Publications

Journal:

  • L. M. Pang, H. Ishibuchi*, L. He, K. Shang, and L. Chen, “Hypervolume-based cooperative coevolution with two reference points for multi-objective optimization,” IEEE Trans. on Evolutionary Computation (Accepted: Early Access)
  • Y. Liu, L. Xu, Y. Han, X. Zeng, G. G. Yen*, and H. Ishibuchi, “Evolutionary multimodal multiobjective optimization for traveling salesman problems,” IEEE Trans. on Evolutionary Computation (Accepted: Early Access)
  • K. Shang, T. Shu, and H. Ishibuchi*, “Learning to approximate: Auto direction vector set generation for hypervolume contribution approximation,” IEEE Trans. on Evolutionary Computation (Accepted: Early Access)
  • Y. Nan, K. Shang, H. Ishibuchi*, and L. He, “An improved local search method for large-scale hypervolume subset selection,” IEEE Trans. on Evolutionary Computation (Accepted: Early Access)
  • J. Zhang, L. He, and H. Ishibuchi*, “Dual fuzzy classifier-based evolutionary algorithm for expensive multiobjective optimization,” IEEE Trans. on Evolutionary Computation (Accepted: Early Access)
  • L. M. Pang, H. Ishibuchi*, and K. Shang, “Use of two penalty values in multi-objective evolutionary algorithm based on decomposition,” IEEE Trans. on Cybernetics，vol. 53, no. 11, pp. 7174-7186, November 2023
  • L. He, K. Shang, Y. Nan, H. Ishibuchi*, and Dipti Srinivasan*, “Relation between objective space normalization and weight vector scaling in decomposition-based multi-objective evolutionary algorithms,” IEEE Trans. on Evolutionary Computation, vol. 27, no. 5, pp. 1177-1191, October 2023.
  • K. Shang, W. Chen, W. Liao, and H. Ishibuchi*, “HV-Net: Hypervolume approximation based on DeepSets,” IEEE Trans. on Evolutionary Computation, vol. 27, no. 4, pp. 1154-1160, August 2023.
  • T. Shu, K. Shang*, H. Ishibuchi*, and Y. Nan, “Effects of archive size on computation time and solution quality for multi-objective optimization,” IEEE Trans. on Evolutionary Computation vol. 27, no. 4, pp. 1145-1153, August 2023.
  • N. Masuyama, Y. Nojima, C. K. Loo, and H. Ishibuchi*, “Multi-label classification via adaptive resonance theory-based clustering,” IEEE Trans. on Pattern Analysis and Machine Intelligence, vol. 45, no. 7, pp. 8696-8712, July 2023.
  • L. He, A. Camacho, Y. Nan, A. Trivedi, H. Ishibuchi*, and Dipti Srinivasan, “Effects of corner weight vectors on the performance of decomposition-based multiobjective algorithms,” Swarm and Evolutionary Computation, Volume 79, June 2023.
  • K. Shang, T. Shu, H. Ishibuchi*, Y. Nan, and L. M. Pang, “Benchmarking large-scale subset selection in evolutionary multi-objective optimization,” Information Sciences, vol. 622, pp. 755-770, April 2023.
  • Y. Wang, H. Ishibuchi*, M. J. Erd, J. Zhua*, “Unsupervised multilayer fuzzy neural networks for image clustering,” Information Sciences, vol. 622, pp. 682-709, April 2023.
  • L. M. Pang, H. Ishibuchi*, and K. Shang, “Counterintuitive experimental results in evolutionary large-scale multi-objective optimization,” IEEE Trans. on Evolutionary Computation, vol. 26, no. 6, pp. 1609-1616, December, 2022.
  • M. Ming, R. Wang, H. Ishibuchi*, and T. Zhang*, “A novel dual-stage dual-population evolutionary algorithm for constrained multi-objective optimization,” IEEE Trans. on Evolutionary Computation, vol. 26, no. 5, pp. 1129-1143, October, 2022.
  • Y. Peng and H. Ishibuchi*, “A diversity-enhanced subset selection framework for multi-modal multi-objective optimization,” IEEE Trans. on Evolutionary Computation, vol. 26, no. 5, pp. 886-900, October, 2022.
  • W. Chen, H. Ishibuchi*, and K. Shang, “Fast greedy subset selection from large candidate solution sets in evolutionary multi-objective optimization,” IEEE Trans. on Evolutionary Computation, vol. 26, no. 4, pp. 750-764, August 2022.
