The improved brown-bear optimization algorithm based on multi-strategy
LIU Tianbao
LIU Xuewei
Abstract：Brown-bear optimization algorithm(BOA)has the advantages of high computational efficiency and no specific parameters,but BOA has some problems such as low accuracy,local optimal and insufficient population diversity.In order to improve its performance,an improved brown-bear optimization algorithm(IBOA)is proposed.Firstly,in the initialization phase,a chaotic mapping strategy is proposed to enhance the diversity of the initial population.Secondly,Gaussian mutation is introduced into the pedal scent marking behavior to enhance the search ability of the algorithm and increase the probability of the algorithm jumping out of the local optimal solution.Finally,the quadratic interpolation strategy is used to generate new brown bear individuals,and the greedy strategy is used to update the local optimal solution to improve the accuracy of population calculation.The experimental results show that the multi-strategy enhanced brown-bear optimization algorithm can effectively enhance the optimization ability of the algorithm,and has certain advantages compared with other algorithms.
Keywords：brown-bear optimization algorithmlogistic-sine-cosine mapGaussian mutationquadratic interpolation
Publication Date：2025-03-31
Online Publishing Date：2025-08-15(First online date of this platform, not the publication date of the document)
Pages：7( 38-44 )