摘要
对启发式优化算法中的差分进化算法进行改进,在进化过程中并行交叉采用DE/rand/1/exp和DE/best/1/exp差分策略,应用聚集度因子进行种群重构,缩小了种群重构后的搜索范围,有效避免了种群重构的随机性.仿真结果表明,改进算法与使用单一差分策略的差分进化算法及PSO算法相比,寻优能力得到了显著提高.
To improve the differential algorithm, a new kind of heuristic optimization method and a modified differential evolution （MDE） scheme are presented, where DE / rand / 1 / exp and DE / best / 1 / exp run currently in evolution process. With the usage of current aggregation degree, the search range of re- initialization of the MDE is reduced and the randomicity of re- initialization is avoided efficiently. The experimental results show that the search ability of MDE is improved remarkably compared to DE with single differential strategy and PSO.
出处
《河海大学常州分校学报》
2007年第4期9-12,共4页
Journal of Hohai University Changzhou
关键词
差分进化
差分策略
种群重构
differential evolution
differential strategy
population re- initialize
