基于混合变异策略的改进差分进化算法及函数优化  

A Modified Differential Evolution Algorithm Based on Hybrid Mutation Strategy for Function Optimization

作　　者：乔俊飞[1] 傅嗣鹏[1] 韩红桂[1] 

机构地区：[1]北京工业大学电子信息与控制工程学院,北京100124

出　　处：《控制工程》2013年第5期943-947,共5页Control Engineering of China

基　　金：国家自然科学基金重点项目(61034008);国家自然科学基金项目(60873043);教育部博士点基金项目(200800050004);北京市"创新人才建设计划"项目(PHR201006103);教育部新世纪优秀人才支持计划项目(NCET-08-0616);北京市属市管高等学校人才强教计划资助项目PHR(IHLB)201006103

摘　　要：针对差分进化算法DE传统变异策略不能有效平衡全局搜索和局部搜索,并且算子固定,导致算法早收敛、搜索效率较低。基于DE变异策略性能,提出一种混合变异策略,力图平衡算法探索和开发能力,使得前期增强全局搜索,保持种群多样性;后期偏重局部搜索,尽快收敛到全局最优值。同时操作算子采用随机正态缩放因子F和时变交叉概率因子CR,进一步改善算法性能。几个典型Benchmarks测试函数实验表明:该改进型差分进化算法能有效避免早收敛,较好地提高算法的全局收敛能力和搜索效率。The traditional mutation strategy of differential evolution algorithm can not reach a good balance between the global search and the local search and the operators are constant. The differential evolution algorithm leads to premature convergence and the low search efficiency. Based on analysis of the performance of the optimization strategies, a hybrid mutation strategy is proposed in this pa- per. The scheme attempts to balance the exploration and exploitation abilities. In this way, emphasis is laid on the global search at the beginning, which results in maintaining the diversity of population. Later, contribution from the local search increases in order to con- verge to the optimal faster. Meanwhile, the random normal scaling factor F and the time - varying crossover probability factor CR are used synchronously to improve the performance of DE. Finally, the modified differential evolution algorithm is tested on benchmark functions. The simulation results show that the modified algorithm can effectively avoid the premature convergence, as well as modified the ~lobal convergence abilitv and the search efficiencv remarkablv.

关 键 词：差分进化算法 混合变异 操作算子 

分 类 号：TP273[自动化与计算机技术—检测技术与自动化装置]