论文标题
审查和基准测试参数控制方法差分进化
Reviewing and Benchmarking Parameter Control Methods in Differential Evolution
论文作者
论文摘要
已经提出了许多具有各种参数控制方法（PCM）的差分进化（DE）算法。但是，以前的研究通常认为PCM是复杂DE算法的组成部分。因此，对每种方法的特征和性能尚不理解。我们对DE的规模因子和交叉率和大规模基准研究的24个PCM进行了深入的评论。我们仔细地从其原始的复杂算法中提取24个PCM，并根据系统的方式描述它们。我们的审查促进了对现有的代表性PCM之间的相似性和差异的理解。在24个黑盒基准功能上研究了DES与24个PCM和16个变体操作员的性能。我们的基准测试结果表明，当在16个不同条件下嵌入标准化框架中时，哪些方法表现出高性能，与原始的复杂算法无关。我们还研究了通过将24种方法与基于甲骨文的模型进行比较，可以将PCM的进一步改进的空间进行，这可以被认为是最佳方法性能的保守下限。
Many Differential Evolution (DE) algorithms with various parameter control methods (PCMs) have been proposed. However, previous studies usually considered PCMs to be an integral component of a complex DE algorithm. Thus the characteristics and performance of each method are poorly understood. We present an in-depth review of 24 PCMs for the scale factor and crossover rate in DE and a large scale benchmarking study. We carefully extract the 24 PCMs from their original, complex algorithms and describe them according to a systematic manner. Our review facilitates the understanding of similarities and differences between existing, representative PCMs. The performance of DEs with the 24 PCMs and 16 variation operators is investigated on 24 black-box benchmark functions. Our benchmarking results reveal which methods exhibit high performance when embedded in a standardized framework under 16 different conditions, independent from their original, complex algorithms. We also investigate how much room there is for further improvement of PCMs by comparing the 24 methods with an oracle-based model, which can be considered to be a conservative lower bound on the performance of an optimal method.
