Improved butterfly algorithm based on population expansion and adaptive parameters
Bing Kang, Junjia He, Min Sun, Yi Rong, Shanqi Jiang, Xiangyan Xiao · 2022
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.