Publications: Citations Reports: Google Scholar, SCOPUS, ORCID, ResearcherID

Journal Papers

  1. Kenny, A., Ray, T. and Singh, H.K., “A Framework for Design Optimization across Multiple Concepts,” Nature Scientific Reports, vol.14, p.7858, 2024. [Q1, SNIP 1.3, IF 4.6]
  2. 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. [Q1, SNIP 2.99, IF 10.267]
  3. Zhang, Z., Li, S., Singh, H.K., Lan, X., Zhang, K., Wahgn, H., Ng, C-T, and Wang, C., “Vibration-based Detection of Non-Overlapping Delaminations in FRP Beams Using Frequency Shifts”, Journal of Sound and Vibration, vol. 583, p.118531, 2024. [Q1, SNIP 1.95, IF 4.7].
  4. 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. [Q1, SNIP 5.62, IF 16.497]
  5. 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. [Q1, SNIP 5.62, IF 16.497]
  6. 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. [Q1, SNIP 1.33, IF 3.476]
  7. 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, vol. 75, p.101146, 2022. [Q1, SNIP 2.99, IF 10.267]
  8. Ray, T., Singh, H.K., Rahi, K., Rodemann, T. and Olhofer, M., “Towards identification of solutions of interest for multi-objective problems considering both objective and variable space information,” Applied Soft Computing, vol. 119, p.108505, 2022. [Q1, SNIP 2.40, IF 8.263]
  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, vol. 26, issue 2, pp. 392 - 399, 2022. [Q1, SNIP 5.62, IF 16.497] .
  10. 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. [IF 6.7] .
  11. 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. [Q1, SNIP 5.62, IF 16.497] .
  12. Tong, H., Pan, J., Singh, H.K., Luo, W., Zhang, Z. and Hui, D., “Delamination detection in composite laminates using improved surrogate-assisted optimization”. Composite Structures, vol. 277, p.114622, 2021. [Q1, SNIP 1.99, IF 4.829].
  13. Pan, J., Shankar, K., Singh, H.K., Wang, H., Zhang, Z. and Tong,H., “Vibration-based Detection of Skin-Stiffener Debonding on Composite Stiffened Panels Using Surrogate-assisted Algorithms”. Composite Structures, vol. 270, p.114090, 2021. [Q1, SNIP 1.99, IF 4.829].
  14. 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. [Q1, SNIP 2.99, IF 10.267]
  15. El-fiqi, H., Campbell, B., Elsayed, S., Perry, A., Singh, H.K., Hunjet, R. and Abbass, H., “The Limits of Reactive Shepherding Approaches for Swarm Guidance,” IEEE Access, vol. 8, pp. 214658-214671.[Q1, SNIP 1.33, IF 3.476]
  16. 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. [Q1, SNIP 1.44, IF 3.441]
  17. 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 [Q1, SNIP 5.62, IF 16.497] .
  18. 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. [Q1, SNIP 2.99, IF 10.267]
  19. 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, vol. 23, issue 5, pp. 904-912 [Q1, SNIP 5.62, IF 16.497] .
  20. Singh, H.K., Islam, M.M., Ray, T. and Ryan, M., “Nested evolutionary algorithms for computationally expensive bilevel optimization problems: Variants and their systematic analysis,” Swarm and Evolutionary Computation, vol. 48, pp. 329-344, 2019. [Q1, SNIP 2.99, IF 10.267]
  21. 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. [Q1, SNIP 2.99, IF 10.267]
  22. Habib, A., Singh, H.K. and Ray, T., “A multiple surrogate assisted multi/many-objective multi-fidelity evolutionary algorithm,” Information Sciences, vol. 502, pp. 537-557, 2019. [Q1, SNIP 2.40, IF 8.223]
  23. Habib, A., Singh, H.K., Chugh, T., Ray, T. and Miettinen, K., “A Multiple Surrogate Assisted Decomposition Based Evolutionary Algorithm for Expensive Multi/Many-Objective Optimization,” IEEE Transactions on Evolutionary Computation, vol. 23, issue 6, pp. 1000-1014, 2019. [Q1, SNIP 5.62, IF 16.497] .
