摘要
文章提出一种改进灰狼优化算法。利用非线性调整策略和混沌Logistic映射对猎物包围机制进行改进,有效协调个体全局搜索与局部开发过程,提升包围速度;在狩猎过程中引入自身位置经验信息,将当前种群最优解与自身历史最优解结合更新个体位置,有效避免局部最优解。通过四种基准函数测试,验证改进灰狼优化算法可以提升寻优精度和收敛速度。
The article proposes an improved grey wolf optimization(IGWO)algorithm.IGWO uses a nonlinear adjustment strategy and chaos Logistic map to improve the prey enclosure mechanism,which can effectively coordinate individual global search and local development process and promote enclosure speed.IGWO introduces its own location experience information in the hunting process,and combines the current population optimal solution with its own historical optimal solution to update the individual location,effectively avoiding local optimal solutions.Through four benchmark function tests,it is verified that the improved grey wolf optimization algorithm can improve the optimization accuracy and convergence speed.
作者
张贻红
郭文涛
ZHANG Yihong;GUO Wentao(Big Data Center of State Grid Corporation of China,Bejing 100053,China)
出处
《现代信息科技》
2020年第20期138-140,145,共4页
Modern Information Technology
