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.
  • Y. Nan, K. Shang, H. Ishibuchi and L. He, “Reverse Strategy for Non-Dominated Archiving,” IEEE Access, vol. 8, pp. 119458-119469, Jun. 2020. [Link]
  • W. Hong, K. Tang, A. Zhou, H. Ishibuchi, and X. Yao, “A scalable indicator-based evolutionary algorithm for large-scale multi-objective optimization,” IEEE Trans. on Evolutionary Computation, vol. 23, no. 3, pp. 525-537, June 2019.
  • Z. Wang, Y. S. Ong, and H. Ishibuchi, “On scalable multiobjective test problems with hardly-dominated boundaries,” IEEE Trans. on Evolutionary Computation, vol. 23, no. 2, pp. 217-231, April 2019.
  • H. Ishibuchi, R. Imada, Y. Setoguchi, and Y. Nojima, “Reference point specification in inverted generational distance for triangular linear Pareto front,” IEEE Trans. on Evolutionary Computation, vol. 22, no. 6, pp. 961-975, December 2018.
  • R. Tanabe and H. Ishibuchi, “An analysis of control parameters of MOEA/D under two different optimization scenarios,” Applied Soft Computing, vol. 70, pp. 22-40, September 2018.
  • H. Zille, H. Ishibuchi, S. Mostaghim and Y. Nojima, “A framework for large-scale multi-objective optimization based on problem transformation,” IEEE Trans. on Evolutionary Computation, vol. 22, no. 2, pp. 260-275, April 2018.
  • R. Tanabe, H. Ishibuchi, and A. Oyama, “Benchmarking multi- and many-objective evolutionary algorithms under two optimization scenarios,” IEEE Access, vol. 5, pp. 19597-19619, December 2017.
  • Ishibuchi, Y. Setoguchi, H. Masuda, and Y. Nojima, “Performance of decomposition-based many-objective algorithms strongly depends on Pareto front shapes,” IEEE Trans. on Evolutionary Computation, vol. 21, no.2, pp. 169-190, April 2017.
  • Wang, Q. Zhang, H. Li, H. Ishibuchi, and L. Jiao, “On the use of two reference points in decomposition based multiobjective evolutionary algorithms,” Swarm and Evolutionary Computation, vol. 34, pp. 89-102, June 2017.
  • Gu, F.-L. Chung, H. Ishibuchi and S. Wang, “Imbalanced TSK fuzzy classifier by cross-class Bayesian fuzzy clustering and imbalance learning,” IEEE Transactions on Systems, Man, and Cybernetics: Systems, vol. 47, no. 8, pp. 2005-2020, August 2017.
  • Wang, J. Xiong, H. Ishibuchi, G. Wu, and T. Zhang, “On the effect of reference point in MOEA/D for multi-objective optimization,” Applied Soft Computing, vol. 58, pp. 25-34, September 2017.
  • Tanabe, H. Ishibuchi, and A. Oyama, “Benchmarking multi- and many-objective evolutionary algorithms under two optimization scenarios,” IEEE Access, vol. 5, pp. 19597-19619, Dec 2017.
  • Ishibuchi, K. Doi, and Y. Nojima, “On the effect of normalization in MOEA/D for multi-objective and many-objective optimization,” Complex & Intelligent Systems, vol. 3, no. 4, pp. 279–294, Dec 2017.
  • Zhou, H. Ishibuchi, and S. Wang, "Stacked-structure-based hierarchical Takagi-Sugeno-Kang fuzzy classification through feature augmentation," IEEE Trans. on Emerging Topics in Computational Intelligence, vol. 1, no. 6, pp. 421-436, December 2017.
  • Wang, Z. Zhou, H. Ishibuchi, T. Liao, and T. Zhang, “Localized weighted sum method for many-objective optimization,” IEEE Trans. on Evolutionary Computation(Accepted: Online Available as an Early Access paper).
  • Zille, H. Ishibuchi, S. Mostaghim and Y. Nojima, “A framework for large-scale multi-objective optimization based on problem transformation,” IEEE Trans. on Evolutionary Computation(Accepted).
  • Zhang, H. Ishibuchi and S. Wang, “Deep Takagi-Sugeno-Kang fuzzy classifier with shared linguistic fuzzy rules,” IEEE Trans. on Fuzzy Systems(Accepted)
  • Chica, R. Chiong, M. Kirley, and H. Ishibuchi, “A networked N-player trust game and its evolutionary dynamics,” IEEE Trans. on Evolutionary Computation(Accepted).
  • Ishibuchi, R. Imada, Y. Setoguchi, and Yusuke Nojima, “Reference point specification in inverted generational distance for triangular linear Pareto front,” IEEE Trans. on Evolutionary Computation(Accepted).

