具有自适应调整策略的混沌灰狼优化算法  

Chaotic Gray Wolf Optimization Algorithm with Adaptive Adjustment Strategy

作　　者：张悦 孙惠香[1] 魏政磊 韩博[1] 

机构地区：[1]空军工程大学航空航天工程学院,西安710038

出　　处：《计算机科学》2017年第B11期119-122,159,共5页Computer Science

基　　金：国家自然科学基金资助项目(51208506)资助

摘　　要：灰狼优化算法(Grey Wolf Optimization,GWO)是新型启元优化算法,相比于其他群体智能优化算法,该算法同样存在收敛速度较慢、不稳定、易陷入局部最优等问题。针对上述问题,根据GWO算法的结构特点,提出了一种自适应调整策略的混沌灰狼优化算法(Chaotic Local Search GWO),利用自适应调整策略来提高GWO算法的收敛速度,通过混沌局部搜索策略增加种群的多样性,使搜索过程避免陷入局部最优。最后利用6个测试函数对算法进行仿真验证,并结合其他4种算法进行了横向比较。实验结果证明,所提出的改进算法在收敛速度、精度以及稳定性方面具有明显的优势。Gray wolf optimization is a new optimization algorithm.Compared with other groups,it has the problems of low convergence speed,unstable and easy to fall into local optimum.According to the structural characteristics of the GWO algorithm,a chaotic gray wolf optimization algorithm based on the adaptive adjustment strategy was proposed.The adaptive adjustment strategy is used to improve the convergence rate of the GWO algorithm.The chaotic local search strategy is used to increase the diversity of the population and avoid search process falling into the local optimal,Finally,six algorithms are used to simulate the algorithm,and the other four algorithms are compared.The experimental results show that the proposed algorithm has obvious advantages in terms of convergence speed,accuracy and stability.

关 键 词：灰狼算法 自适应 混沌 收敛速度 局部最优 

分 类 号：TP301[自动化与计算机技术—计算机系统结构]