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A novel multi-objective particle swarm optimization with multiple search strategies

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  • Lin, Qiuzhen
  • Li, Jianqiang
  • Du, Zhihua
  • Chen, Jianyong
  • Ming, Zhong

Abstract

Recently, multi-objective particle swarm optimization (MOPSO) has shown the effectiveness in solving multi-objective optimization problems (MOPs). However, most MOPSO algorithms only adopt a single search strategy to update the velocity of each particle, which may cause some difficulties when tackling complex MOPs. This paper proposes a novel MOPSO algorithm using multiple search strategies (MMOPSO), where decomposition approach is exploited for transforming MOPs into a set of aggregation problems and then each particle is assigned accordingly to optimize each aggregation problem. Two search strategies are designed to update the velocity of each particle, which is respectively beneficial for the acceleration of convergence speed and the keeping of population diversity. After that, all the non-dominated solutions visited by the particles are preserved in an external archive, where evolutionary search strategy is further performed to exchange useful information among them. These multiple search strategies enable MMOPSO to handle various kinds of MOPs very well. When compared with some MOPSO algorithms and two state-of-the-art evolutionary algorithms, simulation results show that MMOPSO performs better on most of test problems.

Suggested Citation

  • Lin, Qiuzhen & Li, Jianqiang & Du, Zhihua & Chen, Jianyong & Ming, Zhong, 2015. "A novel multi-objective particle swarm optimization with multiple search strategies," European Journal of Operational Research, Elsevier, vol. 247(3), pages 732-744.
    • Handle: RePEc:eee:ejores:v:247:y:2015:i:3:p:732-744
    DOI: 10.1016/j.ejor.2015.06.071
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    References listed on IDEAS

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    3. Fei Han & Yu-Wen-Tian Sun & Qing-Hua Ling, 2018. "An Improved Multiobjective Quantum-Behaved Particle Swarm Optimization Based on Double Search Strategy and Circular Transposon Mechanism," Complexity, Hindawi, vol. 2018, pages 1-22, November.
    4. Yong Wang & Jiayi Zhe & Xiuwen Wang & Yaoyao Sun & Haizhong Wang, 2022. "Collaborative Multidepot Vehicle Routing Problem with Dynamic Customer Demands and Time Windows," Sustainability, MDPI, vol. 14(11), pages 1-37, May.
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    6. Xu, Gang & Luo, Kun & Jing, Guoxiu & Yu, Xiang & Ruan, Xiaojun & Song, Jun, 2020. "On convergence analysis of multi-objective particle swarm optimization algorithm," European Journal of Operational Research, Elsevier, vol. 286(1), pages 32-38.
    7. Capitanescu, F. & Marvuglia, A. & Benetto, E. & Ahmadi, A. & Tiruta-Barna, L., 2017. "Linear programming-based directed local search for expensive multi-objective optimization problems: Application to drinking water production plants," European Journal of Operational Research, Elsevier, vol. 262(1), pages 322-334.
    8. Farshad Rezaei & Hamid R. Safavi & Maryam Zekri, 2017. "A Hybrid Fuzzy-Based Multi-Objective PSO Algorithm for Conjunctive Water Use and Optimal Multi-Crop Pattern Planning," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 31(4), pages 1139-1155, March.
    9. Zhang, XuWei & Liu, Hao & Tu, LiangPing & Zhao, Jian, 2020. "An efficient multi-objective optimization algorithm based on level swarm optimizer," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 177(C), pages 588-602.
    10. Zouache, Djaafar & Moussaoui, Abdelouahab & Ben Abdelaziz, Fouad, 2018. "A cooperative swarm intelligence algorithm for multi-objective discrete optimization with application to the knapsack problem," European Journal of Operational Research, Elsevier, vol. 264(1), pages 74-88.
    11. Ma, Xuemin & Yang, Jingming & Sun, Hao & Hu, Ziyu & Wei, Lixin, 2021. "Feature information prediction algorithm for dynamic multi-objective optimization problems," European Journal of Operational Research, Elsevier, vol. 295(3), pages 965-981.
    12. Ying Sun & Yuelin Gao, 2019. "A Multi-Objective Particle Swarm Optimization Algorithm Based on Gaussian Mutation and an Improved Learning Strategy," Mathematics, MDPI, vol. 7(2), pages 1-16, February.
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