【摘要】 差分进化算法逐渐成为进化计算领域最流行的随机搜索算法之一,已被成功用于求解各类应用问题.差分进化算法参数设置与其性能密切相关,因此算法参数控制与适应策略设计是目前该领域的研究热点之一,目前已涌现出大量参数控制方案,但尚缺乏系统性的综述与分析.首先简要介绍差分进化算法的基本原理与操作,然后将目前参数控制与适应策略分成基于经验的参数控制、参数随机化适应策略、基于统计学习的参数随机化适应策略和参数自适应策略4类进行系统性综述,重点介绍其中的参数适应与自适应策略.此外,为分析各种参数控制与适应策略的功效,以实值函数优化为问题背景设计了相关实验,进一步分析各种策略的效率与实用性,实验结果表明,参数自适应控制策略是目前该领域最有效的方法之一.更多还原

【Abstract】 Differential evolution algorithms have gradually become one of the most popular types of stochastic search algorithms in the area of evolutionary computation.They have been successfully applied to solve various problems in real-world applications.Since their performance often depends heavily on the parameter settings,the design of parameter control and adaptation strategies is one of the current hot topics of research in differential evolution.Although numerous parameter control schemes have been proposed,systematic overviews and analysis are still lacking.In this paper,first the basic principles and operations of the differential evolution algorithm were briefly introduced,and then a detailed overview was provided on different parameter control and adaptation strategies by dividing them into the following four classes: empirical parameter settings,randomized parameter adaptation strategies,randomized parameter adaptation strategies with statistical learning,and parameter self-adaptation strategies.The overview emphasized the latter two classes.To study the efficacy of these parameter control and adaptation strategies,experiments with the background of real-valued function optimization were conducted to compare their efficiency and practicability further.The results showed that the parameter self-adaptation is one of the most effective strategies so far.更多还原