  24. Zhang, Z., Pan, J., Luo, W., Ramakrishnan, K. R., and Singh, H.K., ``Vibration-based delamination detection in curved composite plates". Composites Part A: Applied Science and Manufacturing, vol. 119, pp. 261-274.[Q1, SNIP 2.59, IF 9.483]
  25. Fernandez-Rojas, R., Perry, A., Singh, H.K., Campbell, B., Elsayed, S., Hunjet, R., & Abbass, H., ``Contextual Awareness in Human-Advanced-Vehicle Systems: A Survey". IEEE Access, vol.7, pp. 33304-33328.[Q1, SNIP 1.33, IF 3.476]
  26. 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. [Q1, SNIP 2.51, IF 6.864]
  27. Chand, S., Singh, H.K., 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. [Q1, SNIP 2.99, IF 10.267]
  28. 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 [Q1, SNIP 5.62, IF 16.497] .
  29. 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. [Q1, SNIP 3.67, IF 19.118]
  30. 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. [Q1, SNIP 1.44, IF 3.441]
  31. 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 [Q2, SNIP 1.24, IF 2.500] .
  32. 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. [Q1, SNIP 2.51, IF 6.864]
  33. Chand, S., Huynh, Q.N., Singh, H.K., 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. [Q1, SNIP 2.40, IF 8.223]
  34. Keshk, M., Singh, H.K. and Abbass, H., “Automatic estimation of differential evolution parameters using Hidden Markov Models,” Evolutionary Intelligence, vol. 10, issue 3-4, pp. 77-93, 2018.
  35. He,Y., Wan,J., Lei,X., and Singh,H.K., “Flood Disaster Level Evaluation using a Particle Swarm Optimization Algorithm considering Decision-maker’s Preference,” Water Science and Technology: Water Supply, vol. 18, issue 1, pp. 288-298, 2018.
  36. Zhang,Z., Zhan,C., Shankar,K., Morozov,E., Singh,H.K., and Ray, T., “Sensitivity Analysis of Inverse Algorithms for Damage Detection in Composites,” Composite Structures, vol. 176, pp. 844-859, 2017. [Q1, SNIP 1.99, IF 4.829].
  37. 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. [Q1, SNIP 5.62, IF 16.497] .
  38. 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 [Q1; SNIP 1.10, IF 6.386]
  39. 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. [Q1; IF 16.497, SNIP 5.62] .
  40. 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, issue 4, p.041403, 2017. [Q1; IF 3.441, SNIP 1.44]
  41. Islam, M.M., Singh, H.K., Ray, T., Sinha, A., “An enhanced memetic algorithm for single objective bilevel optimization problems,” Evolutionary Computation, vol. 25, Issue 4, pp. 607-642, 2017.[Q1, SNIP 1.8, IF 4.766].
  42. 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. [Q1, SNIP 1.54, IF 1.996] .
  43. 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, pp. 091404, 2016. [Q1; IF 3.441, SNIP 1.44].
  44. Bhattacharjee, K.S., Singh, H.K. and Ray, T., “Multi-objective optimization with multiple spatially distributed surrogates” ASME Journal of Mechanical Design, vol. 138, issue 9, pp. 091401, 2016 [Q1; IF 3.441, SNIP 1.44].SAMO Code
  45. Singh, H.K., “Development of optimization methods to deal with current challenges in engineering design optimization.” AI Communications vol 21, issue 1, pp. 219-221, 2016.
  46. 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 [Q1; IF 16.497, SNIP 5.62].
  47. 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.” ASME Journal of Mechanical Design, vol. 137, issue 5, pp. 051404-051404, 2015. [Q1; IF 3.441, SNIP 1.44] .
  48. Liu, M., Singh, H. K., and Ray, T., “Application specific instance generator and a memetic algorithm for capacitated arc routing problems” Transportation Research Part C, 2014, vol.43, pp. 249-266, 2014. [Q1, SNIP 3.29, IF 9.022]
  49. 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.[Q1, SNIP 1.8, IF 4.766]. IDEA Code , Oilwell Data
  50. 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. [Q1; IF 16.497, SNIP 5.62] .
