基于混合变异策略的改进差分进化算法及函数优化

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。

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

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 paper． 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 converge 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 global convergence ability and the search efficiency remarkably．