Distributed Differential Evolution Based on Adaptive Mergence and Split for Large-Scale Optimization
Distributed Differential Evolution Based on Adaptive Mergence and Split for Large-Scale Optimization
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基于自适应合并和分裂的分布式差分进化大规模优化
DOI：
10.1109/tcyb.2017.2728725
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发表时间：
2018-07-01
影响因子：
11.8
通讯作者：
Zhang, Jun
中科院分区：
文献类型：
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作者：
Ge, Yong-Feng;Yu, Wei-Jie;Zhang, Jun
如今，大规模优化问题在许多研究领域中普遍存在。为了有效地处理此类问题，本文提出了一种子群体自适应合并和分裂的分布式差分进化（DDE-AMS）。新颖的合并和分裂算子旨在充分利用有限的种群资源，这对于大规模优化具有重要意义。它们是根据亚群的表现自适应地执行的。在进化过程中，一旦某个亚群找到了有希望的区域，当前表现最差的亚群就会融入其中。如果合并后的子群体不能持续提供有竞争力的解决方案，它将被分成两半。这样子种群的数量就得到了自适应调整，表现较好的子种群获得了更多的个体。这样，种群资源就可以在进化过程中针对亚种群进行适应性配置。此外，该算法采用并行主从方式实现。对 20 个广泛使用的大规模基准函数进行了大量的实验。实验结果表明，与几种最先进的算法相比，所提出的 DDE-AMS 可以实现具有竞争力甚至更好的性能。还研究了 DDE-AMS 组件、自适应行为、可扩展性和参数敏感性的影响。最后，我们研究了不同计算资源下 DDE-AMS 的加速比。
Nowadays, large-scale optimization problems are ubiquitous in many research fields. To deal with such problems efficiently, this paper proposes a distributed differential evolution with adaptive mergence and split (DDE-AMS) on subpopulations. The novel mergence and split operators are designed to make full use of limited population resource, which is important for large-scale optimization. They are adaptively performed based on the performance of the subpopulations. During the evolution, once a subpopulation finds a promising region, the current worst performing subpopulation will merge into it. If the merged subpopulation could not continuously provide competitive solutions, it will be split in half. In this way, the number of subpopulations is adaptively adjusted and better performing subpopulations obtain more individuals. Thus, population resource can be adaptively arranged for subpopulations during the evolution. Moreover, the proposed algorithm is implemented with a parallel master–slave manner. Extensive experiments are conducted on 20 widely used large-scale benchmark functions. Experimental results demonstrate that the proposed DDE-AMS could achieve competitive or even better performance compared with several state-of-the-art algorithms. The effects of DDE-AMS components, adaptive behavior, scalability, and parameter sensitivity are also studied. Finally, we investigate the speedup ratios of DDE-AMS with different computation resources.