  • J. Zhang, H. Ishibuchi*, and L. He, “A classification-assisted environmental selection strategy for multiobjective optimization,” Swarm and Evolutionary Computation, vol. 71, Paper ID: 101074, June 2022.
  • K. Shang, H. Ishibuchi*, W. Chen, Y. Nan, and W. Liao, “Hypervolume-optimal μ-distributions on line/plane-based Pareto fronts in three dimensions,” IEEE Trans. on Evolutionary Computation, vol. 26, no. 2, pp. 349-363, April 2022.
  • H. Ishibuchi*, L. M. Pang, and K. Shang, “Difficulties in fair performance comparison of multi-objective evolutionary algorithms,” IEEE Computational Intelligence Magazine , vol. 17, no. 1, pp. 86-101, February 2022.
  • L. He, H. Ishibuchi*, A. Trivedi, H. Wang, Y. Nan, and D. Srinivasan, “A survey of normalization methods in multiobjective evolutionary algorithms,” IEEE Trans. on Evolutionary Computation, vol. 25, no. 6, pp. 1028-1048, December 2021.
  • T. Zhang, Z. Deng, H. Ishibuchi*, and L. M. Pang, “Robust TSK fuzzy system based on semi-supervised learning for label noise data,” IEEE Trans. on Fuzzy Systems, vol. 29, no. 8, pp. 2145-2157, August 2021.
  • J. G. Falcón-Cardona, H. Ishibuchi*, C. A. C. Coello, and M. Emmerich, “On the effect of the cooperation of indicator-based multi-objective evolutionary algorithms,” IEEE Trans. on Evolutionary Computation, vol. 25, no. 4, pp. 681-695, August 2021.
  • K. Shang, H. Ishibuchi, L. He, and L. M. Pang, “A survey on the hypervolume indicator in evolutionary multi-objective optimization,” IEEE Trans. on Evolutionary Computation, vol. 25, no. 1, pp. 1-20, February 2021.

Conference Paper:

  • W. Liao, Y. Wei, M. Jiang, Q. Zhang, and H. Ishibuchi, “Does continual learning meet compositionality? New benchmarks and an evaluation framework,” Proc. of Thirty-seventh Conference on Neural Information Processing Systems Datasets and Benchmarks Track (NeurIPS 2023) New Orleans, USA, December 10-16, 2023.
  • C. Gong, Y. Nan, L. M. Pang, H. Ishibuchi, and Q. Zhang, “Examination of the multimodal nature of multi-objective neural architecture search,” Proc. of 2023 IEEE Symposium Series on Computational Intelligence (IEEE SSCI 2023), pp. 1821-1828, Mexico City, Mexico, December 5-8, 2023.
  • G. Wu, T. Shu, Y. Nan, K. Shang, and H. Ishibuchi, “Ensemble R2-based hypervolume contribution approximation,” Proc. of 2023 IEEE Symposium Series on Computational Intelligence (IEEE SSCI 2023), pp. 1503-1510, Mexico City, Mexico, December 5-8, 2023.
  • C. Gong, Y. Nan, L. M. Pang, H. Ishibuchi, and Q. Zhang, “Initial populations with a few heuristic solutions significantly improve evolutionary multi-objective combinatorial optimization,” Proc. of 2023 IEEE Symposium Series on Computational Intelligence (IEEE SSCI 2023), pp. 1398-1405, Mexico City, Mexico, December 5-8, 2023.
  • J. Zhang, H. Ishibuchi, L. He, and Y. Nan, “Effects of initialization methods on the performance of surrogate-based multiobjective evolutionary algorithms,” Proc. of 2023 IEEE Symposium Series on Computational Intelligence (IEEE SSCI 2023), pp. 933-940, Mexico City, Mexico, December 5-8, 2023.
  • G. Wu, T. Shu, K. Shang, and H. Ishibuchi, “Normalization in R2-based hypervolume and hypervolume contribution approximation,” Proc. of 2023 IEEE Symposium Series on Computational Intelligence (IEEE SSCI 2023), pp. 449-456, Mexico City, Mexico, December 5-8, 2023.
  • T. Shu, Y. Nan, K. Shang, and H. Ishibuchi, “Analysis of partition methods for dominated solution removal from large solution sets,” Proc. of 2023 IEEE Symposium Series on Computational Intelligence (IEEE SSCI 2023), pp. 441-448, Mexico City, Mexico, December 5-8, 2023.
