一种基于进化方向的新的差分进化算法

变异策略对差分进化算法（DE）算法的成功与否起到至关重要的作用.然而,方向信息在DE变异策略的设计当中并没有被充分地挖掘,且对于如何平衡进化速度和种群多样性这两者之间的矛盾也没有得到很好的解决方案.研究了个体在进化选择操作前后产生的差量信息在变异操作上的导向作用,提出了一种新的基于进化方向的变异策略“DE/current-to-pbest/1/Gvector”.同时,为了测试我们这种新的方向信息能否提高算法的优化能力,我们在自适应差分进化算法（JADE）的基础上提出了一种新的算法DVDE.对CEC2005常用的12个测试函数做了仿真实验,实验结果证明DVDE的算法性能平均优于其他5个目前来说性能最好的DE算法（JADE,SaDE,CoDE,jDE,EPSDE）,特别是对于单峰函数,效果更为明显.实验结果也说明进化方向的加入对于提高算法的收敛速度以及保护种群的多样性避免算法过早陷入局部最优起到了较好的作用.

An Evolution Direction-Based Mutation Strategy for Differential Evolution Algorithm

Mutation strategy plays a decisive role on the success of the differential evolution algorithm(DE). However, the direction information has not been fully exploited in the design of DE and the balance between the evolution speed and the population diversity cannot be well handled so far. In this paper, it explores a novel direction information which is generated by the selection operation and it''s directive effect on the mutation operation. On this basis, it proposes an evolution direction-based mutation strategy "DE/current-to-pbest/1/Gvector" and an improved differential evolution algorithm based on adaptive differential evolution algorithm(JADE) for comparison. We name our algorithm as DVDE and compare it with five state-of-the-art adaptive DE variants(JADE, SaDE, CoDE, jDE, EPSDE), using 12 standard numerical benchmarks taken from the IEEE Congress on Evolutionary Computation 2005. The simulation results indicate that the average performance of the DVDE is better than those of all other competitors, especially for the unimodal functions. The experimental results also illustrate that the using of the evolution direction is helpful to improve the algorithm''s convergence speed, maintain the population, and effectively avoid premature convergence problem.