求解高维复杂函数的混合蛙跳–灰狼优化算法

A Shuffled Frog Leaping-Grey Wolf Algorithm for Solving High Dimensional Complex Functions

中文关键词  高维复杂函数  改进的Logistic映射  距离控制参数  灰狼优化算法  混合蛙跳算法

英文关键词  high-dimensional complex function  improved Logistic mapping  distance control parameter  grey wolf optimization algorithm  shuffled frog leaping algorithm

中文摘要

      针对高维复杂函数问题, 提出一种混合蛙跳–灰狼优化算法(SFL–GWO). 该算法通过改进的Logistic映射初 始化GWO算法种群提高算法的多样性; 其次, 提出一种新的距离控制参数的非线性调整策略来增强种群的探索与 开发的能力; 最后通过引入改进的随机蛙跳算法中改变最差位置的方式使SFL–GWO算法跳出局部最优的局限. 通 过选取的10个高维复杂函数的寻优结果验证了算法的性能, 并与粒子群优化算法(PSO)、灰狼优化算法(GWO)和鲸 鱼优化算法(WOA)3种基本算法以及与8种改进算法的寻优的结果进行了比较. 仿真结果证明: SFL–GWO算法在不 仅可以提高收敛精度也可以提高算法的搜索速度, 证明了SFL–GWO算法在求解高维复杂函数的高效性.

英文摘要

      Aiming at the problem of high-dimensional complex function, a shuffled frog leaping-grey wolf optimization algorithm (SFL–GWO) is proposed. Firstly, in order to improve species diversity of the algorithm, the improved Logistic map is used to initialize the GWO algorithm. Secondly, a new nonlinear adjustment strategy of distance control parameters is proposed to enhance the population exploration and development ability. Thirdly, an improved random strategy of Frog Leaping is introduced, which can change the worst position to let SFL–GWO algorithm jumping out the local optimum. Finally, the performance of the algorithm is verified by the optimization results of 10 high-dimensional complex functions. The performance results demonstrated that the SFL–GWO algorithm can not only improve the convergence accuracy but also improve the search speed of the algorithm in solving high-dimensional complex functions compared with particle swarm optimization (PSO), grey wolf optimization (GWO), wolf optimization algorithm (WOA) and several improved algorithms.