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A Novel Simple Particle Swarm Optimization Algorithm for Global Optimization

Author

Listed:
  • Xin Zhang

    (School of Electrical Engineering & Automation, Jiangsu Normal University, Xuzhou 221116, China)

  • Dexuan Zou

    (School of Electrical Engineering & Automation, Jiangsu Normal University, Xuzhou 221116, China)

  • Xin Shen

    (School of Electrical Engineering & Automation, Jiangsu Normal University, Xuzhou 221116, China)

Abstract

In order to overcome the several shortcomings of Particle Swarm Optimization (PSO) e.g., premature convergence, low accuracy and poor global searching ability, a novel Simple Particle Swarm Optimization based on Random weight and Confidence term (SPSORC) is proposed in this paper. The original two improvements of the algorithm are called Simple Particle Swarm Optimization (SPSO) and Simple Particle Swarm Optimization with Confidence term (SPSOC), respectively. The former has the characteristics of more simple structure and faster convergence speed, and the latter increases particle diversity. SPSORC takes into account the advantages of both and enhances exploitation capability of algorithm. Twenty-two benchmark functions and four state-of-the-art improvement strategies are introduced so as to facilitate more fair comparison. In addition, a t -test is used to analyze the differences in large amounts of data. The stability and the search efficiency of algorithms are evaluated by comparing the success rates and the average iteration times obtained from 50-dimensional benchmark functions. The results show that the SPSO and its improved algorithms perform well comparing with several kinds of improved PSO algorithms according to both search time and computing accuracy. SPSORC, in particular, is more competent for the optimization of complex problems. In all, it has more desirable convergence, stronger stability and higher accuracy.

Suggested Citation

  • Xin Zhang & Dexuan Zou & Xin Shen, 2018. "A Novel Simple Particle Swarm Optimization Algorithm for Global Optimization," Mathematics, MDPI, vol. 6(12), pages 1-34, November.
    • Handle: RePEc:gam:jmathe:v:6:y:2018:i:12:p:287-:d:185927
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    References listed on IDEAS

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    2. Chen, Syuan-Yi & Wu, Chien-Hsun & Hung, Yi-Hsuan & Chung, Cheng-Ta, 2018. "Optimal strategies of energy management integrated with transmission control for a hybrid electric vehicle using dynamic particle swarm optimization," Energy, Elsevier, vol. 160(C), pages 154-170.
    3. Zahra Pooranian & Mohammad Shojafar & Jemal H. Abawajy & Ajith Abraham, 2015. "An efficient meta-heuristic algorithm for grid computing," Journal of Combinatorial Optimization, Springer, vol. 30(3), pages 413-434, October.
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    Cited by:

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    2. Ümit Işıkdağ & Gebrail Bekdaş & Yaren Aydın & Sudi Apak & Junhee Hong & Zong Woo Geem, 2024. "Adaptive Neural Architecture Search Using Meta-Heuristics: Discovering Fine-Tuned Predictive Models for Photocatalytic CO 2 Reduction," Sustainability, MDPI, vol. 16(23), pages 1-29, December.
    3. Zhou, Wenhao & Li, Hailin & Zhang, Zhiwei, 2022. "A novel seasonal fractional grey model for predicting electricity demand: A case study of Zhejiang in China," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 200(C), pages 128-147.
    4. Qiang Yang & Yufei Jing & Xudong Gao & Dongdong Xu & Zhenyu Lu & Sang-Woon Jeon & Jun Zhang, 2022. "Predominant Cognitive Learning Particle Swarm Optimization for Global Numerical Optimization," Mathematics, MDPI, vol. 10(10), pages 1-35, May.
    5. Laith Abualigah & Ali Diabat, 2023. "Improved multi-core arithmetic optimization algorithm-based ensemble mutation for multidisciplinary applications," Journal of Intelligent Manufacturing, Springer, vol. 34(4), pages 1833-1874, April.

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