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Modified Sand Cat Swarm Optimization Algorithm for Solving Constrained Engineering Optimization Problems

Author

Listed:
  • Di Wu

    (School of Education and Music, Sanming University, Sanming 365004, China)

  • Honghua Rao

    (School of Information Engineering, Sanming University, Sanming 365004, China)

  • Changsheng Wen

    (School of Information Engineering, Sanming University, Sanming 365004, China)

  • Heming Jia

    (School of Information Engineering, Sanming University, Sanming 365004, China)

  • Qingxin Liu

    (School of Computer Science and Technology, Hainan University, Haikou 570228, China)

  • Laith Abualigah

    (Hourani Center for Applied Scientific Research, Al-Ahliyya Amman University, Amman 19328, Jordan
    Faculty of Information Technology, Middle East University, Amman 11831, Jordan
    Faculty of Information Technology, Applied Science Private University, Amman 11931, Jordan
    School of Computer Sciences, Universiti Sains Malaysia, Pulau Pinang 11800, Malaysia)

Abstract

The sand cat swarm optimization algorithm (SCSO) is a recently proposed metaheuristic optimization algorithm. It stimulates the hunting behavior of the sand cat, which attacks or searches for prey according to the sound frequency; each sand cat aims to catch better prey. Therefore, the sand cat will search for a better location to catch better prey. In the SCSO algorithm, each sand cat will gradually approach its prey, which makes the algorithm a strong exploitation ability. However, in the later stage of the SCSO algorithm, each sand cat is prone to fall into the local optimum, making it unable to find a better position. In order to improve the mobility of the sand cat and the exploration ability of the algorithm. In this paper, a modified sand cat swarm optimization (MSCSO) algorithm is proposed. The MSCSO algorithm adds a wandering strategy. When attacking or searching for prey, the sand cat will walk to find a better position. The MSCSO algorithm with a wandering strategy enhances the mobility of the sand cat and makes the algorithm have stronger global exploration ability. After that, the lens opposition-based learning strategy is added to enhance the global property of the algorithm so that the algorithm can converge faster. To evaluate the optimization effect of the MSCSO algorithm, we used 23 standard benchmark functions and CEC2014 benchmark functions to evaluate the optimization performance of the MSCSO algorithm. In the experiment, we analyzed the data statistics, convergence curve, Wilcoxon rank sum test, and box graph. Experiments show that the MSCSO algorithm with a walking strategy and a lens position-based learning strategy had a stronger exploration ability. Finally, the MSCSO algorithm was used to test seven engineering problems, which also verified the engineering practicability of the proposed algorithm.

Suggested Citation

  • Di Wu & Honghua Rao & Changsheng Wen & Heming Jia & Qingxin Liu & Laith Abualigah, 2022. "Modified Sand Cat Swarm Optimization Algorithm for Solving Constrained Engineering Optimization Problems," Mathematics, MDPI, vol. 10(22), pages 1-41, November.
  • Handle: RePEc:gam:jmathe:v:10:y:2022:i:22:p:4350-:d:977802
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    References listed on IDEAS

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    1. Shuang Wang & Abdelazim G. Hussien & Heming Jia & Laith Abualigah & Rong Zheng, 2022. "Enhanced Remora Optimization Algorithm for Solving Constrained Engineering Optimization Problems," Mathematics, MDPI, vol. 10(10), pages 1-32, May.
    2. Honghua Rao & Heming Jia & Di Wu & Changsheng Wen & Shanglong Li & Qingxin Liu & Laith Abualigah, 2022. "A Modified Group Teaching Optimization Algorithm for Solving Constrained Engineering Optimization Problems," Mathematics, MDPI, vol. 10(20), pages 1-36, October.
    3. Changsheng Wen & Heming Jia & Di Wu & Honghua Rao & Shanglong Li & Qingxin Liu & Laith Abualigah, 2022. "Modified Remora Optimization Algorithm with Multistrategies for Global Optimization Problem," Mathematics, MDPI, vol. 10(19), pages 1-36, October.
    4. Qingxin Liu & Ni Li & Heming Jia & Qi Qi & Laith Abualigah, 2022. "Modified Remora Optimization Algorithm for Global Optimization and Multilevel Thresholding Image Segmentation," Mathematics, MDPI, vol. 10(7), pages 1-42, March.
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    Cited by:

    1. Jinhua You & Heming Jia & Di Wu & Honghua Rao & Changsheng Wen & Qingxin Liu & Laith Abualigah, 2023. "Modified Artificial Gorilla Troop Optimization Algorithm for Solving Constrained Engineering Optimization Problems," Mathematics, MDPI, vol. 11(5), pages 1-42, March.
    2. Farzad Kiani & Sajjad Nematzadeh & Fateme Aysin Anka & Mine Afacan Findikli, 2023. "Chaotic Sand Cat Swarm Optimization," Mathematics, MDPI, vol. 11(10), pages 1-47, May.

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