An Enhanced Multi-Population Ensemble Differential Evolution

Xiangping Li, Guangming Dai
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引用次数: 2

Abstract

MPEDE integrates multiple effective strategies to solve optimization problems. However, there is still some room to improve the optimization performance of it. In this work, we introduce an enhanced multi-population ensemble DE (eMPEDE). In the proposed algorithm, an improved mutation strategy "rand-to-mpbest/1" replaces "rand/1" in MPEDE to balance the exploration and exploitation, which utilizes multiple best solutions to guide searching. Moreover, an improved parameter adaptation method is employed to alleviate premature convergence by using success-history based adaptation. The experiments on CEC2005 benchmark problems are executed, including a comparison with other peer competitors. The experimental results reveal the capability of eMPEDE to generate more competitive results compared to MPEDE and other peer competitors.
一种增强的多种群集合差异进化
MPEDE集成了多种有效的策略来解决优化问题。但是,其优化性能仍有一定的提升空间。在这项工作中,我们引入了一种增强型多种群集合DE (eMPEDE)。在该算法中,改进的变异策略“rand-to-mpbest/1”取代了MPEDE中的“rand/1”策略,利用多个最优解指导搜索,实现了搜索和利用的平衡。此外,采用一种改进的参数自适应方法,通过基于成功历史的自适应来缓解过早收敛的问题。在CEC2005基准问题上进行了实验,并与其他竞争对手进行了比较。实验结果表明,与MPEDE和其他同类竞争者相比,eMPEDE能够产生更具竞争力的结果。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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