基于随机扩散搜索的协同差分进化算法

Cooperation Differential Evolution Algorithm Based on Stochastic Diffusion Search

针对差分进化算法存在的收敛速度慢、稳健性差等问题,借鉴多种群并行机制和随机搜索策略,提出一种基于随机扩散搜索的协同差分进化算法。引入反向混沌搜索的初始化机制,利用随机扩散搜索策略将种群分为成功和失败2个子群并进行改进,对改进的成功和失败子群分别采用不同的差分策略,克服单一差分策略的缺陷,同时定期使子群的部分最好与最差个体实现一对一的信息交流,从而达到协同进化的目的。仿真结果证明,与粒子群优化算法及差分进化算法相比,该算法具有较好的收敛速度和寻优能力。

Aiming at the problem of slow convergence speed, bad robustness, reference multi populations parallel mechanism and random search strategy. A novel synergy search Differential Evolution based on Stochastic Diffusion Searcb（SDS-DE）, which aims to accelerate convergence and improve accuracy of SDS-DE, the algorithm introduces the initialization mechanism of opposition chaos search, and stochastic diffusion search strategy is used to divided populations into successful and failed two sub-groups, and differential strategies of the success and failures sub-groups are used to overcome the shortcomings single differential strategy, meanwhile, regularly subgroup best and worst part of individuals to achieve positive and negative feedback mechanisms for information exchange, so as to achieve the purpose of co-evolution. Simulation results prove that the SDS-DE performs better convergence speed and optimization capability by the comparison with the Particle Swarm Optimization（PSO） and other DE algorithms.