摘要
针对标准的灰狼优化算法GWO对于复杂优化问题的求解易陷入局部最优的缺点,从混沌初始化和非线性控制策略2个角度,提出一种基于Cubic映射和反向学习的灰狼优化算法COGWO。首先,利用Cubic映射和反向学习策略对种群进行初始化,并通过非线性参数控制策略来调节寻优过程中的参数;然后,对6种基准测试函数进行寻优实验,实验结果表明,COGWO算法具有更好的收敛精度、收敛速度和稳定性;最后,将COGWO算法应用到了实际的工程优化问题中。
Aiming at the problem that the grey wolf optimization algorithm(GWO)is easy to fall into the local optimal solution to the complex optimization problems,from the perspective of chaos initia-lization and nonlinear control strategy,a grey wolf optimization algorithm based on cubic mapping and opposition-based learning is proposed(COGWO).Firstly,the cubic mapping and opposition-based learning strategies are used to initialize the population,and the parameters are adjusted by a nonlinear parameter control strategy in the optimization process.Then,the optimization experiment on six benchmark test functions show that the COGWO algorithm has better convergence accuracy,convergence speed and stability.Finally,the COGWO algorithm is applied to a practical engineering optimization problem.
作者
张孟健
张浩
陈曦
杨靖
ZHANG Meng-jian;ZHANG Hao;CHEN Xi;YANG Jing(College of Electrical Engineering,Guizhou University,Guiyang 550025,China)
出处
《计算机工程与科学》
CSCD
北大核心
2021年第11期2035-2042,共8页
Computer Engineering & Science
基金
国家自然科学基金(61861007,61640014)
贵州省工业攻关项目(黔科合支撑[2019]2152)
贵州省研究生创新基金(YJSCXJH[2019]005)
贵州省科技基金(黔科合基础[2020]1Y266)
贵州省农业攻关项目(黔科合支撑[2017]2520-1)
贵州省联合基金(黔科合LH字[2017]7228)。
关键词
灰狼优化算法
Cubic映射
反向学习
拉伸弹簧设计
非线性
grey wolf optimization algorithm
Cubic mapping
opposition-based learning
tensile spring design
nonlinear
