摘要
针对标准灰狼优化(grey wolf optimization,GWO)算法存在后期收敛速度慢,求解精度不高,易出现早熟收敛现象等问题,提出了一种基于对立学习策略和Rosenbrock局部搜索的混合灰狼优化(hybrid GWO,HGWO)算法。该算法首先采用对立学习策略取代随机初始化生成初始种群,以保证群体的多样性;然后对当前群体中最优个体进行Rosenbrock局部搜索,以增强局部搜索能力和加快收敛速度;最后为了避免算法出现早熟收敛现象,利用精英对立学习方法产生精英对立个体。对6个标准测试函数进行仿真实验,并与其他算法进行比较,结果表明,HGWO算法收敛速度快,求解精度高。
The standard grey wolf optimization(GWO)algorithm has a few disadvantages of slow convergence,low solving precision and high possibility of being trapped in local optimum.To overcome these disadvantages ofGWO algorithm,this paper proposes a hybrid GWO(HGWO)algorithm based on opposition-based learning strategyand Rosenbrock local search method.In the proposed hybrid algorithm,opposition-based learning strategy is introducedto generate initial population,which strengthens the diversity of population.Rosenbrock local search methodis applied to the current best individual,which improves the convergence speed and local search ability of GWOalgorithm.Elite opposition-based learning approach is used to avoid premature convergence of GWO algorithm.The experimental results of6well-known benchmark functions show that the proposed HGWO algorithm hasstrong convergence and high precision.
作者
王敏
唐明珠
WANG Min;TANG Mingzhu(Department of Information Engineering, Hunan Mechanical & Electrical Polytechnic, Changsha 410151, China;School of Computer and Communication, Hunan University, Changsha 410082, China;School of Energy and Power Engineering, Changsha University of Science & Technology, Changsha 410114, China)
出处
《计算机科学与探索》
CSCD
北大核心
2017年第4期673-680,共8页
Journal of Frontiers of Computer Science and Technology
基金
国家自然科学基金No.61403046
湖南省科技计划项目No.2014FJ3051~~
