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Adaptive differential evolution with a new joint parameter adaptation method
Soft Computing ( IF 4.1 ) Pub Date : 2020-07-20 , DOI: 10.1007/s00500-020-05182-2
Miguel Leon , Ning Xiong

Differential evolution (DE) is a population-based metaheuristic algorithm that has been proved powerful in solving a wide range of real-parameter optimization tasks. However, the selection of the mutation strategy and control parameters in DE is problem dependent, and inappropriate specification of them will lead to poor performance of the algorithm such as slow convergence and early stagnation in a local optimum. This paper proposes a new method termed as Joint Adaptation of Parameters in DE (JAPDE). The key idea lies in dynamically updating the selection probabilities for a complete set of pairs of parameter generating functions based on feedback information acquired during the search by DE. Further, for mutation strategy adaptation, the Rank-Based Adaptation (RAM) method is utilized to facilitate the learning of multiple probability distributions, each of which corresponds to an interval of fitness ranks of individuals in the population. The coupling of RAM with JAPDE results in the new RAM-JAPDE algorithm that enables simultaneous adaptation of the selection probabilities for pairs of control parameters and mutation strategies in DE. The merit of RAM-JAPDE has been evaluated on the benchmark test suit proposed in CEC2014 in comparison to many well-known DE algorithms. The results of experiments demonstrate that the proposed RAM-JAPDE algorithm outperforms or is competitive to the other related DE variants that perform mutation strategy and control parameter adaptation, respectively.



中文翻译:

一种新的联合参数自适应方法的自适应差分进化

差分进化(DE)是一种基于种群的元启发式算法,已被证明在解决各种实参数优化任务方面功能强大。但是,DE中突变策略和控制参数的选择取决于问题,对它们的不适当说明会导致算法性能不佳,例如收敛速度慢和局部最优的早期停滞。本文提出了一种新的方法,称为DE中的参数联合自适应(JAPDE)。关键思想在于,根据DE搜索期间获取的反馈信息,动态更新完整的一对参数生成函数对的选择概率。此外,为了适应突变策略,利用基于秩的适应(RAM)方法来促进多种概率分布的学习,每个概率分布对应于人口中个体适应度等级的间隔。RAM与JAPDE的耦合产生了新的RAM-JAPDE算法,该算法可同时调整DE中控制参数对和突变策略的选择概率。与许多著名的DE算法相比,RAM-JAPDE的优点已在CEC2014中提出的基准测试套件上进行了评估。实验结果表明,所提出的RAM-JAPDE算法优于或胜过其他分别执行变异策略和控制参数自适应的DE变体。RAM与JAPDE的耦合产生了新的RAM-JAPDE算法,该算法可同时调整DE中控制参数对和突变策略的选择概率。与许多著名的DE算法相比,RAM-JAPDE的优点已在CEC2014中提出的基准测试套件上进行了评估。实验结果表明,所提出的RAM-JAPDE算法优于或胜过其他分别执行变异策略和控制参数自适应的DE变体。RAM与JAPDE的耦合产生了新的RAM-JAPDE算法,该算法可同时调整DE中控制参数对和突变策略的选择概率。与许多著名的DE算法相比,RAM-JAPDE的优点已在CEC2014中提出的基准测试套件上进行了评估。实验结果表明,所提出的RAM-JAPDE算法优于或胜过其他分别执行变异策略和控制参数自适应的相关DE变量。与许多著名的DE算法相比,RAM-JAPDE的优点已在CEC2014中提出的基准测试套件上进行了评估。实验结果表明,所提出的RAM-JAPDE算法优于或胜过其他分别执行变异策略和控制参数自适应的相关DE变量。与许多著名的DE算法相比,RAM-JAPDE的优点已经在CEC2014中提出的基准测试套件上进行了评估。实验结果表明,所提出的RAM-JAPDE算法优于或胜过其他分别执行变异策略和控制参数自适应的DE变体。

更新日期:2020-07-31
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