一种基于入侵杂草算法改进的差分进化算法
A Modified Differential Evolution Algorithm Based on Invasive Weed Optimization
摘要: 差分进化算法(Differential Evolution, DE)的DE/rand/1/bin模式具有良好的全局性能，但其收敛速度慢，针对此，应用入侵杂草算法(Invasive Weed Optimization, IWO)的设计思想，研究提出一种改进的差分进化算法(Modified Differential Evolution Algorithm based on Invasive Weed Optimization, IWOMDE)。该算法依据IWO算法思想，以群体平均适应度为划分依据，将群体中的个体划分为优秀个体与较差个体，对优秀个体使其能够多进化，而较差个体少进化或者“停滞”以维持种群的多样性。仿真实验结果表明，IWOMDE算法对多峰多模态函数具有优化效率高、优化精度高的特性。
Abstract: DE/rand/1/bin model of DE Algorithm has good global performance, but its convergence speed is slow. A modified differential evolution optimization algorithm named IWOMDE was presented in this paper based on Invasive Weed Optimization algorithm. The IWOMDE algorithm incorporated IWO’s design philosophy into Differential Evolution (DE) algorithm. The IWOMDE algorithm divided the evolution population into elite individuals and poor individuals based on the average fitness of population. The elite individuals can be evolutionary many times, but the poor individuals are less evolved or “stagnate” in order to maintain the diversity of population. The simulation results showed the hybrid optimization algorithm has the advantage of searching effectively and being fairly robust to initial conditions.
文章引用：卢青波, 崔巍, 闫生辉, 李廷锋. 一种基于入侵杂草算法改进的差分进化算法[J]. 计算机科学与应用, 2019, 9(2): 266-274. https://doi.org/10.12677/CSA.2019.92031

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