多变异策略差分进化算法的研究与应用

摘要

标准差分进化( DE)算法在高维多峰等复杂函数优化时易出现早熟现象，并且算法后期收敛速度较慢。为此，研究2种标准差分进化算法的变异策略( DE/rand/1和DE/best/1)，并将其进行串行组合，提出一种多变异策略的差分进化算法( MDE)。在4个Benchmark函数上的测试结果表明，在多变异策略下，通过对MDE算法控制参数的调整能有效拓展和平衡改进后算法的全局与局部搜索能力，其所得最优解的精度、算法的收敛速度都较标准差分进化算法有明显优势，能较好地解决电力负载分配问题。%In order to overcome the shortcomings of the standard Differential Evolution ( DE ) algorithm in the optimization of complex functions like dimension multi-modal functions,such as the problem of premature and slow later convergence,this paper proposes a DE algorithm based on the Mutation strategy( MDE) through serial combination of DE/rand/1 and DE/best/1. It makes an in-depth study of this algorithms,and finally the algorithm is tested on the four Benchmark functions. Result shows that through the modulation of the control parameters of MDE can effectively expands and balances the global and local search capabilities of the improved algorithm,and its resultant optimal accuracy,and convergence speed are better than standard DE algorithm. It can be well applied in electric power load distribution.