2024/forthcoming
  1. Kenny, A., Ray, T., Singh, H.K., “An Extension of the Welded Beam Problem that Includes Multiple Interacting Design Concepts,” in Proceedings of International Conference on Evolutionary Multi-Criterion Optimization, (Canberra, Australia), in press, accepted 11/2024.
  2. Wang, B., Singh, H.K., Ray, T., “Selective evaluations for expediting multi-objective bilevel optimization,” in Proceedings of International Conference on Evolutionary Multi-Criterion Optimization, (Canberra, Australia), in press, accepted 11/2024.
  3. Singh, H.K., “Extended Results on Analytical Hypervolume Indicator Calculation of Linear and Quadratic Pareto Fronts,” in Proceedings of International Conference on Evolutionary Multi-Criterion Optimization, (Canberra, Australia), in press, accepted 11/2024.
  4. 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.
  5. Rahi, K.H., Singh, H.K. and Ray, T., “Towards solving expensive optimization problems with heterogeneous constraint costs,” in Proceedings of the Genetic and Evolutionary Computation Conference Companion, (Melbourne, Australia), pp. 2032-2040, 2024.
  6. 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), 2024.
  7. Kenny, A., Ray, T. and Singh, H.K., “A Framework for Design Optimization across Multiple Concepts,” Scientific Reports, vol. 14, no. 1, p.7858, 2024.
  8. Kenny, A., Ray, T., Limmer, S., Singh, H.K., Rodemann, T., and Olhofer, M., , “A Hierarchical Dissimilarity Metric for Automated Machine Learning Pipelines, and Visualizing Search Behaviour,” in Proceedings of Evostar (EvoAPPS), pp. 115-129, 2024.
  9. Niloy, R.S.,Singh, H.K. and Ray, T., "A benchmark test suite for evolutionary multi-objective multi-concept optimization", Swarm and Evolutionary Computation, vol. 84, p. 101429, 2024.
2023
  1. 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.
  2. 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.
  3. Niloy, R.S.,Singh, H.K. and Ray, T., "A Brief Review of Multi-Concept Multi-Objective Optimization Problems", in Proceedings of 2023 IEEE Symposium Series On Computational Intelligence, (Mexico city, Mexico), pp. 1151-1157, 2023.
  4. 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.
  5. Lette, M., Rahi, K.H., 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.
  6. 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.
  7. 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.
  8. Kenny, A., Ray, T., Singh, H.K., Li, X., “A test suite for multi-objective multi-fidelity optimization,” in Proceedings of Evolutionary Multi-Criterion Optimization, (Leiden, Netherlands), pp. 361-373, 2023.
  9. 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, vol. 9, no. 2, pp. 1193-1209, 2023.
2022
  1. 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.
  2. Huynh, Q.N., Singh, H.K., 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.
  3. Tarek, M. and Ray, T., “Approximation schemes for stochastic compliance-based topology optimization with many loading scenarios,” Structural and Multidisciplinary Optimization, 65(5), pp.1-19, 2022
  4. Singh, H.K. and Branke, J., “Identifying Stochastically Non-dominated Solutions Using Evolutionary Computation,” In International Conference on Parallel Problem Solving from Nature (pp. 193-206). Springer, Cham, 2022
  5. Mamun, M.M., Singh, H.K. and Ray, T., “An approach for computationally expensive multi-objective optimization problems with independently evaluable objectives,” Swarm and Evolutionary Computation, 75, p.101146, 2022
  6. Ray, T., Singh, H.K., Rahi, K.H., Rodemann, T. and Olhofer, M., 2022. “Towards identification of solutions of interest for multi-objective problems considering both objective and variable space information,” Applied Soft Computing, 119, p.108505, 2022
  7. Ray, T., Mamun, M.M. and Singh, H.K., “A Simple Evolutionary Algorithm for Multi-modal Multi-objective Optimization,” Proceedings of IEEE Congress on Evolutionary Computation, (Padua, Italy), pp. 1-19, 2022.
  8. Wang, B., Singh, H.K. and Ray, T., “Investigating Neighborhood Solution Transfer Schemes for Bilevel Optimization,” In 2022 IEEE Congress on Evolutionary Computation (CEC) (pp. 1-8). IEEE.
  9. Mamun, M.M., Singh, H.K. and Ray, T., “A Multifidelity Approach for Bilevel Optimization With Limited Computing Budget,” IEEE Transactions on Evolutionary Computation, 26(2), pp.392-399, 2022
  10. Huynh, Q.N., Singh, H.K. 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 (pp. 558-561), 2022
2021
  1. 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.
