混合柯西变异和均匀分布的蝗虫优化算法

由于位置更新公式存在局部开发能力较强而全局探索能力较弱的缺陷,导致蝗虫优化算法(GOA)易陷入局部最优以及早熟收敛,对此,提出一种混合柯西变异和均匀分布的蝗虫优化算法(HCUGOA).受柯西算子和粒子群算法的启发,提出具有分段思想的位置更新方式以增加种群多样性,增强全局探索能力;将柯西变异算子与反向学习策略相融合,对最优位置即目标值进行变异更新,提高算法跳出局部最优的能力;为了更好地平衡全局探索与局部开发,将均匀分布函数引入非线性控制参数c,构建新的随机调整策略.通过对12个基准函数和CEC2014函数进行仿真实验以及Wilcoxon秩和检验的方法来评估改进算法的寻优能力,实验结果表明,HCUGOA算法在收敛精度和收敛速度等方面都得到极大的改进.

Hybrid Cauchy mutation and uniform distribution of grasshopper optimization algorithm

Due to the strong local exploitation ability and the weak global exploration ability of the location update formula, the grasshopper optimization algorithm(GOA) is easy to fall into local optimum and easy to prematurely converge. Therefore, this paper proposes a hybrid Cauchy mutation and uniform distribution of the grasshopper optimization algorithm(HCUGOA). Firstly, inspired by the Cauchy operator and particle swarm optimization algorithm, a location update method with segmentation idea is proposed to increase the diversity of the population and to enhance the global exploration ability. Then, the fusion of Cauchy mutation and opposition-based learning and the variation of the optimal position which is the target value improve the ability of the algorithm to jump out of the local optimum. Finally, in order to better balance the global exploration and local exploitation, the uniform distribution function is introduced into the nonlinear control parameter c, so that a new random adjustment strategy can be built. The optimization performance of the improved algorithm is evaluated by a sets of simulation experiments and Wilcoxon''s test on 12 benchmark functions and modern CEC 2014 functions. The experimental results show that the HCUGOA has been greatly improved in terms of convergence accuracy and convergence speed.