  • K. Shang, T. Shu, G. Wu, Y. Nan, L. M. Pang, and H. Ishibuchi, “Empirical hypervolume optimal μ-distributions on complex Pareto fronts,” Proc. of 2023 IEEE Symposium Series on Computational Intelligence (IEEE SSCI 2023), pp. 433-440, Mexico City, Mexico, December 5-8, 2023.
  • Y. Nan, T. Shu, and H. Ishibuchi, “Two-stage lazy greedy inclusion hypervolume subset selection for large-scale problem,” Proc. of 2023 IEEE International Conference on Systems, Man, and Cybernetics (IEEE SMC 2023), pp. 1154-1161, Maui, Hawaii, USA, October 1-4, 2023.
  • L. M. Pang, Y. Nan, and H. Ishibuchi, “How to find a large solution set to cover the entire Pareto front in evolutionary multi-objective optimization,” Proc. of 2023 IEEE International Conference on Systems, Man, and Cybernetics (IEEE SMC 2023), pp. 1188-1194, Maui, Hawaii, USA, October 1-4, 2023.
  • C. Gong, L. M. Pang, Y. Nan, H. Ishibuchi, and Q. Zhang, “Effects of initialization methods on the performance of multi-objective evolutionary algorithms,” Proc. of 2023 IEEE International Conference on Systems, Man, and Cybernetics (IEEE SMC 2023), pp. 1168-1175, Maui, Hawaii, USA, October 1-4, 2023.
  • H. Zhu, K. Shang, and H. Ishibuchi, “STHV-Net: Hypervolume approximation based on set transformer,” Proc. of 2023 Genetic and Evolutionary Computation Conference (GECCO 2023), pp. 804-812, Lisbon, Portugal, July 15-19, 2023.
  • T. Shu, Y. Nan, K. Shang, and H. Ishibuchi, “Two-phase procedure for efficiently removing dominated solutions from large solution sets,” Proc. of 2023 Genetic and Evolutionary Computation Conference (GECCO 2023), pp. 740-748, Lisbon, Portugal, July 15-19, 2023.
  • H. Ishibuchi, L. M. Pang, and K. Shang, “Effects of dominance modification on hypervolume-based and IGD-based performance evaluation results of NSGA-II,” Proc. of 2023 Genetic and Evolutionary Computation Conference (GECCO 2023), pp. 679-687, Lisbon, Portugal, July 15-19, 2023.
  • L. He, Y. Nan, H. Ishibuchi, and D. Srinivasan, “Effects of objective space normalization in multi-objective evolutionary algorithms on real-world problems,” Proc. of 2023 Genetic and Evolutionary Computation Conference (GECCO 2023), pp. 670-678, Lisbon, Portugal, July 15-19, 2023.
  • C. Gong, Y. Nan, L. M. Pang, H. Ishibuchi, and Q. Zhang, “Effects of including optimal solutions in the initial population on evolutionary multi-objective optimization,” Proc. of 2023 Genetic and Evolutionary Computation Conference (GECCO 2023), pp. 661-669, Lisbon, Portugal, July 15-19, 2023.
  • G. An, Z. Wu, Z. Shen, K. Shang, and H. Ishibuchi, “Evolutionary multi-objective deep reinforcement learning for autonomous UAV navigation in large-scale complex environments,” Proc. of 2023 Genetic and Evolutionary Computation Conference (GECCO 2023), pp. 633-641, Lisbon, Portugal, July 15-19, 2023.
  • Y. Nan, T. Shu, and H. Ishibuchi, “Effects of external archives on the performance of multi-objective evolutionary algorithms on real-world problems,” Proc. of 2023 IEEE Congress on Evolutionary Computation (IEEE CEC 2023), Chicago, USA, July 1-5, 2023.
  • L. He, Y. Nan, H. Ishibuchi, and D. Srinivasan, “Preference-based nonlinear normalization for multiobjective optimization,” Proc. of 12th International Conference on Evolutionary Multi-Criterion Optimization (EMO 2023), pp. 563-577, Leiden, Netherland, March 20-24, 2023.
  • Y. Nan, H. Ishibuchi, T. Shu, and K. Shang, “Two-stage greedy approximated hypervolume subset selection for large-scale problems,” Proc. of 12th International Conference on Evolutionary Multi-Criterion Optimization (EMO 2023), pp. 391-404, Leiden, Netherland, March 20-24, 2023.
  • H. Ishibuchi, Y. Nan, and L. M. Pang, “Performance evaluation of multi-objective evolutionary algorithms using artificial and real-world problems,” Proc. of 12th International Conference on Evolutionary Multi-Criterion Optimization (EMO 2023), pp. 333-347, Leiden, Netherland, March 20-24, 2023.
