计算机科学 ›› 2019, Vol. 46 ›› Issue (6A): 106-110.
倪洪杰, 彭春祥, 周晓根, 俞立
NI Hong-jie, PENG Chun-xiang, ZHOU Xiao-gen, YU Li
摘要: 针对差分进化算法的变异策略选择问题,提出一种阶段性策略自适应差分进化算法(SSADE)。首先,根据各个体与当前最优个体之间的平均距离衡量种群的拥挤度,进而估计种群的进化阶段;然后,将整个种群划分为多个子种群,并针对不同阶段的特性,设计子种群协同进化变异策略池;最后,根据各变异策略的历史成功信息,从对应的策略池中动态自适应地选择合适的变异策略,从而达到平衡全局探测和局部搜索的目的。在12个经典测试函数上的实验结果表明,所提SSADE算法在计算代价、可靠性、解的质量和扩展性方面优于现有主流算法。
中图分类号:
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