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A Hybrid PSO-DE Intelligent Algorithm for Solving Constrained Optimization Problems Based on Feasibility Rules

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  • Eryang Guo

    (School of Mathematics and Information Sciences, North Minzu University, Yinchuan 750021, China
    Ningxia Province Cooperative Innovation Center of Scientific Computing and Intelligent Information Processing, North Minzu University, Yinchuan 750021, China)

  • Yuelin Gao

    (School of Mathematics and Information Sciences, North Minzu University, Yinchuan 750021, China
    Ningxia Province Cooperative Innovation Center of Scientific Computing and Intelligent Information Processing, North Minzu University, Yinchuan 750021, China
    Ningxia Province Key Laboratory of Intelligent Information and Data Processing, North Minzu University, Yinchuan 750021, China)

  • Chenyang Hu

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

  • Jiaojiao Zhang

    (School of Mathematics and Information Sciences, North Minzu University, Yinchuan 750021, China
    Ningxia Province Cooperative Innovation Center of Scientific Computing and Intelligent Information Processing, North Minzu University, Yinchuan 750021, China)

Abstract

In this paper, we study swarm intelligence computation for constrained optimization problems and propose a new hybrid PSO-DE algorithm based on feasibility rules. Establishing individual feasibility rules as a way to determine whether the position of an individual satisfies the constraint or violates the degree of the constraint, which will determine the choice of the individual optimal position and the global optimal position in the particle population. First, particle swarm optimization (PSO) is used to act on the top 50% of individuals with higher degree of constraint violation to update their velocity and position. Second, Differential Evolution (DE) is applied to act on the individual optimal position of each individual to form a new population. The current individual optimal position and the global optimal position are updated using the feasibility rules, thus forming a hybrid PSO-DE intelligent algorithm. Analyzing the convergence and complexity of PSO-DE. Finally, the performance of the PSO-DE algorithm is tested with 12 benchmark functions of constrained optimization and 57 engineering optimization problems, the numerical results show that the proposed algorithm has good accuracy, effectiveness and robustness.

Suggested Citation

  • Eryang Guo & Yuelin Gao & Chenyang Hu & Jiaojiao Zhang, 2023. "A Hybrid PSO-DE Intelligent Algorithm for Solving Constrained Optimization Problems Based on Feasibility Rules," Mathematics, MDPI, vol. 11(3), pages 1-34, January.
    • Handle: RePEc:gam:jmathe:v:11:y:2023:i:3:p:522-:d:1040111
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    References listed on IDEAS

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    3. Minggang Dong & Ning Wang & Xiaohui Cheng & Chuanxian Jiang, 2014. "Composite Differential Evolution with Modified Oracle Penalty Method for Constrained Optimization Problems," Mathematical Problems in Engineering, Hindawi, vol. 2014, pages 1-15, October.
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