ray, T., Ling, P.C., and Kang, T., “A new fitness assignment and parent selection strategy within an evolutionary algorithm for constrained optimization problems,” in Proceedings of 4th Asia Pacific Conference on Simulated Evolution and Learning, (Singapore), 2002.
    158. Ray, T., and Tsai, H.M., “Some issues in NURBS representation of airfoil shapes for optimization,” in Proceedings of the 8th International Conference on Numerical Grid Generation in Computational Field Simulations, (Missisipi, USA), pp. 815-825, 2002.
    159. Ray, T., Tsai, H.M., and Tan, C.M., “Effects of solver fidelity on the performance of a stochastic parallel search for airfoil shape optimization,” in Proceedings of the 9th AIAA/ISSMO Symposium on Multidisciplinary Analysis and Optimization, (Atlanta, USA), pp. 1816–1826, 2002.
    160. Ray, T., and Liew, K.M., “A swarm with an effective information sharing mechanism for unconstrained and constrained single objective optimization problems,” in Proceedings of the IEEE Congress on Evolutionary Computation, (Seoul, South Korea), pp. 75–80, 2001.
    161. Ray, T., and Saini, P., “A simple illustration of optimization using a swarm,” in Proceedings ofInternational Conference on Computational Intelligence, Robotics and Autonomous Systems, (Singapore), pp. 29–34, 2001.
    162. Tan, H., Liew, K.M., Ray, T., and Tan, M.J., “Evolutionary algorithm for optimal process design in sheet metal forming for minimum spring-back,” in Proceedings of the Simulations of Materials Processing: Theory, Methods and Applications, Eds. K Mori, Swets and Zeitlinger, pp. 723–727, 2001.
    163. Ray, T., Tai, K., and Seow, K.C., “An evolutionary algorithm for constrained optimization,” in Proceedings of the Genetic and Evolutionary Computing Conference, (Las Vegas, USA), pp. 771–777, 2000.
    164. Kang, T., Guang, Y.C., and Ray, T., “Design synthesis of path generating complaint mechanisms by evolutionary optimization of topology and shape,” in Proceedings of the ASME DETC 2000 Design Automation Conference, (Maryland, USA), 2000.
    165. Ray, T., “Mathematical modelling for large scale system optimization,” in Proceedings of the Pacific Asian Conference on Expert Systems, (Singapore), pp. 742–747, 1997.
    166. Ray, T., Heng, K.J., and Khoong, C.M., “Mathematical modelling and optimization of a real life dynamic facility,” in Proceedings of the Ship and Ocean Technology Conference, (Kharagpur, India), 1997.
    167. Ray, T., and Mallick, S.K., “A decision model for optimum FPSO acquisition and management,”, in Proceedings of the Offshore Technology Conference, (Houston, USA), pp. 333-338, 1997.
    168. Ray, T., “A neural network model for design applications,” in Proceedings of the National Seminar on Aerospace Structures, (Bombay, India), 1996.
    169. Ray, T., Gokarn, R.P., and Sha, O.P., “An integrated decision support system for concept design,” in Proceedings of the International Conference on Maritime Technology, (Singapore), pp. 161–177, 1995.
    170. Ray, T., Gokarn, R.P., and Sha, O.P., “Analysis of ship design under uncertainty in the multiple criteria decision making framework,” in Proceedings of the International Symposium on Practical design of Ships and Mobile Units, (Seoul, South Korea), vol. 2, pp. 1264–1266, 1995.
    171. Sha, O.P, Ray, T., and Gokarn, R.P., “An artificial neural network model for preliminary ship design,” in Proceedings of the International Conference on Computer Applications in Shipbuilding, (Bremen, Germany), vol. 2, pp. 10–15, 1994.
    172. Ray, T., Gokarn, R.P., and Sha, O.P., “Selection of the main propulsion plant at the preliminary design stage using multiple criteria decision making,” in Proceedings of the International Maritime Conference, (Bombay, India), pp. 3–14, 1994.