  • L. M. Pang, Y. Nan, and H. Ishibuchi, “Partially degenerate multi-objective test problems,” Proc. of 12th International Conference on Evolutionary Multi-Criterion Optimization (EMO 2023), pp. 277-290, Leiden, Netherland, March 20-24, 2023.
  • J. Zhang, L. He, and H. Ishibuchi, “An improved fuzzy classifier-based evolutionary algorithm for expensive multiobjective optimization problems with complicated Pareto sets,” Proc. of 12th International Conference on Evolutionary Multi-Criterion Optimization (EMO 2023), pp. 231-246, Leiden, Netherland, March 20-24, 2023.
  • L. Chen, L. M. Pang and H. Ishibuchi, "New Solution Creation Operator in MOEA/D for Faster Convergence," Proc. of International Conference on Parallel Problem Solving from Nature (PPSN), 2022. [Pdf]
  • Y. Peng and H. Ishibuchi, "Dynamic Multi-modal Multi-objective Optimization: A Preliminary Study," Proc. of International Conference on Parallel Problem Solving from Nature (PPSN), 2022. [Pdf]
  • T. Shu, K. Shang, Y. Nan and H. Ishibuchi, "Direction Vector Selection for R2-Based Hypervolume Contribution Approximation," Proc. of International Conference on Parallel Problem Solving from Nature (PPSN), 2022. [Pdf]
  • K. Shang, W. Liao and H. Ishibuchi, "HVC-Net: Deep Learning Based Hypervolume Contribution Approximation," Proc. of International Conference on Parallel Problem Solving from Nature (PPSN), 2022. [Pdf]
  • H. Ishibuchi, Y. Peng, and L. M. Pang, “Multi-modal multi-objective test problems with an infinite number of equivalent Pareto sets,” Proc. of 2022 IEEE Congress on Evolutionary Computation (IEEE CEC 2022), pp. 1-8, Padua, Italy, July 18-23, 2022.
  • L. Chen, L. M. Pang, H. Ishibuchi*, and K. Shang, “Periodical weight vector update using an unbounded external archive for decomposition-based evolutionary multi-objective optimization,” Proc. of 2021 IEEE Symposium Series on Computational Intelligence (IEEE SSCI 2021), pp. 1-8, Orlando, Florida, USA, December 5-7, 2021.
  • C. Gong, L. M. Pang, and H. Ishibuchi*, “Initial population generation method and its effects on MOEA/D,” Proc. of 2021 IEEE Symposium Series on Computational Intelligence (IEEE SSCI 2021), pp. 1-8, Orlando, Florida, USA, December 5-7, 2021.
  • Y. Nan, K. Shang, H. Ishibuchi, and L. He, “Improving local search hypervolume subset selection in evolutionary multi-objective optimization,” Proc. of 2021 IEEE International Conference on Systems, Man, and Cybernetics (IEEE SMC 2021), pp. 751-757, Melbourne, Australia, October 17-20, 2021.
  • L. M. Pang, K. Shang, L. Chen, H. Ishibuchi, and W. Chen, “Proposal of a new test problem for large-scale multi- and many-objective optimization,” Proc. of 2021 IEEE International Conference on Systems, Man, and Cybernetics (IEEE SMC 2021), pp. 4108-4121, Melbourne, Australia, October 17-20, 2021.
  • K. Shang, H. Ishibuchi, L. M. Pang, and Y. Nan, “Reference point specification for greedy hypervolume subset selection,” Proc. of 2021 IEEE International Conference on Systems, Man, and Cybernetics (IEEE SMC 2021), pp. 168-175, Melbourne, Australia, October 17-20, 2021.
  • Y. Peng and H. Ishibuchi, “A decomposition-based hybrid evolutionary algorithm for multi-modal multi-objective optimization,” Proc. of 2021 IEEE International Conference on Systems, Man, and Cybernetics (IEEE SMC 2021), pp. 160-167, Melbourne, Australia, October 17-20, 2021.
  • W. Chen, H. Ishibuchi, and K. Shang, “Clustering-based subset selection in evolutionary multiobjective optimization,” Proc. of 2021 IEEE International Conference on Systems, Man, and Cybernetics (IEEE SMC 2021), pp. 468-475, Melbourne, Australia, October 17-20, 2021.
