一种基于自适应策略的混合鲸鱼优化算法

A Hybrid Whale Optimization Algorithm Based on Adaptive Strategy

针对鲸鱼优化算法在函数优化时存在收敛速度慢、易陷入局部最优等问题,提出了一种基于自适应策略的混合鲸鱼优化算法。该算法中先利用蝙蝠算法的局部搜索机制对当前鲸鱼算法最优解进行高斯扰动产生局部新解,再把局部新解放入蝙蝠种群中,根据局部新解的位置优劣更新鲸鱼群位置,达到增强种群多样性、避免过早陷入局部最优的目的;然后利用自适应策略改善平衡算法的全局寻优和局部寻优;最后通过优化鲸鱼搜索路径提升搜索精度。实验仿真结果表明:本文提出的算法的总体寻优性能优于狼群算法、蝗虫算法、标准粒子群算法和标准鲸鱼算法。

In this paper,a hybrid whale optimization algorithm based on adaptive strategy is proposed to solve the problem of slow convergence speed and easy falling into local optimum of whale optimization algorithm in function optimization.Firstly,the local search mechanism of bat algorithm is employed to perform a Gaussian perturbation on the current optimal solution of whale algorithm to generate a new local solution,which is released into the bat population.Then,the location of whale groups is updated on the basis of the location of the new local solution to enhance population diversity and avoid falling into a local optimum prematurely.Secondly,an adaptive strategy is adopted to improve the global and local optimization of balancing algorithm.Finally,the search accuracy of the algorithm is improved by whale search path optimization.The experimental simulation results show that the overall optimization performance of the proposed algorithm is better than that of wolf swarm algorithm,locust algorithm,standard particle swarm algorithm and standard whale algorithm.