  2. Rahi, K.H., Singh, H.K. and Ray, T., “Partial Evaluation Strategies for Expensive Evolutionary Constrained Optimization,” IEEE Transactions on Evolutionary Computation, vol. 25, no. 6, pp. 1103-1117, 2021.
  3. 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.
  4. 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.
  5. 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 Conference Companion, (Lille, France), pp. 211-212, 2021.
  6. Wang, B., Singh, H.K., and Ray, T., “Investigating normalization bounds for hypervolume-based infill criterion for expensive multiobjective optimization,” in Proceedings of International Conference on Evolutionary Multi-Criterion Optimization, (Shenzhen, China), pp. 519-530, 2021.
  7. Chen, Y., Singh, H.K., Zhou,A., and Ray, T., “A fast converging evolutionary algorithm for constrained multiobjective portfolio optimization,” in Proceedings of International Conference on Evolutionary Multi-Criterion Optimization, (Shenzhen, China), pp. 283-295, 2021.
  8. Rahi, K.H., Singh, H.K. and Ray, T., “Feasibility-ratio based sequencing for computationally efficient constrained optimization,” Swarm and Evolutionary Computation, vol. 62, p.100850, 2021.
2020
  1. Rahi, K.H., Singh, H.K. and Ray, T., “Evolutionary algorithm embedded with bump-hunting for constrained design optimization,” ASME Journal of Mechanical Design, vol. 143, issue 2, p.021706, 2020.
  2. Singh, H.K., and Deb, K., “Investigating the Equivalence Between PBI and AASF Scalarization for Multi-objective Optimization,” Swarm and Evolutionary Computation, vol. 53, p.100630, 2020.
  3. Singh, H.K., “Understanding Hypervolume Behavior Theoretically for Benchmarking in Evolutionary Multi/Many-objective Optimization,” IEEE Transactions on Evolutionary Computation, vol. 24, issue 3, pp. 603-610, 2020
  4. 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 Companion, (Cancun, Mexico), pp. 201-201, 2020.
  5. 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 Companion, (Cancun, Mexico), pp. 267-268, 2020.
  6. 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), 2020.
  7. 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.
2019
  1. 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.
  2. 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.
  3. 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.
  4. Chand, S., Singh, H.K., and Ray, T., “Evolving rollout-justification based heuristics for resource constrained project scheduling problems,” Swarm and Evolutionary Computation, vol.50, 100556, 2019.
  5. 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.
  6. 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 Companion, (Prague, Czech Republic), pp. 1734-1741, 2019.
  7. 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 Companion, (Prague, Czech Republic), pp. 201-202, 2019.
  8. 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.
  9. Singh, H.K. and Deb, K., “A Parametric Investigation of PBI and AASF Scalarizations,” in Proceedings of the Genetic and Evolutionary Computation Companion, (Prague, Czech Republic), pp. 233-234, 2019.
  10. 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.
  11. 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.
  12. 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.
  13. Bhattacharjee, K.S., Singh, H.K. and Ray, T., “Optimum Wind Farm Layouts: A Many-objective Perspective and Case Study,” International Conference on Evolutionary Multi-criterion Optimization, Lecture Notes in Computer Science LNCS 11411, pp. 707-718, 2019.
  14. Singh, H.K., Bhattacharjee, K.S.,and Ray, T., “Distance based subset selection for benchmarking in evolutionary multi/many-objective optimization,” IEEE Transactions on Evolutionary Computation, in Press, Accepted 11/2018
  15. Pan, J., Zhang, Z., Wu, J., Ramakrishnan, K., and Singh, H.K., “A novel method of vibration modes selection for improving accuracy of frequency-based damage detection,” Composites Part B, vol. 159, pp. 437-446, 2019.
2018
  1. Asafuddoula, M., Singh,H.K. 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.
  2. Habib, A., Singh, H.K. and Ray, T., “A multiple surrogate assisted evolutionary algorithm for optimization involving iterative solvers,” Engineering Optimization, volume 50, issue 9, pp. 1625-1644, 2018.
  3. Singh, H.K., Bhattacharjee, K.S., Ray, T., and Mostaghim, S.,“Investigation of a simple distance based ranking metric for decomposition-based multi/many-objective evolutionary algorithms,” Australasian Conference on Artificial Intelligence, Lecture Notes in Computer Science (Artificial Intelligence), pp. 384-396, 2018.