  • Y. Liu, L. Xu, Y. Han, N. Masuyama, Y. Nojima, H. Ishibuchi, and G. G. Yen, “Multi-modal multi-objective traveling salesman problem and its evolutionary optimizer”, Proc. of 2021 IEEE International Conference on Systems, Man, and Cybernetics (IEEE SMC 2021), pp. 770-777, Melbourne, Australia, October 17-20, 2021.
  • Y. Wang, H. Ishibuchi, J. Zhu, Y. Wang, and T. Dai, “Unsupervised fuzzy neural network for image clustering,” Proc. of 2021 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE 2021), pp. 1-6, Luxembourg, July 11-14, 2021.
  • H. Ishibuchi and H. Sato, “Evolutionary many-objective optimization,” 2021 IEEE Congress on Evolutionary Computation (IEEE CEC 2021), Kraków, Poland, June 28 - July 1, 2021. (Tutorial Talk)
  • Y. Nan, K. Shang, H. Ishibuchi, and L. He, “A two-stage hypervolume contribution approximation method based on R2 indicator,” Proc. of 2021 IEEE Congress on Evolutionary Computation (IEEE CEC 2021), pp. 2468-2475, Kraków, Poland, June 28 - July 1, 2021.
  • L. M. Pang, H. Ishibuchi, and K. Shang, “Using a genetic algorithm-based hyper-heuristic to tune MOEA/D for a set of various test problems,” Proc. of 2021 IEEE Congress on Evolutionary Computation (IEEE CEC 2021), pp. 1486-1494, Kraków, Poland, June 28 - July 1, 2021.
  • L. Chen, L. M. Pang, H. Ishibuchi, and K. Shang, “Periodical generation update using an unbounded external archive for multi-objective optimization,” Proc. of 2021 IEEE Congress on Evolutionary Computation (IEEE CEC 2021), pp. 1912-1920, Kraków, Poland, June 28 - July 1, 2021.
  • K. Shang, H. Ishibuchi, and W. Chen, “Greedy approximated hypervolume subset selection for many-objective optimization,” Proc. of 2021 Genetic and Evolutionary Computation Conference (GECCO 2021), pp. 448-456, Lille, France, July 10-14, 2021. (Best Paper Award)
  • L. He, H. Ishibuchi, and D. Srinivasan, “Metric for evaluating normalization methods in multiobjective optimization,” Proc. of 2021 Genetic and Evolutionary Computation Conference (GECCO 2021), pp. 403-411, Lille, France, July 10-14, 2021.
  • J. Zhang, H. Ishibuchi, K. Shang, L. He, L. M. Pang, and Y. Peng, “Environmental selection using a fuzzy classifier for multiobjective evolutionary algorithms,” Proc. of 2021 Genetic and Evolutionary Computation Conference (GECCO 2021), pp. 485-492, Lille, France, July 10-14, 2021.
  • K. Shang, H. Ishibuchi, and Y. Nan, “Distance-based subset selection revisited,” Proc. of 2021 Genetic and Evolutionary Computation Conference (GECCO 2021), pp. 439-447, Lille, France, July 10-14, 2021.
  • J. Zhang and H. Ishibuchi, “Multiobjective optimization with fuzzy classification-assisted environmental selection,” Proc. of 11th International Conference on Evolutionary Multi-Criterion Optimization (EMO 2021), pp. 580-592, Shenzhen, China, March 28-31, 2021.
  • Y. Peng and H. Ishibuchi, “Niching diversity estimation for multi-modal multi-objective optimization,” Proc. of 11th International Conference on Evolutionary Multi-Criterion Optimization (EMO 2021), pp. 323-334, Shenzhen, China, March 28-31, 2021.
  • L. M. Pang, H. Ishibuchi, and K. Shang, “Using a genetic algorithm-based hyper-heuristic to tune MOEA/D for a set of benchmark test problems,” Proc. of 11th International Conference on Evolutionary Multi-Criterion Optimization (EMO 2021), pp. 164-177, Shenzhen, China, March 28-31, 2021.
  • K. Shang, H. Ishibuchi, L. Chen, W. Chen, and L. M. Pang, “Improving the efficiency of R2HCA-EMOA,” Proc. of 11th International Conference on Evolutionary Multi-Criterion Optimization (EMO 2021), pp. 115-125, Shenzhen, China, March 28-31, 2021.
  • Q. Yang, Z. Wang, and H. Ishibuchi, “It is hard to distinguish between dominance resistant solutions and extremely convex Pareto optimal solutions,” Proc. of 11th International Conference on Evolutionary Multi-Criterion Optimization (EMO 2021), pp. 3-14, Shenzhen, China, March 28-31, 2021.

 

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