摘要
介绍了一种基于新的变异算子多种群的新遗传算法 ,该算法可用来解决复杂的多峰函数优化问题 .解决这些问题的传统遗传算法经常陷入局部最优 ,新算法引入一种新的基于主群、附属子群的结构可避免传统遗传算法难以克服的早熟收敛 .在该结构中 ,主群采用新的变异算子来保持良好的群体分布 ,并促使较优模式的快速增长 ,附属子群设计在有限区域内获取局部最优 .用搜索历史记录及主子群体通讯能减少搜索空间 ,以获取全局最优和几个局部最优 .搜索局部最优和全局最优可用于多人脸检测以及路径寻优问题 .实验表明 。
Authors presents a novel genetic algorithms based on multi species with new mutation operator. The method is designed for optimizing complex multimodal function in which the standard genetic algorithms (SGA) always gets struck into a local optimum. The authors introduce a new structure based on main species and additional species to avoid premature convergence of SGA. In this structure, the main species use a new mutation operator to keep population diversity in entire search space and acquire the fast increasing of better models, and the additional species are designed to get local optima in the specified regions. The store of research history and the communication between main and additional species help to decrease research space for acquiring the global optimum and several local optima. The experiments carried on to optimize several complex multimodal functions have acquired good results.
出处
《四川大学学报（自然科学版）》
CAS
CSCD
北大核心
2003年第2期278-282,共5页
Journal of Sichuan University(Natural Science Edition)
基金
国家自然科学基金 (60 2 72 0 95 )