  4. Ray, T., Habib, A., Singh, H.K., and Ryan, M., “Uncovering performance envelopes through optimum design of tests,” Australasian Conference on Artificial Intelligence, Lecture Notes in Computer Science (Artificial Intelligence), pp. 445-457, 2018.
  5. Bhattacharjee, K.S., Singh, H.K. and Ray, T., “Multiple surrogate assisted many-objective optimization for engineering design,” ASME Journal of Mechanical Design, volume 140, issue 5, p.051403, 2018.
  6. Huynh, Q.N., Chand, S., Singh, H.K., 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
  7. 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.
  8. Zhang, Z., He, M., Liu, A., Singh, H.K., Ramakrishnan, K., Hui, D., Shankar, K., Morozov, E., “Vibration-based assessment of delaminations in FRP composites,” Composites Part B, vol. 144, pp. 254-266, 2018.
  9. 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.
  10. Milowski, J., Bhattacharjee, K.S., Singh, H.K. 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, 2018.
2017
  1. Zhang, Z., Zhan, C., Shankar, K., Morozov, E., Singh, H.K., Ray, T., “Sensitivity Analysis of Inverse Algorithms for Damage Detection in Composites,” Composite Structures, volume 175, pp. 844-859, 2017.
  2. Bhattacharjee, K.S., Singh, H.K., Ray, T., “Enhanced Pareto Interpolation Method to Aid Decision Making for Discontinuous Pareto Optimal Fronts,” Australasian Conference on Artificial Intelligence, Lecture Notes in Computer Science (Artificial Intelligence), pp. 93-105, 2017.
  3. Islam, M.M., Singh, H.K., Ray, T., “Use of a non-nested formulation to improve search for bilevel optimization,” Australasian Conference on Artificial Intelligence, Lecture Notes in Computer Science (Artificial Intelligence), pp. 106-118, 2017.
  4. Bhattacharjee, K.S., Singh, H.K., 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.
  5. Zhang, Z., Pan, J., Fu, J., Singh, H. K., Pi, Y., Wu, J., and Rao, R., “Optimization of long span portal frames using spatially distributed surrogates”, Steel and Composite Structures, vol. 24, no. 2, pp. 227-237, 2017.
  6. 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
  7. 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.
  8. Zhang, H., Zhou, A., Zhang, G. and Singh, H.K., “Accelerating MOEA/D by Nelder-Mead method”, IEEE Congress on Evolutionary Computation (CEC), (San Sebastian, Spain), pp. 976-983, 2017.
  9. Islam, M.M., Singh, H.K., Ray, T., “A Surrogate Assisted Approach for Single-objective Bilevel Optimization,” IEEE Transactions on Evolutionary Computation, vol. 21, issue 5, pp. 681-696, 2017.
  10. Bhattacharjee, K.S., Singh, H.K. and Ray, T., “A novel decomposition based evolutionary algorithm for engineering design optimization,” ASME Journal of Mechanical Design, vol. 139, part 4, p. 041403, 2017.
  11. Habib, A., Singh, H. and Ray, T., A Batch Infill Strategy for Computationally Expensive Optimization Problems,” in Proceedings of the Australasian Conference on Artifical Life and Computational Intelligence, Lecture Notes in Artificial Intelligence, vol. 10142, pp 74-85, 2017
  12. 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.
  13. 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
  14. Branke, J., Asafuddoula, M., Bhattacharjee, K.S., Ray, T.,"Efficient Use of Partially Converged Simulations in Evolutionary Optimization," IEEE Transactions on Evolutionary Computation, vol. 21m issue 1, pp. 52-64, 2017.
  15. 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, volume 47, issue 11, 2017.
2016
  1. 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), available online, pp. 1-8, 2016
  2. 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), available online, pp. 1-8, 2016
  3. Bhattacharjee, K.S., Singh, H.K. 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.
  4. 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.
  5. Bhattacharjee, K.S., Singh, H. and Ray, T., “Multi-objective optimization with multiple spatially distributed surrogates,” Journal of Mechanical Design, vol. 138, issue 9, 2016. SAMO Code
  6. 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.
  7. Singh, H.K., 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.
  8. Zaman, M.F., Elsayed, S., Ray, T., and Sarker, R., “Configuring two-algorithm-based evolutionary approach for solving dynamic economic dispatch problems," Journal Engineering Applications of Artificial Intelligence, vol. 53, no. 2, pp. 105-125, 2016.
