一种基于灰狼策略的大白鲨混合优化算法  

A White Shark Hybrid Optimization Algorithm based on the Grey Wolf Strategy

作　　者：刘新忠 丁鹏达 曾慧林 黄逸桓 聂础辉 周晨明 谢文武 LIU Xinzhong;DING Pengda;ZENG Huilin;HUANG Yiheng;NIE Chuhui;ZHOU Chenming;XIE Wenwu(School of Information Science and Engineering,Hunan Institute of Science and Technology,Yueyang 414006,China)

机构地区：[1]湖南理工学院信息科学与工程学院,湖南岳阳414006

出　　处：《成都工业学院学报》2023年第4期60-66,共7页Journal of Chengdu Technological University

基　　金：湖南省自然科学基金项目(2023JJ50045);湖南省教育厅项目(20C0879);国家大学生创新创业计划训练项目(20221265800X);湖南省大学生创新创业计划训练项目(S202212658015);湖南省研究生科研创新项目(QL20220248)。

摘　　要：大白鲨优化算法可以有效解决一些连续搜索空间的优化问题,但存在易于陷入局部最优解和收敛速度慢等问题。提出的混合灰狼策略的大白鲨优化算法,通过结合灰狼优化算法中头狼的围剿策略,改进大白鲨优化算法中白鲨的围剿方案,可以获得更佳的局部开发能力。在大白鲨优化算法中的声音定位部分,通过引入变异的方式增加随机性,以解决局部最优问题。通过对算法进行Benchmark测试,并与粒子群、灰狼优化、白鲨优化算法进行比较,实验结果表明该算法在局部开发能力、收敛速度等方面有较好提升。The White Shark Optimization algorithm can effectively solve some continuous search space optimization problems,but there are problems such as easy falling into the local optimal solution and slow convergence speed.The Great White Shark Optimization algorithm with a mixed Grey Wolf strategy proposed in this paper was to use the leader wolf s encirclement and suppression strategy in the Grey Wolf Optimization algorithm to improve the the White Shark s encirclement and suppression strategy in the Great White Shark Optimization algorithm.This new strategy can get better local development capabilities.In the sound localization part of the White Shark Optimization algorithm,the randomness was increased by introducing variation to solve the local optimal problem.Through the Benchmark test of the algorithm,and compared with particle swarm optimization,Gray Wolf Optimization,and White Shark Optimization algorithms,the experimental results show that the algorithm has a good improvement in local development ability,convergence speed,and other aspects.

关 键 词：灰狼策略 大白鲨优化算法 参数优化 搜索机制 粒子群算法 

分 类 号：TP18[自动化与计算机技术—控制理论与控制工程]