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Swarm-Intelligence Optimization Method for Dynamic Optimization Problem

Author

Listed:
  • Rui Liu

    (School of Electronic Information, Guangxi Minzu University, Nanning 530006, China)

  • Yuanbin Mo

    (Guangxi Key Laboratory of Hybrid Computation and IC Design Analysis, Guangxi Minzu University, Nanning 530006, China
    Institute of Artificial Intelligence, Guangxi Minzu University, Nanning 530006, China)

  • Yanyue Lu

    (School of Chemistry and Chemical Engineering, Guangxi Minzu University, Nanning 530006, China)

  • Yucheng Lyu

    (Institute of Artificial Intelligence, Guangxi Minzu University, Nanning 530006, China)

  • Yuedong Zhang

    (School of Electronic Information, Guangxi Minzu University, Nanning 530006, China)

  • Haidong Guo

    (Institute of Artificial Intelligence, Guangxi Minzu University, Nanning 530006, China)

Abstract

In recent years, the vigorous rise in computational intelligence has opened up new research ideas for solving chemical dynamic optimization problems, making the application of swarm-intelligence optimization techniques more and more widespread. However, the potential for algorithms with different performances still needs to be further investigated in this context. On this premise, this paper puts forward a universal swarm-intelligence dynamic optimization framework, which transforms the infinite-dimensional dynamic optimization problem into the finite-dimensional nonlinear programming problem through control variable parameterization. In order to improve the efficiency and accuracy of dynamic optimization, an improved version of the multi-strategy enhanced sparrow search algorithm is proposed from the application side, including good-point set initialization, hybrid algorithm strategy, Lévy flight mechanism, and Student’s t -distribution model. The resulting augmented algorithm is theoretically tested on ten benchmark functions, and compared with the whale optimization algorithm, marine predators algorithm, harris hawks optimization, social group optimization, and the basic sparrow search algorithm, statistical results verify that the improved algorithm has advantages in most tests. Finally, the six algorithms are further applied to three typical dynamic optimization problems under a universal swarm-intelligence dynamic optimization framework. The proposed algorithm achieves optimal results and has higher accuracy than methods in other references.

Suggested Citation

  • Rui Liu & Yuanbin Mo & Yanyue Lu & Yucheng Lyu & Yuedong Zhang & Haidong Guo, 2022. "Swarm-Intelligence Optimization Method for Dynamic Optimization Problem," Mathematics, MDPI, vol. 10(11), pages 1-28, May.
    • Handle: RePEc:gam:jmathe:v:10:y:2022:i:11:p:1803-:d:823582
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    References listed on IDEAS

    as
    1. D. Brockmann & L. Hufnagel & T. Geisel, 2006. "The scaling laws of human travel," Nature, Nature, vol. 439(7075), pages 462-465, January.
    2. Qi Xiong & Xinman Zhang & Shaobo He & Jun Shen, 2021. "A Fractional-Order Chaotic Sparrow Search Algorithm for Enhancement of Long Distance Iris Image," Mathematics, MDPI, vol. 9(21), pages 1-17, November.
    3. Li Shi & Xuehong Ding & Min Li & Yuan Liu & Muhammad Ahmad, 2021. "Research on the Capability Maturity Evaluation of Intelligent Manufacturing Based on Firefly Algorithm, Sparrow Search Algorithm, and BP Neural Network," Complexity, Hindawi, vol. 2021, pages 1-26, August.
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