文献类型：会议论文

英文题名：Differential Evolution Improved with Adaptive Control Parameters and Double Mutation Strategies

作者：Liu, Jun[1];Yin, Xiaoming[1];Gu, Xingsheng[1]

机构：[1]East China Univ Sci & Technol, Minist Educ, Key Lab Adv Control & Optimizat Chem Proc, Shanghai 200237, Peoples R China

会议论文集：Joint Conference of the 16th Asia Simulation Conference / SCS International Autumn Simulation Multi-Conference (AsiaSim/SCS AutumnSim)

会议日期：OCT 08-11, 2016

会议地点：Beijing, PEOPLES R CHINA

语种：英文

外文关键词：Differential evolution; Mutation strategies; Adaptive parameters; Dynamic random search

摘要：Recently, differential evolution (DE) algorithm has attracted more and more attention as an excellent and effective approach for solving numerical optimization problems. However, it is difficult to set suitable mutation strategies and control parameters. In order to solve this problem, in this paper a dynamic adaptive double-model differential evolution (DADDE) algorithm for global numerical optimization is proposed, and dynamic random search (DRS) strategy is introduced to enhance global search capability of the algorithm. The simulation results of ten benchmark show that the proposed DADDE algorithm is better than several other intelligent optimization algorithms.