Scale factor inheritance mechanism in distributed differential evolution
Scale factor inheritance mechanism in distributed differential evolution
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DOI：
10.1007/s00500-009-0510-5
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发表时间：
2009-10
期刊：
影响因子：
4.1
通讯作者：
Matthieu Weber;V. Tirronen;Ferrante Neri
中科院分区：
文献类型：
--
作者：
Matthieu Weber;V. Tirronen;Ferrante Neri
本文提出了一种分布式差分进化，它采用了一种新颖的自适应方案，即比例因子继承。在所提出的算法中，群体分布在根据环形拓扑分配的几个子群体中。每个子群都由其自己的比例因子值来表征。根据概率标准，表现出最佳性能的个体被迁移到邻近群体，并替换目标子群体中伪随机选择的个体。目标亚群不仅继承了该个体，而且还继承了尺度因子（如果在当前的进化阶段看来有希望的话）。此外，扰动机制增强了算法的探索特性。所提出的算法已经在一组各种测试问题上运行，然后与文献中最近提出的两种顺序差分进化算法和三种分布式差分进化算法进行了比较，这些算法代表了该领域的最新技术。数值结果表明，所提出的方法对于大多数分析问题似乎都非常有效，并且优于本研究中考虑的所有其他算法。
This article proposes a distributed differential evolution which employs a novel self-adaptive scheme, namely scale factor inheritance. In the proposed algorithm, the population is distributed over several sub-populations allocated according to a ring topology. Each sub-population is characterized by its own scale factor value. With a probabilistic criterion, that individual displaying the best performance is migrated to the neighbor population and replaces a pseudo-randomly selected individual of the target sub-population. The target sub-population inherits not only this individual but also the scale factor if it seems promising at the current stage of evolution. In addition, a perturbation mechanism enhances the exploration feature of the algorithm. The proposed algorithm has been run on a set of various test problems and then compared to two sequential differential evolution algorithms and three distributed differential evolution algorithms recently proposed in literature and representing state-of-the-art in the field. Numerical results show that the proposed approach seems very efficient for most of the analyzed problems, and outperforms all other algorithms considered in this study.