Abstract

In the evolutionary computing community, differential evolution (DE) is well appreciated as a simple yet versatile population-based, non-convex optimizer designed for continuous optimization problems. A simple two-phase DE algorithm is presented in this article, which aims to identify promising basins of attraction on a non-convex functional landscape in the first phase, and starting from those previously identified search regions, a success history-based switch parameter DE is employed to further fine tune the search process leading to the optima of the landscape. Our proposed framework has been validated on the well-known IEEE Congress on Evolutionary Computation (CEC) benchmark suites (CEC 2013, 2014 and 2017). Results of the proposed method are compared with corresponding CEC winners (SHADE for CEC 2013, L-SHADE for CEC 2014 and jSO for CEC 2017).

中文翻译：

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一个简单的两相微分演化算法，用于改进全局数值优化

摘要

在进化计算社区中，差分进化（DE）是一种为连续优化问题而设计的简单而通用的，基于总体的非凸优化器，已广受赞誉。本文提出了一种简单的两阶段DE算法，该算法旨在在第一阶段中确定非凸功能性景观上有希望的吸引盆地，并从先前确定的搜索区域开始，基于成功历史的开关参数DE用于进一步微调搜索过程，从而优化景观。我们提出的框架已在著名的IEEE进化计算大会（CEC）基准套件上得到验证（CEC 2013、2014和2017）。将该方法的结果与相应的CEC获奖者进行了比较（2013年CEC的SHADE，