  9. 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.
  10. Lu, K, Branke, J., and Ray, T., "Improving efficiency of bi-level worst case optimization,” "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.
  11. Singh, H.K., Alam, K., and Ray, T., "Use of infeasible solutions during constrained evolutionary search: A short survey,” in Proceedings of the Australasian Conference on Artifical Life and Computational Intelligence, (Canberra, Australia), vol. 9592, Lecture Notes in Artificial Intelligence, pp 193-205, Springer, 2016.
  12. Zaman, M.F., Elsayed, S., Sarker, R., Ray, T., "A double action genetic algorithm for scheduling the wind-thermal generators,” in Proceedings of the Australasian Conference on Artifical Life and Computational Intelligence, (Canberra, Australia), vol. 9592, Lecture Notes in Artificial Intelligence, pp 258-269, Springer, 2016.
  13. Bhattacharjee, K.S., Singh, H.K., and Ray, T., "A study on performance metrics to identify solutions of interest from a trade-off set,” in Proceedings of the Australasian Conference on Artifical Life and Computational Intelligence, (Canberra, Australia), vol. 9592, Lecture Notes in Artificial Intelligence, pp 66-77, Springer, 2016.
  14. Ismail, M.A., Elsayed, S., Ray, T., and Sarker, R., , "A differential evolution algorithm for solving resource constrained project scheduling problems,” in Proceedings of the Australasian Conference on Artifical Life and Computational Intelligence, (Canberra, Australia), vol. 9592, Lecture Notes in Artificial Intelligence, pp 209-220, Springer, 2016.
  15. Islam, M.. Singh, H.K., and Ray, T., "A nested differential evolution based algorithm for solving multi-objective bilevel optimization problems,” in Proceedings of the Australasian Conference on Artifical Life and Computational Intelligence, (Canberra, Australia),vol. 9592, Lecture Notes in Artificial Intelligence, pp 101-112, Springer, 2016.
  16. 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.
  17. 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.
  18. 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.
  19. 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.
  20. 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.
  21. 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.
  22. 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.
  23. 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.
  24. 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.
  25. 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
2015
  1. 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.
  2. Asafuddoula, M., Singh, H.K. 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.
  3. Ray, T., Asafuddoula, M., Singh, H.K. and Alam, K., “An approach to identify six sigma robust solutions of multi/many-objective engineering design optimization problems,” Journal of Mechanical Design, vol. 137, issue 5, pp. 051404-051404, 2015.
  4. 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. Contact: Md.Asaf@adfa.edu.au for comments.
  5. 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.
  6. Bhattacharjee, K.S., and Ray, T., "Cost to evaluate versus Cost to learn ? Performance of selective evaluation strategies in multiobjective optimization,” in Proceedings of the Australasian Joint Conference on Artificial Intelligence, (Canberra, Australia), pp. 63-75, 2015.
  7. Bhattacharjee, K.S., and Ray, T. , "An evolutionary algorithm with classifier guided constraint evaluation strategy for computationally expensive optimization problems,” in Proceedings of the Australasian Joint Conference on Artificial Intelligence, (Canberra, Australia), pp. 49-62, 2015. Supplementary Material
  8. Singh, H.K, Asafuddoula, M., Alam, K., and Ray, T., “Re-design for robustness: An approach based on many objective optimization,” in Proceedings of the Seventh International Conference on Evolutionary Multi-Criterion Optimization (EMO-2015), (Guimaraes, Portugal), vol. 9019, Part-II, Lecture Notes in Computer Science, pp. 343-357, Springer, 2015.
  9. 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.
  10. Asafuddoula, M., Ray, T., and Singh, H.K., "Characterizing Pareto front approximations in many-objective optimization,” in Proceedings of the Genetic and Evolutionary Computation Conference, (Madrid, Spain), pp. 607-614, 2015.
  11. Asafuddoula, M., Ray, T., Isaacs, A., and Singh, H.K., "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.
  12. 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.
  13. Islam, M.. Singh, H.K., 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.
  14. Wang, B., Singh, H.K., 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.
  15. 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..
  16. 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.
  17. Bhattacharjee, K.S., and Ray, T., “A novel constraint handling strategy for expensive optimization problems,” in Proceedings of the Eleventh World Congress of Structural and Multidisciplinary Optimization,(Sydney, Australia), 2015.
