智能系统学报2017，Vol.12Issue(4)：431-442,12.DOI:10.11992/tis.201605015
差分进化算法综述
Research survey of differential evolution algorithms
摘要
Abstract
Due to its simple algorithm structure, ease of performance, high optimization efficiency, simple parameter setting, and excellent robustness, the differential evolution ( DE ) algorithm has attracted increasing attention from researchers. In this paper, we outline the basic concepts of the DE algorithm as well as its limitations, and review four improvement strategies, including a control parameter, differential strategy, population structure, and mixing it with other optimization algorithms. We discuss the advantages and disadvantages of these strategies and suggest directions for future improvements to the DE algorithm.关键词
差分进化/启发式并行搜索/差分策略/控制参数/种群结构/混合优化/收敛速度/优化效率Key words
differential evolution algorithm/heuristic parallel search/differential strategies/control parameter/population structure/mixed optimization/convergence rate/optimization efficiency分类
信息技术与安全科学引用本文复制引用
丁青锋,尹晓宇..差分进化算法综述[J].智能系统学报,2017,12(4):431-442,12.基金项目
国家自然科学基金项目(61501186) （61501186）
江西省普通本科高校中青年教师发展计划访问学者专项资金项目 （）
江西省自然科学基金项目(20171BAB202001) （20171BAB202001）
江西省教育厅科学基金项目( GJJ150491) . （ GJJ150491）
