辽宁工程技术大学学报（自然科学版）2023，Vol.42Issue(6)：722-732,11.DOI:10.11956/j.issn.1008-0562.2023.06.012
多策略改进的麻雀搜索算法
Multi-strategy improved sparrow search algorithm
摘要
Abstract
Aiming at the problems that the sparrow search algorithm tends to fall into local optimization during iterative convergence,a multi-strategy improved sparrow search algorithm(NLSSA)is proposed.Firstly,the neighborhood center of gravity reverse learning strategy is used to optimize the initial population of the sparrow algorithm and improve the initial individual quality,and then,the long and short distance jump of the Levy flight strategy is used to update the sparrow producer position,thereby improving the local extremum escape ability of the sparrow algorithm,and finally,the adaptive weight is introduced in the follower position update mechanism,so as to balance the local mining and global optimization ability of the sparrow algorithm.In order to verify the performance of the proposed NLSSA algorithm,this paper uses eight benchmark functions to test,and the test results and the Wilcoxon symbolic rank test show that the NLSSA algorithm has higher search accuracy,stability performance and convergence speed than sparrow search algorithm,particle swarm algorithm,grey wolf optimizer and other improved sparrow search algorithms.关键词
麻雀搜索算法/邻域重心反向学习/Levy飞行策略/自适应权重/基准函数Key words
sparrow search algorithm/neighborhood center of gravity reverse learning/Levy flight strategy/adaptive weights/benchmark function分类
数理科学引用本文复制引用
回立川,李瑶,李欢欢,于淼,王久阳..多策略改进的麻雀搜索算法[J].辽宁工程技术大学学报（自然科学版）,2023,42(6):722-732,11.基金项目
辽宁省高等学校基本科研项目(LJ2017QL009) （LJ2017QL009）
辽宁工程技术大学学科创新团队资助项目(LNTU20TD-32) （LNTU20TD-32）
