Self-adapting Differential Evolution Algorithm with Extension Variable Dimension
Recently, the differential evolution (DE) algorithm has attracted much attention as an effective approach for numerical optimization. Since the performance of DE is sensitive to the choice of associated control parameters, a large number of strategies on parameter determination have been presented in the past several years. However, most of them have limitations. Thus, to get optimal performance, time-consuming parameter tuning is necessary. This paper introduces an extension variable dimension of DE (EVSDE). In EVSDE, the control parameters are considered as a variable of the component. The variable dimension is expended and a new mutation strategy is employed for the extension dimension of variables on mutation operation. On the basis of experience value, the control parameters follow the individual variable and implement the dynamic self-adaptive process in the evolutionary process. It thus helps to improve the robustness of the algorithm and avoid premature convergence. Simulation results show the EVSDE is better than or at least comparable to other classic and adaptive DE algorithms from the literature in terms of convergence performance for a set of 10 benchmark problems.
Differential evolution algorithm Adaptive parameter control Extension variable dimension
FENG Da GAO Yuan GAO LiQun
AVIC Aerodynamics Research Institute, Shenyang, 110034, China Department of Computer Science, The University of Western Ontario, Ontario, N6A 5B7, Canada School of Information Science and Engineering, Northeastern University, Shenyang, 110004, China
国际会议
The 24th Chinese Control and Decision Conference (第24届中国控制与决策学术年会 2012 CCDC)
太原
英文
751-754
2012-05-23（万方平台首次上网日期，不代表论文的发表时间）