Research on Self-adapting Differential Evolution With OBL
MIAO Xiaofeng
LIU Zhiwei
Abstract：Differential evolution(DE)is a well-known optimization technique to deal with nonlinear and complex problems. In order to tackle the problems,such as much overhead,problem-dependent parameters,etc. This paper presents a mixed DE algo?rithm,called MDE,by employing opposition-based learning(OBL)and a self-adapting mechanism to adjust parameters to im?prove the convergence and robustness. Experiments in Matlab show that the proposed approach MDE outperforms many existing algo?rithms on convergence,robustness and overhead,proving that hybrid is an effective path on DE research.
Keywords：genetic algorithmoptimizationopposition-based learning (OBL)self-adaptingdifferential evolution (DE)simulation
Publication Date：2019-01-01
Online Publishing Date：2025-08-15(First online date of this platform, not the publication date of the document)
Pages：5( 2953-2956,3120 )