基于群体分布的自适应差分进化算法

doi:10.11896/jsjkx.181202356

摘要/Abstract

摘要： 差分进化算法是一种简单有效的启发式全局优化算法,但是其优化性能受差分进化策略及控制参数取值的影响较大,不合适的策略和参数容易导致算法早熟收敛。因此,针对差分进化算法搜索过程中变异策略和控制参数的选择问题,文中提出了一种基于群体分布的自适应差分进化算法(Population Distribution-based Self-adaptive Differential Evolution,PDSDE)。首先,设计适应因子以衡量当前种群的分布情况,进而实现算法所处进化阶段的自适应判断;然后,根据不同进化阶段的特点,设计阶段特定的变异策略和控制参数,并设计自适应机制以实现算法策略和参数的动态调整,从而平衡算法的全局探测和局部搜索能力,以达到提高算法搜索效率的目的;最后,将所提算法与6种主流改进算法进行比较。15个典型测试函数的数值实验表明,所提算法在平均函数评价次数、求解精度、收敛速度等指标的评价优于文中给出的6种主流改进算法,因此可以证明所提算法的计算代价、优化性能和收敛性能更具优势。

关键词: 差分进化, 阶段划分, 全局优化, 群体分布, 自适应

Abstract: Differential evolution is a simple and powerful heuristic global optimization algorithm.However,its performance is strongly influenced by the differential evolution strategies and the value of control parameters.Inappropriate strategies and parameters may lead the algorithm fall into premature convergence.Aiming at the problem about selection of strategies and parameters in search process of differential evolution,a population distribution-based self-adaptive differential evolution algorithm was proposed.Firstly,the adaptive factor is established for measuring the distribution of the current population,and the evolution stage of the algorithm can be further determined adaptively.Then,according to the characteristics of different evolution stages,the stage-specific mutation strategies and control parameters are designed,the self-adaptive mechanism is also designed in order to realize dynamic adjustment of strategies and parameters,to balance the global detection and local search capabilityof the algorithm,and improve the search efficiency of the algorithm.Finally,the proposed algorithm is compared with six main-stream differential evolution variants.The numerical experiments of fifteen typical test functions show that the proposed algorithm is superior to six main-stream differential evolution variants in terms of the measures of the average function evaluation times,solution accuracy and converge velocity.Therefore,the computational cost,optimization performance and convergence performance of the proposed algorithm can be proved to be more advantageous.

Key words: Differential evolution, Global optimization, Population distribution, Self-adaptive, Stage division

中图分类号:

TP301.6

李章维,王柳静. 基于群体分布的自适应差分进化算法[J]. 计算机科学, 2020, 47(2): 180-185. https://doi.org/10.11896/jsjkx.181202356

LI Zhang-wei,WANG Liu-jing. Population Distribution-based Self-adaptive Differential Evolution Algorithm[J]. Computer Science, 2020, 47(2): 180-185. https://doi.org/10.11896/jsjkx.181202356

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