Multi-objective particle swarm optimizers: An experimental comparison. Particle Swarm Optimization (PSO) has received increasing attention in the optimization research community since its first appearance in the mid-1990s. Regarding multi-objective optimization, a considerable number of algorithms based on Multi-Objective Particle Swarm Optimizers (MOPSOs) can be found in the specialized literature. Unfortunately, no experimental comparisons have been made in order to clarify which MOPSO version shows the best performance. In this paper, we use a benchmark composed of three well-known problem families (ZDT, DTLZ, and WFG) with the aim of analyzing the search capabilities of six representative state-of-the-art MOPSOs, namely, NSPSO, SigmaMOPSO, OMOPSO, AMOPSO, MOPSOpd, and CLMOPSO. We additionally propose a new MOPSO algorithm, called SMPSO, characterized by including a velocity constraint mechanism, obtaining promising results where the rest perform inadequately

References in zbMATH (referenced in 27 articles , 1 standard article )

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  1. Kontogiannis, Spyridon G.; Savill, Mark A.: A generalized methodology for multidisciplinary design optimization using surrogate modelling and multifidelity analysis (2020)
  2. Luo, Jianping; Huang, Xiongwen; Yang, Yun; Li, Xia; Wang, Zhenkun; Feng, Jiqiang: A many-objective particle swarm optimizer based on indicator and direction vectors for many-objective optimization (2020)
  3. Qiao, Junfei; Li, Fei; Yang, Shengxiang; Yang, Cuili; Li, Wenjing; Gu, Ke: An adaptive hybrid evolutionary immune multi-objective algorithm based on uniform distribution selection (2020)
  4. Benitez-Hidalgo, A.; Nebro, AJ; Garcia-Nieto, J.; Oregi, I.; Del Ser, J.: jMetalPy: a Python Framework for Multi-Objective Optimization with Metaheuristics (2019) arXiv
  5. He, Zhaoshuang; Chen, Yanhua; Shang, Zhihao; Li, Caihong; Li, Lian; Xu, Mingliang: A novel wind speed forecasting model based on moving window and multi-objective particle swarm optimization algorithm (2019)
  6. Liu, Jianchang; Li, Fei; Kong, Xiangyong; Huang, Peiqiu: Handling many-objective optimisation problems with R2 indicator and decomposition-based particle swarm optimiser (2019)
  7. Santiago, Alejandro; Dorronsoro, Bernabé; Nebro, Antonio J.; Durillo, Juan J.; Castillo, Oscar; Fraire, Héctor J.: A novel multi-objective evolutionary algorithm with fuzzy logic based adaptive selection of operators: FAME (2019)
  8. Pan, Anqi; Wang, Lei; Guo, Weian; Wu, Qidi: A diversity enhanced multiobjective particle swarm optimization (2018)
  9. Patanè, Andrea; Santoro, Andrea; Romano, Vittorio; La Magna, Antonino; Nicosia, Giuseppe: Enhancing quantum efficiency of thin-film silicon solar cells by Pareto optimality (2018)
  10. Liu, Chao; Yan, Bai; Gao, Yang: A new hypervolume-based differential evolution algorithm for many-objective optimization (2017)
  11. Salgueiro, Yamisleydi; Toro, Jorge L.; Bello, Rafael; Falcon, Rafael: Multiobjective variable mesh optimization (2017)
  12. Schlünz, E. B.; Bokov, P. M.; van Vuuren, J. H.: A comparative study on multiobjective metaheuristics for solving constrained in-core fuel management optimisation problems (2016)
  13. Smairi, Nadia; Siarry, Patrick; Ghedira, Khaled: A hybrid particle swarm approach based on tribes and tabu search for multi-objective optimization (2016)
  14. Yang, Zhi; Liu, Cungen; Wang, Xuefeng; Qian, Weixin: An improved multiobjective PSO for the scheduling problem of panel block construction (2016)
  15. Yun, Yeboon; Nakayama, Hirotaka; Yoon, Min: Generation of Pareto optimal solutions using generalized DEA and PSO (2016)
  16. Zhou, Di; Li, Yajun; Jiang, Bin; Wang, Jun: A novel multiobjective quantum-behaved particle swarm optimization based on the ring model (2016)
  17. Knight, Joshua T.; Singer, David J.; Collette, Matthew D.: Testing of a spreading mechanism to promote diversity in multi-objective particle swarm optimization (2015)
  18. Lin, Qiuzhen; Li, Jianqiang; Du, Zhihua; Chen, Jianyong; Ming, Zhong: A novel multi-objective particle swarm optimization with multiple search strategies (2015)
  19. Alves, Maria João; Costa, João Paulo: An algorithm based on particle swarm optimization for multiobjective bilevel linear problems (2014)
  20. Dai, Cai; Wang, Yuping; Ye, Miao: A new evolutionary algorithm based on contraction method for many-objective optimization problems (2014)

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