摘要
针对鲸鱼优化算法在优化复杂工程时易陷入局部最优、收敛精度低等问题,提出一种基于精英反向学习和Levy飞行的鲸鱼优化算法(ELWOA),该算法首先通过精英反向学习优化初始化种群,提高种群的多样性;然后增加自适应权重因子,有利于平衡算法的全局和局部搜索能力;最后将Levy飞行策略应用到鲸鱼优化算法,在最优位置附近进行小范围搜索,有利于算法后期跳出局部最优,提升算法的局部搜索能力。通过对多个测试函数的仿真优化分析,结果表明,ELWOA算法比WOA、MWOA算法具有较快收敛速度和较好的收敛精度。
Aiming at the problems that the whale optimization algorithm is easy to fall into local optimum and low convergence accuracy when optimizing complex engineering,a whale optimization algorithm(ELWOA)based on elite backward learning and Levy flight is proposed,which first optimizes the initialized population through elite backward learning to improve the diversity of the population;then increases the adaptive weight factor,which is beneficial to balance the global and local search ability of the algorithm;finally,the Levy flightstrategy is applied to the whale optimization algorithm to conduct a small search near the optimal position,which is beneficial to the algorithm to jump out of the local optimum later and improve the local search ability of the algorithm.Through the simulation and optimization analysis of several test functions,the results show that the ELWOA algorithm has faster convergence speed and better convergence accuracy than the WOA and MWOA algorithms.
作者
孟宪猛
蔡翠翠
Meng Xianmeng;Cai Cuicui(College of Electronic Engineering,Anhui Xinhua University,Hefei 230088,China;College of Information Engineering,West Anhui University,Lu′an 237012,China)
出处
《电子测量技术》
北大核心
2021年第20期82-87,共6页
Electronic Measurement Technology
基金
安徽高校自然科学基金(KJ2018A0417)
安徽省质量工程(2018ylzy073,2020jyxm2146)项目资助。
关键词
智能优化算法
鲸鱼优化算法
精英反向学习
自适应权重
Levy飞行
intelligent optimization algorithms
whale optimization algorithm
elite inverse learning
adaptive weighting
Levy flight
作者简介
孟宪猛,硕士研究生,高级工程师,主要研究方向为智能信息处理。E-mail:mengxianm@126.com。
