基于倒位变异的蜉蝣优化算法

蜉蝣算法(Mayfly Algorithm,MA)作为一种新型群智能优化算法,具有较好的寻优性能.但在高维非线性复杂问题上,蜉蝣算法依然容易出现早熟收敛现象.本文提出一种基于倒位变异的蜉蝣算法(Inversion Variation Mayfly Algorithm,IVMA),改变原算法在变异上的操作,随机选择个体的随机维度向全局最优个体的随机维度靠近,同时利用精英策略保留进化成果.利用倒位操作,将最优个体某一维度段内位置发生倒转,提高了算法跳出局部最优的能力.通过对10个测试函数的结果分析,表明本文所提出的算法具有较好的收敛精度,收敛性能得到了提高.

Mayfly Optimization Algorithm Based on Inversion Variation

The Mayfly Algorithm (MA), which serves as a new swarm intelligence optimization algorithm, proves to perform well in optimization. However, when it comes to complex problems related to high dimensions and linearity, MA is still prone to premature convergence. Thus, a new mayfly algorithm based on inversion variation (Inversion Variation Mayfly Algorithm, IVMA) is proposed. IVMA, which changes the operation of the original MA on mutation, stochastically selects the random dimension of an individual to approach that of the global optimal individual. In addition, it retains the evolution results with the elite strategy. The inversion operation is used to reverse the position of the optimal individual in a certain dimension segment, which enhances the ability of the algorithm to jump out of the local optimum. The results from ten test functions indicate that IVMA has high convergence accuracy and improved convergence performance.