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Chaotic Multi-Objective Particle Swarm Optimization Algorithm Incorporating Clone Immunity

Author

Listed:
  • Ying Sun

    (School of Computer Science and Information Engineering, Hefei University of Technology, Hefei 230009, China)

  • Yuelin Gao

    (Ningxia Province Key Laboratory of Intelligent Information and Data Processing, North Minzu University, Yinchuan 750021, China)

  • Xudong Shi

    (School of Mathematics and Statistics, Ningxia University, Yinchuan 750021, China)

Abstract

It is generally known that the balance between convergence and diversity is a key issue for solving multi-objective optimization problems. Thus, a chaotic multi-objective particle swarm optimization approach incorporating clone immunity (CICMOPSO) is proposed in this paper. First, points in a non-dominated solution set are mapped to a parallel-cell coordinate system. Then, the status of the particles is evaluated by the Pareto entropy and difference entropy. At the same time, the algorithm parameters are adjusted by feedback information. At the late stage of the algorithm, the local-search ability of the particle swarm still needs to be improved. Logistic mapping and the neighboring immune operator are used to maintain and change the external archive. Experimental test results show that the convergence and diversity of the algorithm are improved.

Suggested Citation

  • Ying Sun & Yuelin Gao & Xudong Shi, 2019. "Chaotic Multi-Objective Particle Swarm Optimization Algorithm Incorporating Clone Immunity," Mathematics, MDPI, vol. 7(2), pages 1-16, February.
    • Handle: RePEc:gam:jmathe:v:7:y:2019:i:2:p:146-:d:203331
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    References listed on IDEAS

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    Cited by:

    1. Ricardo Fitas & Heinz Joachim Schaffrath & Samuel Schabel, 2023. "A Review of Optimization for Corrugated Boards," Sustainability, MDPI, vol. 15(21), pages 1-27, November.
    2. Yildirim, Gokce & Tanyildizi, Erkan, 2023. "An innovative approach based on optimization for the determination of initial conditions of continuous-time chaotic system as a random number generator," Chaos, Solitons & Fractals, Elsevier, vol. 172(C).
    3. Johan M. Bogoya & Andrés Vargas & Oliver Schütze, 2019. "The Averaged Hausdorff Distances in Multi-Objective Optimization: A Review," Mathematics, MDPI, vol. 7(10), pages 1-35, September.

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