鲸鱼优化算法研究与应用进展  

Research and application of whale optimization algorithm

作　　者：王颍超 WANG Ying-chao(School of Cyberspace Science and Technology,Beijing Institute of Technology,Beijing 100081,China)

机构地区：[1]北京理工大学网络空间安全学院,北京100081

出　　处：《计算机工程与科学》2024年第5期881-896,共16页Computer Engineering & Science

基　　金：国家重点研发计划(2021YFB1715700)。

摘　　要：鲸鱼优化算法WOA是一种根据概率收敛的新型群体智能优化算法,具有原理简单易实现、参数量少易设置和全局与局部开发分别控制易平衡等特点。系统地分析WOA的基本原理和算法性能影响因素,重点论述现有的算法改进策略和算法混合策略的优点及局限性,并阐述了WOA在支持向量机、人工神经网络、组合优化和复杂函数优化等方面的应用与发展。最后,结合WOA的特点及其应用成果,对WOA的发展方向进行了展望。The Whale Optimization Algorithm(WOA)is a novel swarm intelligence optimization algorithm that converges based on probability.It features simple and easily implementable algorithm principles,a small number of easily adjustable parameters,and a balance between global and local search control.This paper systematically analyzes the basic principles of WOA and factors influencing algorithm performance.It focuses on discussing the advantages and limitations of existing algorithm improvement strategies and hybrid strategies.Additionally,the paper elaborates on the applications and developments of WOA in support vector machines,artificial neural networks,combinatorial optimization,complex function optimization,and other areas.Finally,considering the characteristics of WOA and its research achievements in applications,the paper provides a prospective outlook on the research and development directions of WOA.

关 键 词：鲸鱼优化算法 群体智能 搜索机制 改进策略 

分 类 号：TP301.6[自动化与计算机技术—计算机系统结构]