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An opposition-based butterfly optimization algorithm with adaptive elite mutation in solving complex high-dimensional optimization problems

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  • Li, Yu
  • Yu, Xiaomei
  • Liu, Jingsen

Abstract

To solve complex high-dimensional optimization problems, an opposition-based butterfly optimization algorithm with adaptive elite mutation (OBOAEM) is proposed. In the initial stage, the opposition-based learning mechanism is introduced to increase the diversity of the initial population and improve the probability of finding the optimal value. In order to balance the process of global search and local search, the segmental adjustment factor is used to improve the optimization accuracy of the algorithm. In the final stage of the algorithm, the elite mutation strategy is adopted to prevent precocity of the algorithm. In this paper, 23 benchmark functions are selected to test OBOAEM and original algorithm BOA in low dimension, and 16 benchmark functions are introduced to test OBOAEM and eight intelligent optimization algorithms in high dimensions 100, 500 and 1000. In addition, the simulation experiments of 30 CEC2014 complex deformation functions reveal the OBOAEM has a good effect in solving complex optimization problems, which are compared with six state-of-art algorithms. Friedman test is used forstatistical analysis, showing that OBOAEM has better optimization performance for complex high-dimensional problems. Finally, OBOAEM is applied to engineering design problems, and it is proved that OBOAEM is competitive in solving real-world problems.

Suggested Citation

  • Li, Yu & Yu, Xiaomei & Liu, Jingsen, 2023. "An opposition-based butterfly optimization algorithm with adaptive elite mutation in solving complex high-dimensional optimization problems," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 204(C), pages 498-528.
  • Handle: RePEc:eee:matcom:v:204:y:2023:i:c:p:498-528
    DOI: 10.1016/j.matcom.2022.08.020
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    Cited by:

    1. Feilong Chang & Fahui Yuan & Zhixiong Lu, 2023. "A Multi-Objective Optimization Method for a Tractor Driveline Based on the Diversity Preservation Strategy of Gradient Crowding," Agriculture, MDPI, vol. 13(7), pages 1-16, June.
    2. Ye, Wenwen & Li, Shengping, 2023. "Convergence analysis of flow direction algorithm in continuous search space and its improvement," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 212(C), pages 91-121.
    3. Pan, Jeng-Shyang & Zhang, Zhen & Chu, Shu-Chuan & Zhang, Si-Qi & Wu, Jimmy Ming-Tai, 2024. "A parallel compact Marine Predators Algorithm applied in time series prediction of Backpropagation neural network (BNN) and engineering optimization," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 220(C), pages 65-88.

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