基于双变异模式协同的自适应微分进化算法  

Self-adaptive differential evolution algorithm with dual mutation modes collaboration

作　　者：王世豪[1,2] 杨红雨[1,2] 李玉贞[3] 韩松臣[1,2] 杨波[2] 

机构地区：[1]四川大学空天科学与工程学院,成都610065 [2]四川大学国家空管自动化系统技术重点实验室,成都610065 [3]上海电器科学研究所,上海200063

出　　处：《控制与决策》2017年第7期1196-1202,共7页Control and Decision

基　　金：国家自然科学基金项目(71573184);民航科技项目(20150228)

摘　　要：针对微分进化算法(DE)易陷入局部最优解、进化后期收敛速度慢、求解精度低等缺点,结合DE/rand/1和DE/best/1两种变异模式分别具有全局探索能力和局部开发能力的优点,引入精英存档策略和控制参数自适应策略,提出一种双变异模式协同自适应微分进化(DMCSa DE)算法.15个典型benchmark测试函数的实验结果表明,DMCSa DE能够有效提高算法的全局探索能力和局部开发能力,避免早熟收敛,大大提高算法的收敛性能和鲁棒性,同时,精英种群的大小对DMCSa DE的优化性能具有明显的影响.The differential evolution（DE） algorithm has some disadvantages, such as easy to fall into local optimal solutions, slow convergence speed and low convergence precision in the later stage of evolution. By means of combining DE/rand/1 mutation mode with global exploration ability and DE/best/1 mutation mode with local exploitation ability,and introducing the elite archive strategy and control parameters adaptation strategy, a self-adaptive differential evolution with dual mutation modes collaboration（DMCSa DE） is proposed. A total of 15 typical benchmark test functions are used to perform comparative experiments. The experimental results show that DMCSa DE can effectively improve the global exploration ability and local exploitation ability, avoid the premature convergence and greatly improve the convergence performance and robustness. Additionally, the size of elite population has a significant effect on the optimization performance of DMCSa DE.

关 键 词：微分进化 全局优化 精英存档 控制参数自适应 

分 类 号：TP18[自动化与计算机技术—控制理论与控制工程]