MOPSO

MOPSO: a proposal for multiple objective particle swarm optimization. This paper introduces a proposal to extend the heuristic called ”particle swarm optimization” (PSO) to deal with multiobjective optimization problems. Our approach uses the concept of Pareto dominance to determine the flight direction of a particle and it maintains previously found nondominated vectors in a global repository that is later used by other particles to guide their own flight. The approach is validated using several standard test functions from the specialized literature. Our results indicate that our approach is highly competitive with current evolutionary multiobjective optimization techniques.


References in zbMATH (referenced in 55 articles )

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  1. Han, Ding; Zheng, Jianrong: A Kriging model-based expensive multiobjective optimization algorithm using R2 indicator of expectation improvement (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. Wang, Chao; Koh, Jin Ming; Yu, Tiantang; Xie, Neng Gang; Cheong, Kang Hao: Material and shape optimization of bi-directional functionally graded plates by GIGA and an improved multi-objective particle swarm optimization algorithm (2020)
  4. Xu, Gang; Luo, Kun; Jing, Guoxiu; Yu, Xiang; Ruan, Xiaojun; Song, Jun: On convergence analysis of multi-objective particle swarm optimization algorithm (2020)
  5. Zhang, XuWei; Liu, Hao; Tu, LiangPing; Zhao, Jian: An efficient multi-objective optimization algorithm based on level swarm optimizer (2020)
  6. Zheng, Wei; Wu, Jianyu; Zhang, Chenghu; Sun, Jianyong: A clustering-based multiobjective evolutionary algorithm for balancing exploration and exploitation (2020)
  7. Chen, Chen; Wei, Yu: Robust multiobjective portfolio optimization: a set order relations approach (2019)
  8. Guerrero-Peña, Elaine; Araújo, Aluízio Fausto Ribeiro: Multi-objective evolutionary algorithm with prediction in the objective space (2019)
  9. Sadouki, Samia Chibani; Tari, Abdelkamel: Multi-objective and discrete elephants herding optimization algorithm for QoS aware web service composition (2019)
  10. Sun, Jian-Qiao; Xiong, Fu-Rui; Schütze, Oliver; Hernández, Carlos: Cell mapping methods. Algorithmic approaches and applications (2019)
  11. Custódio, A. L.; Madeira, J. F. A.: MultiGLODS: global and local multiobjective optimization using direct search (2018)
  12. Duan, Qibin; Kroese, Dirk P.: Splitting for multi-objective optimization (2018)
  13. Duggirala, Aparna; Jana, R. K.; Shesu, R. Venkat; Bhattacharjee, Prasun: Design optimization of deep groove ball bearings using crowding distance particle swarm optimization (2018)
  14. Jiang, Min; Qiu, Liming; Huang, Zhongqiang; Yen, Gary G.: Dynamic multi-objective estimation of distribution algorithm based on domain adaptation and nonparametric estimation (2018)
  15. Luo, Naili; Li, Xia; Lin, Qiuzhen: Objective reduction for many-objective optimization problems using objective subspace extraction (2018)
  16. Wong, C. S. Y.; Al-Dujaili, Abdullah; Suresh, S.; Sundararajan, N.: Pareto-aware strategies for faster convergence in multi-objective multi-scale search optimization (2018)
  17. Coventry, Brandon S.; Parthasarathy, Aravindakshan; Sommer, Alexandra L.; Bartlett, Edward L.: Hierarchical winner-take-all particle swarm optimization social network for neural model fitting (2017)
  18. Halassi, Abdoul-hafar: An attractor-based multiobjective particle swarm optimization (2017)
  19. Jia, Chunhua; Zhu, Hong: An improved multiobjective particle swarm optimization based on culture algorithms (2017)
  20. Jie, Haoxiang; Wu, Yizhong; Zhao, Jianjun; Ding, Jianwan; Liangliang: An efficient multi-objective PSO algorithm assisted by Kriging metamodel for expensive black-box problems (2017)

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