兵工自动化2025，Vol.44Issue(7)：31-36,6.DOI:10.7690/bgzdh.2025.07.007
基于多策略融合的改进灰狼算法
Improved Grey Wolf Algorithm Based on Multi-strategy Fusion
摘要
Abstract
In order to solve the problems of small search scale,slow convergence speed and imbalance between global search and local search in current path optimization algorithms,a multi-strategy fusion of grey wolf optimization algorithm(MGWO)is proposed.The quality of the initial solution is improved by introducing the elite reverse optimization strategy to initialize the population.An adaptive weight mechanism is used to dynamically adjust the leadership of the optimal wolf.The ability of balancing local search and global exploration is improved through the piecewise search method.The simulation results show that the algorithm performs well,can quickly find the optimal path,and improve the overall performance of the algorithm,which has a certain reference.关键词
改进灰狼算法/精英反向策略/自适应权重/分段策略/路径优化Key words
improved grey wolf algorithm/elite reverse strategy/adaptive weight/segmentation strategy/path optimization分类
数理科学引用本文复制引用
文竹,韦杏琼,刘静怡..基于多策略融合的改进灰狼算法[J].兵工自动化,2025,44(7):31-36,6.基金项目
广西哲学社会科学研究课题(24KSB008) （24KSB008）
广西高等教育本科教学改革工程A类项目(2024JGA395) （2024JGA395）
