Improved butterfly algorithm based on population expansion and adaptive parameters

Bing Kang; Junjia He; Min Sun; Yi Rong; Shanqi Jiang; Xiangyan Xiao

doi:10.1117/12.2639542

27 June 2022 Improved butterfly algorithm based on population expansion and adaptive parameters

Bing Kang, Junjia He, Min Sun, Yi Rong, Shanqi Jiang, Xiangyan Xiao

Proceedings Volume 12253, International Conference on Automation Control, Algorithm, and Intelligent Bionics (ACAIB 2022); 122530W (2022) https://doi.org/10.1117/12.2639542
Event: Second International Conference on Automation Control, Algorithm, and Intelligent Bionics (ACAIB 2022), 2022, Qingdao, China

Abstract

To address the problems of slow convergence process and low accuracy of final convergence results in butterfly optimization algorithm (BOA), a symmetric augmentation optimized population and parameter dynamic adaptive butterfly algorithm (PEAPBOA) is proposed, which optimizes the initial population by symmetric augmentation combined with dominant population method to improve the speed of algorithm convergence, and then crosses and mutates the individuals with poor adaptation. The algorithm further introduces dynamically changing sensory modality c, power exponent a, dynamic switching probability p and position updating dynamic weights w1 and w2, so that the algorithm focuses on global search in the early stage and local search in the later stage, which improves the breadth of global search and the depth of local random search of the algorithm, thus improving the convergence speed and convergence accuracy of the algorithm. The test experiment verifies the effectiveness and superiority of the improved butterfly algorithm proposed in this paper.

Citation Download Citation

Bing Kang, Junjia He, Min Sun, Yi Rong, Shanqi Jiang, and Xiangyan Xiao "Improved butterfly algorithm based on population expansion and adaptive parameters", Proc. SPIE 12253, International Conference on Automation Control, Algorithm, and Intelligent Bionics (ACAIB 2022), 122530W (27 June 2022); https://doi.org/10.1117/12.2639542

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