信息工程大学学报2025，Vol.26Issue(5)：548-553,560,7.DOI:10.3969/j.issn.1671-0673.2025.05.007
基于混合策略的苦鱼优化算法
Bitterling Fish Optimization Algorithm Based on Hybrid Strategy
摘要
Abstract
Aiming at the problems of slow convergence speed and easy to fall into local optimum in the basic bitterling fish optimization(BFO)algorithm,a bitterling fish optimization based on hybrid strat-egy(HSBFO)algorithm is proposed.Firstly,the Tent-Logistic-Cosine chaotic mapping is used to ini-tialize the population quality.Secondly,the double-sided mirror reflection theory is introduced to deal with the cross-border individuals and solve the problem of uneven population distribution.Finally,the Gaussian-Cauchy difference strategy is utilized to enhance the algorithm's ability to escape from local optima.The HSBFO algorithm is compared with particle swarm optimization(PSO)algorithm,whale optimization algorithm(WOA),seagull optimization algorithm(STOA),sine cosine algorithm(SCA)and basic BFO algorithm to optimize nine benchmark test functions.The experimental results show that the HSBFO algorithm has better optimization accuracy than the other four optimization algorithms.The HSBFO algorithm is applied to the cantilever beam design problem,and the experimental results show that the performance of the HSBFO algorithm is better than the basic BFO algorithm in engineering op-timization,which verifies the feasibility of the HSBFO algorithm in dealing with practical engineering problems.关键词
混合策略/苦鱼优化算法/Tent-Logistic-Cosine混沌映射/双面镜反射理论/高斯柯西差分策略/悬臂梁设计问题Key words
hybrid strategy/bitterling fish optimization algorithm/Tent-Logistic-Cosine chaotic map-ping/double-sided mirror reflection theory/Gaussian-Cauchy differential strategy/cantilever beam design problem分类
计算机与自动化引用本文复制引用
潘博阳..基于混合策略的苦鱼优化算法[J].信息工程大学学报,2025,26(5):548-553,560,7.