  51. 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.[Q1, SNIP 2.64, IF 5.524]
  52. Singh, H. K., Pawar, P. M., Jung, S. N., & Ganguli, R., "On the effect of mass and stiffness unbalance on helicopter tail rotor system behavior." Aircraft Engineering and Aerospace Technology, vol. 80, issue 2, 129-138, 2008.

Book Chapters

  1. Bhattacharjee, K.S., Singh, H.K. 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, vol. 833, pp.17-46, Springer, 2020.
  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.
    3. CCEA-AVP Code
  3. 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

Conference Papers

  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. Saini, B., Shavazipour, B., Singh, H.K., Miettinen, K., “An Efficient Iterative Approach for Uniformly Representing Pareto Fronts,” in Proceedings of International Conference on Evolutionary Multi-Criterion Optimization, (Canberra, Australia), in press, accepted 11/2024.
  5. Sinha, A., Pujara, D., Singh, H.K., “Bilevel Optimization-based Decomposition for Solving Single and Multiobjective Optimization Problems,” in Proceedings of International Conference on Evolutionary Multi-Criterion Optimization, (Canberra, Australia), in press, accepted 11/2024.
  6. 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.
  7. 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.
  8. Sinha, A., Pujara, D. and Singh, H.K., “Decomposition of Difficulties in Complex Optimization Problems Using a Bilevel Approach,” in Proceedings of IEEE Congress on Evolutionary Computation, (Yokohama, Japan), 2024.
  9. 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.
  10. 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 (Aberystwyth, UK), pp. 115-129, 2024.
  11. 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.
  12. Liu, J., Singh, H.K., Elsayed, S., Hunjet, R. and Abbass, H., “Distance Constrained Robotic Swarm Shepherding Based on Two-phase Ant Colony Optimisation" in Proceedings of IEEE Conference on Systems, Man, and Cybernetics, (Maui, USA), Accepted 06/2023.
  13. 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.
  14. 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.
  15. Nguyen, D.T., Singh, H.K., Elsayed, S., Hunjet, R. and Abbass, H., “Multi-Agent Knowledge Transfer in a Society of Interpretable Neural Network Minds for Dynamic Context Formation in Swarm Shepherding,” in Proceedings of IEEE International Joint Conference on Neural Networks, (Gold Coast, Australia), pp. 1-9, 2023.
  16. 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.
  17. 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.
  18. Liu, J., Singh, H.K., Elsayed, S., Hunjet, R. and Abbass, H., “Effective robotic swarm shepherding in the presence of obstacles ,” in Proceedings of IEEE Congress on Evolutionary Computation, (Chicago, USA), pp. 1-8, 2023.
  19. 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.
  20. 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.
  21. Singh, H.K. and Juergen Branke, “Identifying stochastically non-dominated solutions using evolutionary computation,” in Proceedings of Parallel Problem Solving from Nature, (Dortmund, Germany), pp. 193-206, 2022.
  22. 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.
  23. 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.
  24. Debie, E., Singh, H.K., Elsayed, S., Perry, A., Hunjet, R., Abbass, H., “A Neuro-Evolution Approach to Shepherding Swarm Guidance in the Face of Uncertainty" in Proceedings of IEEE International Conference on Systems, Man, and Cybernetics, (Melbourne, Australia), pp. 1-8, 2021.
  25. 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.
  26. 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.
  27. 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.
  28. 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.
  29. 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.
  30. Elsayed, S., Singh, H.K., Debie, E., Perry, A., Campbell, B., Hunjet, R. and Abbass, H., “Path Planning for Shepherding a Swarm in a Cluttered Environment using Differential Evolution,” in IEEE Symposium Series on Computational Intelligence, (Canberra, Asturalia), accepted 09/2020.
  31. 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.
  32. Habib, A., Rahi, K.H., Singh, H.K., 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.
  33. El-fiqi, H., Campbell, B., Elsayed, S., Perry, A., Singh, H.K., Hunjet, R. and Abbass, H., “A preliminary study towards an improved shepherding model,” in Proceedings of the Genetic and Evolutionary Computation Companion, (Cancun, Mexico), pp. 75-76, 2020.