Conference Paper:

  • 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]
  • 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]
  • 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]
  • 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]
  • H. Ishibuchi, R. Imada, N. Masuyama, and Y. Nojima, “Two-layered weight vector specification in decomposition-based multi-objective algorithms for many-objective optimization problems,” Proc. of 2019 IEEE Congress on Evolutionary Computation, pp. 2435-2442, Wellington, New Zealand, June 10-13, 2019.
  • H. Ishibuchi, Y. Peng, and K. Shang, “A scalable multimodal multiobjective test problem,” Proc. of 2019 IEEE Congress on Evolutionary Computation, pp. 302-309, Wellington, New Zealand, June 10-13, 2019. Best Runner-Up Paper Award
  • K. Shang, H. Ishibuchi, M-L. Zhang and Y. Liu, “A new R2 indicator for better hypervolume approximation,” Proc. of 2018 Genetic and Evolutionary Computation Conference (GECCO 2018), pp. 745-752, Kyoto, Japan, July 15-19, 2018. Best Paper Award
  • Ishibuchi, R. Imada, Y. Setoguchi, and Y. Nojima, “Hypervolume subset selection for triangular and inverted triangular Pareto fronts of three-objective problems,” Proc. of 14th ACM/SIGEVO Conferenceon Foundations of Genetic Algorithms (FOGA 2017), pp. 95-110, Copenhagen, Denmark, January 12-15, 2017.
  • Tanigaki, Y. Nojima, and H. Ishibuchi, “Performance comparison of EMO algorithms on test problems with different search space shape,” Proc. of Joint 17th World Congress of International Fuzzy Systems Association and 9th International Conference on Soft Computing and Intelligent Systems, 6 pages, Otsu, Japan, June 27-30, 2017. Student Best Paper Award
  • Nojima, S. Takemura, K. Watanabe, and H. Ishibuchi, “Michigan-style fuzzy GBML with (1+1)-ES generation update and multi-pattern rule generation,” Proc. of Joint 17th World Congress of International Fuzzy Systems Association and 9th International Conference on Soft Computing and Intelligent Systems, 6 pages, Otsu, Japan, June 27-30, 2017.
  • Nojima, K. Arahari, S. Takemura, and H. Ishibuchi, “Multiobjective fuzzy genetics-based machine learning based on MOEA/D with its modifications,” Proc. of 2017 IEEE International Conference on Fuzzy Systems, 6 pages, Naples, Italy, July 9-12, 2017.
  • B. Nguyen, B. Xue, H. Ishibuchi, P. Andreae, and M. Zhang, “Multiple reference points MOEA/D for feature selection,” Companion of 2017 Genetic and Evolutionary Computation Conference, pp. 157-158, Berlin, Germany, July 15-19, 2017.
  • Nojima, Y. Tanigaki, and H. Ishibuchi, “Multiobjective data mining from solutions by evolutionary multiobjective optimization,” Proc. of 2017 Genetic and Evolutionary Computation Conference, pp. 617-624, Berlin, Germany, July 15-19, 2017.
  • Ishibuchi, R. Imada, Y. Setoguchi, and Y. Nojima, “Reference point specification in hypervolume calculation for fair comparison and efficient search,” Proc. of 2017 Genetic and Evolutionary Computation Conference, pp. 585-592, Berlin, Germany, July 15-19, 2017. Best Paper Award
  • Chen, R. Qu, and H. Ishibuchi, “Variable-depth adaptive large neighbourhood search algorithm for open periodic vehicle routing problem with time windows,” Proc. of International Workshop on Harbour, Maritime & Multimodal Logistics Modelling and Simulation(HMS 2017), Barcelona, Spain, September 18-20, 2017. (11 pages)
  • Ishibuchi, R. Imada, K. Doi, and Y. Nojima, “Use of inverted triangular weight vectors in decomposition-based multiobjective algorithms,” Proc. of 2017 IEEE International Conference on Systems, Man, and Cybernetics, pp. 373-378, Banff, Canada, October 5-8, 2017.
  • Gao, Y. Nojima, and H. Ishibuchi, “Multi-objective GAssist with NSGA-II,” Proc. of 18th International Symposium on Advanced Intelligent Systems, pp. 696-703, Deagu, Republic of Korea, October 11-14, 2017.
  • Doi, R. Imada, Y. Nojima, and H. Ishibuchi, “Use of inverted triangular weight vectors in decomposition-based many-objective algorithms,” Proc. of 11th International Conference on Simulated Evolution and Learning, pp. 321-333, Shenzhen, China, Nov. 10-13, 2017.

 

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