IDEAS home Printed from https://ideas.repec.org/a/gam/jmathe/v7y2019i2p148-d203512.html
   My bibliography  Save this article

A Multi-Objective Particle Swarm Optimization Algorithm Based on Gaussian Mutation and an Improved Learning Strategy

Author

Listed:
  • Ying Sun

    (School of Computer Science and Information Engineering, Hefei University of Technology, Hefei 230009, China
    These authors contributed equally to this work.)

  • Yuelin Gao

    (School of Computer Science and Information Engineering, Hefei University of Technology, Hefei 230009, China
    Ningxia Province Key Laboratory of Intelligent Information and Data Processing, North Minzu University, Yinchuan 750021, China
    These authors contributed equally to this work.)

Abstract

Obtaining high convergence and uniform distributions remains a major challenge in most metaheuristic multi-objective optimization problems. In this article, a novel multi-objective particle swarm optimization (PSO) algorithm is proposed based on Gaussian mutation and an improved learning strategy. The approach adopts a Gaussian mutation strategy to improve the uniformity of external archives and current populations. To improve the global optimal solution, different learning strategies are proposed for non-dominated and dominated solutions. An indicator is presented to measure the distribution width of the non-dominated solution set, which is produced by various algorithms. Experiments were performed using eight benchmark test functions. The results illustrate that the multi-objective improved PSO algorithm (MOIPSO) yields better convergence and distributions than the other two algorithms, and the distance width indicator is reasonable and effective.

Suggested Citation

  • 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.
    • Handle: RePEc:gam:jmathe:v:7:y:2019:i:2:p:148-:d:203512
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2227-7390/7/2/148/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2227-7390/7/2/148/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. 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.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Alfredo Alcayde & Raul Baños & Francisco M. Arrabal-Campos & Francisco G. Montoya, 2019. "Optimization of the Contracted Electric Power by Means of Genetic Algorithms," Energies, MDPI, vol. 12(7), pages 1-13, April.
    2. Liu, Ruochen & Li, Jianxia & fan, Jing & Mu, Caihong & Jiao, Licheng, 2017. "A coevolutionary technique based on multi-swarm particle swarm optimization for dynamic multi-objective optimization," European Journal of Operational Research, Elsevier, vol. 261(3), pages 1028-1051.
    3. 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.
    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.
    5. Yu, Shiwei & Zheng, Shuhong & Gao, Shiwei & Yang, Juan, 2017. "A multi-objective decision model for investment in energy savings and emission reductions in coal mining," European Journal of Operational Research, Elsevier, vol. 260(1), pages 335-347.
    6. 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.
    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. 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.
    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. Qi You & Jun Sun & Feng Pan & Vasile Palade & Bilal Ahmad, 2021. "DMO-QPSO: A Multi-Objective Quantum-Behaved Particle Swarm Optimization Algorithm Based on Decomposition with Diversity Control," Mathematics, MDPI, vol. 9(16), pages 1-20, August.
    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. 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.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jmathe:v:7:y:2019:i:2:p:148-:d:203512. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.