  34. 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.
  35. 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.
  36. 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.
  37. Habib, A., Singh, H.K. 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.
  38. Huynh, Q.N., Singh, H.K. 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.
  39. Chand, S., Singh, H.K. and Ray, T., “Rollout based heuristics for the quantum circuit compilation problem,” in Proceedings of the IEEE Congress on Evolutionary Computation, (Wellington, New Zealand), pp. 951-958, 2019.
  40. Rahi, K.H., Singh, H.K. and Ray, T., “Investigating the use of sequencing and infeasibility driven strategies for constrained optimization,” in Proceedings of the IEEE Congress on Evolutionary Computation, (Wellington, New Zealand), pp. 1643-1650, 2019.
  41. Singh, H.K., Campbell, B., Elsayed, S., Perry, A., Hunjet, R. and Abbass, H., “Modulation of Force Vectors for Effective Shepherding of a Swarm: A Bi-Objective Approach,” in Proceedings of the IEEE Congress on Evolutionary Computation, (Wellington, New Zealand), pp. 2942-2949, 2019.
  42. Bhattacharjee, K.S., Singh, H.K. and Ray, T., “Optimum Wind Farm Layouts: A Many-objective Perspective and Case Study,” In Proceedings of International Conference on Evolutionary Multi-criterion Optimization, (East Lansing, USA), Lecture Notes in Computer Science LNCS 11411, pp. 707-718, 2019.
  43. Lu, Z., Deb, K., and Singh, H.K., “Balancing Survival of Feasible and Infeasible Solutions in Evolutionary Optimization Algorithms,” in Proceedings of the IEEE Congress on Evolutionary Computation, (Rio de Janeiro, Brazil), pp. 282-289, 2018.
  44. 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,” in Proceedings of Australasian Conference on Artificial Intelligence (Wellington, New Zealand), Lecture Notes in Computer Science, vol. 11320, pp. 384-396, Springer, 2018.
  45. Ray, T., Habib, A., Singh, H.K., and Ryan, M., “Uncovering performance envelopes through optimum design of tests,” in Proceedings of Australasian Conference on Artificial Intelligence, (Wellington, New Zealand), Lecture Notes in Computer Science, vol. 11320, pp. 445-457, Springer, 2018.
  46. Islam, M.M., Singh, H.K. 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.
  47. Chand, S., Singh, H.K. 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.
  48. Singh, H.K. and Yao, X., “Improvement of reference points for decomposition based multi-objective evolutionary algorithms,” in Proceedings of Simulated Evolution and Learning, (Shenzhen, China), Lecture Notes in Computer Science, volume 10593, pp. 284-296, 2017.
  49. Bhattacharjee, K.S., Singh, H.K. and Ray, T., “Enhanced Pareto Interpolation Method to Aid Decision Making for Discontinuous Pareto Optimal Fronts,” in Proceedings of Australasian Conference on Artificial Intelligence, (Melbourne, Australia), Lecture Notes in Computer Science (Artificial Intelligence), volume 10400, pp. 93-105, 2017.
  50. Islam, M.M., Singh, H.K. and Ray, T., “Use of a non-nested formulation to improve search for bilevel optimization,” in Proceedings of Australasian Conference on Artificial Intelligence, (Melbourne, Australia), Lecture Notes in Computer Science (Artificial Intelligence), volume 10400, pp. 106-118, 2017.
  51. Habib, A., Singh, H.K. and Ray, T., A Batch Infill Strategy for Computationally Expensive Optimization Problems,” in Proceedings of the Australasian Conference on Artifical Life and Computational Intelligence, (Melbourne, Australia). vol. 10142, pp. 74-85, 2017
  52. 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.
  53. Chand, S., Singh, H.K. 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
  54. Bhattacharjee, K.S., Singh, H.K., 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.
  55. Huynh, Q.N., Singh, H.K. and Ray, T., Improving symbolic regression through a semantics driven framework,” in Proceedings of IEEE Symposium Series on Computational Intelligence,(Athens, Greece, 2016
  56. Habib, A., Singh, H.K. 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), 2016
  57. 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, 2017.
