The improved mayfly optimization algorithm
The improved mayfly optimization algorithm
复制标题
改进的蜉蝣优化算法
DOI：
10.1088/1742-6596/1684/1/012077
复制
发表时间：
2020
期刊：
影响因子：
--
通讯作者：
Yu
中科院分区：
文献类型：
--
作者：
Zheng;Juan Zhao;Suruo Li;Yu
将粒子群优化算法与差分进化算法相结合，提出了一种改进的蜉蝣优化算法。速度将与相关个体之间的笛卡尔距离相关。本文基于尽可能相互靠近的思想，对速度更新方程进行了合理的修正。仿真结果表明，改进的MO算法性能优于原MO算法。
The mayfly optimization (MO) algorithm was proposed with a better hybridization of the particle swarm optimization (PSO) and the differential evolution (DE) algorithms. The velocity would be relevant to the Cartesian distance among the relevant individuals. In this paper, a reasonable revision for the velocity updating equations was proposed based on the idea of moving towards each other as capable as they can. Simulation results proved that the improved MO algorithm would perform better than the original one.