  18. Singh, H.K., and Ray, T., “Many-objective optimization in engineering design: Case studies using a decomposition based evolutionary algorithm,” in Proceedings of the Eleventh World Congress of Structural and Multidisciplinary Optimization,(Sydney, Australia), 2015.
2014
  1. 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.
  2. Alam,K. Ray, T., and Anavatti, S., “A brief taxonomy of autonomous underwater vehicle design literature,” Ocean Engineering,vol. 88, pp. 627-630, 2014.
  3. 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
  4. Alam, K. , Ray, T. and Anavatti, S., “Design and construction of an autonomous underwater vehicle,” Neurocomputing, vol.142, pp. 16-29, 2014.
  5. Liu, M., Singh, H. K, 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.
  6. 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.
  7. 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.
  8. 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, vol. 56, pp. 906–925, 2014.
  9. 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.
  10. 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
  11. 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.
  12. 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.
  13. Liu, M., Singh, H.K., 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.
  14. Liu, M., Singh, H.K., 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.
  15. 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.
  16. 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.
  17. Singh, H.K., 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.
  18. Singh, H.K., 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.
2013
  1. 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.
  2. 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.
  3. 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.
  4. 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.
  5. Singh, H.K. , 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
  6. Hassanein, O., Anavatti, S., and Ray, T., “Hybrid neuro-fuzzy network identification for autonomous underwater vehicle,” in Proceedings of the Swarm, Evolutionary and Memetic Computing SEMCCO-2013,(Chennai, India), vol. 8298, Lecture Notes in Computer Science, pp. 287-297, Springer, 2013.
  7. Kirsanov, A., Anavatti, S., and Ray, T., “Path planning with respect of flows and dynamic obstacles for the autonomous underwater vehicle,” in Proceedings of the Swarm, Evolutionary and Memetic Computing SEMCCO-2013, (Chennai,India), vol. 8298, Lecture Notes in Computer Science, pp. 476-486, Springer, 2013.
  8. 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 the Seventh International Conference on Evolutionary Multi-Criterion Optimization (EMO-2013), (Sheffield, UK), vol. 7811, Lecture Notes in Computer Science, pp. 417-427, Springer, 2013.
  9. 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.
  10. 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.
  11. 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.
  12. 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.
  13. 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.
  14. 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.
  15. 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.
2012
  1. 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.
  2. 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.
  3. 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.
  4. 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.
  5. 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
  6. 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.
  7. Alam, K., Ray, T. and Anavatti, S., “An evolutionary approach for the design of autonomous underwater vehicles,” in Proceedings of Advances in Artificial Intelligence, (Sydney, Australia), vol. 7691, Lecture Notes in Artificial Intelligence, pp. 279-290, Springer, 2012.
  8. Asafuddoula, M.,Ray, T. and Sarker, R., “A self-adaptive differential evolution algorithm with constraint sequencing,” in Proceedings of Advances in Artificial Intelligence, (Sydney, Australia), vol. 7691, Lecture Notes in Artificial Intelligence, pp. 182-193, Springer, 2012.
  9. Khan, M., and Ray, T., “A memetic algorithm for efficient solution of 2D and 3D shape matching problems,” in Proceedings of Advances in Artificial Intelligence, (Sydney, Australia), vol. 7691, Lecture Notes in Artificial Intelligence, pp. 362-372, Springer, 2012.
  10. Liu, M., and Ray, T., “Efficient solution of capacitated arc routing problems with a limited computational budget,” in Proceedings of Advances in Artificial Intelligence, (Sydney, Australia), vol. 7691, Lecture Notes in Artificial Intelligence, pp. 791-802, Springer, 2012.
  11. 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
  12. 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.
  13. 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.
  14. 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.
  15. 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.
  16. 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.
  17. 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.
  18. 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.
  19. 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.
  20. 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.
  21. 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.
  22. 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.
  23. 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.
  24. 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.
  25. 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.
  26. 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.
  27. 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.
  28. 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.
  29. 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.
2011
  1. Prusty, B. G., Sul, J., and Ray, T., "Fatigue behaviour of short fibre composites," Nova Science publishers, New York, USA, 2011.
  2. Sarker, R., and Ray, T., “Agent-based evolutionary algorithms: emerging paradigm or buzzwords?”, OR/MS Today, vol. 38, no. 5, October, 2011.
  3. 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.
  4. 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.
  5. 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 PartB, vol. 42, issue 6, pp. 1453–1460, 2011.
