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 22 articles , 1 standard article )

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  1. 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)
  2. 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)
  3. Pan, Anqi; Wang, Lei; Guo, Weian; Wu, Qidi: A diversity enhanced multiobjective particle swarm optimization (2018)
  4. Patanè, Andrea; Santoro, Andrea; Romano, Vittorio; La Magna, Antonino; Nicosia, Giuseppe: Enhancing quantum efficiency of thin-film silicon solar cells by Pareto optimality (2018)
  5. Liu, Chao; Yan, Bai; Gao, Yang: A new hypervolume-based differential evolution algorithm for many-objective optimization (2017)
  6. Salgueiro, Yamisleydi; Toro, Jorge L.; Bello, Rafael; Falcon, Rafael: Multiobjective variable mesh optimization (2017)
  7. 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)
  8. Smairi, Nadia; Siarry, Patrick; Ghedira, Khaled: A hybrid particle swarm approach based on tribes and tabu search for multi-objective optimization (2016)
  9. Yang, Zhi; Liu, Cungen; Wang, Xuefeng; Qian, Weixin: An improved multiobjective PSO for the scheduling problem of panel block construction (2016)
  10. Yun, Yeboon; Nakayama, Hirotaka; Yoon, Min: Generation of Pareto optimal solutions using generalized DEA and PSO (2016)
  11. Zhou, Di; Li, Yajun; Jiang, Bin; Wang, Jun: A novel multiobjective quantum-behaved particle swarm optimization based on the ring model (2016)
  12. Knight, Joshua T.; Singer, David J.; Collette, Matthew D.: Testing of a spreading mechanism to promote diversity in multi-objective particle swarm optimization (2015)
  13. Lin, Qiuzhen; Li, Jianqiang; Du, Zhihua; Chen, Jianyong; Ming, Zhong: A novel multi-objective particle swarm optimization with multiple search strategies (2015)
  14. Alves, Maria João; Costa, João Paulo: An algorithm based on particle swarm optimization for multiobjective bilevel linear problems (2014)
  15. Dai, Cai; Wang, Yuping; Ye, Miao: A new evolutionary algorithm based on contraction method for many-objective optimization problems (2014)
  16. He, Xiaoguang; Dai, Cai; Chen, Zehua: Many-objective optimization using adaptive differential evolution with a new ranking method (2014)
  17. Wang, Yan; Zeng, Jian-chao: A multi-objective artificial physics optimization algorithm based on ranks of individuals (2013) ioport
  18. Yun, Yeboon; Nakayama, Hirotaka: Utilizing expected improvement and generalized data envelopment analysis in multi-objective genetic algorithms (2013)
  19. Chakraborty, Prithwish; Das, Swagatam; Roy, Gourab Ghosh; Abraham, Ajith: On convergence of the multi-objective particle swarm optimizers (2011)
  20. Durillo, J. J.; Nebro, A. J.; Luna, F.; Coello, C. A. Coello; Alba, E.: Convergence speed in multi-objective metaheuristics: efficiency criteria and empirical study (2010)

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