  58. Huynh, Q.N., Singh, H.K. 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.
  59. Asafuddoula, M., Singh, H.K. 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.
  60. Habib, A., Singh, H.K. 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.
  61. Islam, M.M., Singh, H.K. 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.
  62. Huynh, Q.N., Singh, H.K. 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.
  63. Chand, S., Singh, H.K. 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.
  64. Bhattacharjee, K.S., Singh, H.K., 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
  65. Asafuddoula, M., Ray, T., and Singh, H.K. , "Characterizing Pareto Front Approximations in Many-objective Optimization", in Proceedings of the ACM Genetic and Evolutionary Computation (GECCO), Madrid, Spain, pp.607-614, 2015.
  66. Singh, H.K., Alam, K. and Ray, T., "Use of Infeasible Solutions During Constrained Evolutionary Search: A Short Survey", in Proceedings of Australasian Conference on Artificial Life and Computational Intelligence, (Canberra, Australia), Lecture Notes in Artificial Intelligence, vol. 9592, pp 193-205, Springer, 2016.
  67. 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 Australasian Conference on Artificial Life and Computational Intelligence, (Canberra, Australia), Lecture Notes in Artificial Intelligence, vol. 9592, pp 66-77, Springer, 2016.
  68. Islam, M.M., Singh, H.K. and Ray, T., "A Nested Differential Evolution based Algorithm for Solving Multi-objective Bilevel Optimization Problems", in Proceedings of Australasian Conference on Artificial Life and Computational Intelligence, (Canberra, Australia), Lecture Notes in Artificial Intelligence, vol. 9592, pp 101-112, Springer, 2016.
  69. Singh, H.K., Asafuddoula, M., Ray, T. and Alam, K., "Re-design for robustness using many-objective decomposition based evolutionary optimization," in Proceedings of International Conference on Evolutionary Multi-Criteria Optimization (Guimarães, Portugal), Lecture Notes in Computer Science, vol. 9019, pp. 343–357, Springer, 2015.
  70. Islam, M.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 2015, (Sendai, Japan), pp. 1643-1650, 2015.
    71. (Acknowledgement for travel grant: CASS Foundation - http://www.cassfoundation.org/)
  71. 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 2015, (Sendai, Japan), pp. 1129-1136, 2015.
    72. (Acknowledgement for travel grant: CASS Foundation - http://www.cassfoundation.org/)
  72. 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 2015, (Sendai, Japan), pp. 893-899, 2015.
  73. Singh, H.K., 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.
  74. Liu, M., Singh, H.K. , and Ray, T., "A benchmark generator for dynamic capacitated arc routing problems",Proceedings of the IEEE Congress on Evolutionary Computation, Beijing, China, pp. 579-586, 2014.
  75. 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.
  76. 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.
  77. 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.
  78. Singh, H.K., Ray, T. and Smith, W., "Performance of infeasibility empowered memetic algorithm(IEMA) on engineering design problems," in Proceedings of Australasian Conference on Artificial Intelligence (Adelaide, Australia), Lecture Notes in Computer Science, vol. 6464, pp. 425-434, Springer, 2011.IEMA Code
  79. 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.
  80. 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
  81. 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
  82. 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.
  83. Singh, H.K., Isaacs, A., Ray, T., and Smith, W., “An improved secondary ranking for many objective opti¬mization problems,” in Proceedings of ACM Conference on Genetic and Evolutionary Computation Conference, (Montreal, Canada), pp. 1837–1838, 2009.
  84. 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.
  85. 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), Lecture Notes in Computer Science, vol. 5361, pp. 401–410, Springer, 2008.
  86. Singh, H.K., Isaacs, A., Ray, T., and Smith, W., “Infeasibility driven evolutionary algorithm (IDEA) for engineering design optimization,” in Proceedings of Australasian Joint Conference on Artificial Intelligence, (Auckland, New Zealand), Lecture Notes in Artificial Intelligence, vol. 5360, pp. 104–115, Springer, 2008.
  87. 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.
  88. 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.