  6. Saha, A. and Ray, T., “Practical robust design optimization using evolutionary algorithms,” Journal of Mechanical Design, vol. 133, pp. 101012-1–101012-19, 2011.
  7. Singh, H.K. ,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
  8. 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.
  9. Singh, H.K, Ray, T. and Smith, W., “Performance of infeasibility empowered memetic algorithm(IEMA) on engineering design problems,” in Proceedings of Advances in Artificial Intelligence, (Adelaide, Australia), vol. 6464, Lecture Notes in Computer Science, pp. 425-434, Springer, 2011.IEMA Code
  10. 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.
  11. 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.
  12. Alam, K. , Singh, H.K. , 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.
  13. 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.
  14. 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.
  15. 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.
  16. 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.
  17. 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.
  18. 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.
  19. 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.
  20. 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.
  21. 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.
  22. Singh, H.K. , 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
  23. 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
  24. 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.
  25. 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.
  26. 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.
  27. 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.
  28. 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.
2010
  1. 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.
  2. Singh, H.K. 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.
  3. 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.
  4. 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.
  5. Singh, H.K. and Ray, T. , “C-PSA: Constrained Pareto simulated annealing for constrained multi-objective optimization,” Information Sciences, vol. 180, no. 13, pp. 2499–2513, 2010.
  6. 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.
  7. 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.
  8. 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.
  9. Singh, H.K., 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
  10. Singh, H.K., 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.
2009
  1. 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
  2. Ray, T., Singh, H.K. , 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
  3. 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.
  4. 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.
  5. 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.
  6. 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.
  7. 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.
  8. 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.
  9. Singh, H.K., 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.
  10. Singh, H.K., 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.
  11. 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.
  12. 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.
2008
  1. 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.
  2. 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.
  3. 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.
  4. 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.
  5. 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.
  6. Isaacs, A., Ray, T. , and Smith, W., “An efficient hybrid algorithm for optimization of discrete structures,” in Proceedings of Simulated Evolution and Learning, (Melbourne, Australia), vol. 5361, Lecture Notes in Computer Science, pp. 625–634, Springer, 2008.
  7. Singh, H.K., 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 Simulated Evolution and Learning, (Melbourne, Australia), vol. 5361, Lecture Notes in Computer Science, pp. 401–410, Springer, 2008.
  8. Singh, H.K., Isaacs, A., Ray, T., and Smith, W., “Infeasibility driven evolutionary algorithm (IDEA) for engineering design optimization,” in Proceedings of the 21st Australasian Joint Conference on Artificial Intelligence, (Auckland, New Zealand), vol. 5360, Lecture Notes in Artificial Intelligence, pp. 104–115, Springer, 2008.
  9. 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.
  10. 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.
  11. 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.
  12. 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.
  13. Singh, H.K., 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.
  14. Singh, H.K., 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.
  15. 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.
  16. 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.
2007
  1. 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.
  2. 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.
  3. 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.
  4. Liew, K. M. , Ray, T. , and Tan, P.K., “Computational swarm strategies for single objective design optimization problems,” International Journal for Computational Methods in Engineering Science and Mechanics, vol. 8, no. 1, pp. 9–18, 2007.
  5. Isaacs, A., Ray, T., and Smith, W., “An evolutionary algorithm with spatially distributed surrogates for multi-objective optimization,” in Proceedings of the 3rd Australasian Conference on Artificial Life, (Gold Coast, Australia), vol. 4828, Lecture Notes in Computer Science, pp. 257–268, Springer, 2007.
  6. Puttige, V., Sreenatha, A., and Ray, T., “Comparative analysis of multiple neural networks for online identification of a UAV,” in Proceedings of the Advances in Artificial Intelligence, (Gold Coast, Australia), vol. 4830, Lecture Notes in Computer Science, pp. 120–129, Springer, 2007.
  7. 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.
  8. 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.
  9. 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.
  10. 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.
  11. 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.
2006
  1. 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.
  2. 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
  3. 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.
  4. 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.
  5. 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.
  6. 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.
  7. 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.
  8. 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.
2005
  1. 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.
  2. 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.
  3. Won, K.S. and Ray, T., ”A framework for design optimization using surrogates,” Engineering Optimization, vol. 37, no. 7,pp. 685–703, 2005.
  4. Ray, T.,
  5. 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.
  6. 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.
  7. 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.
  8. 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.
  9. 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.
  10. 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.
  11. 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.
  12. 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.