计算机与数字工程2019，Vol.47Issue(12)：2953-2956,3120,5.DOI:10. 3969/j. issn. 1672-9722. 2019. 12. 003
引入反向学习机制的自适应差分进化算法研究
Research on Self-adapting Differential Evolution With OBL
摘要
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.关键词
遗传算法/优化算法/反向学习/自适应/差分进化/仿真Key words
genetic algorithm/optimization/opposition-based learning (OBL)/self-adapting/differential evolution (DE)/simulation分类
信息技术与安全科学引用本文复制引用
苗晓锋,刘志伟..引入反向学习机制的自适应差分进化算法研究[J].计算机与数字工程,2019,47(12):2953-2956,3120,5.基金项目
国家自然科学基金项目(编号:61672433),榆林职业技术学院神木校区2018年校级教科研课题重点项目(编号:ZK-201801)资助. （编号:61672433）
