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A simple two-phase differential evolution for improved global numerical optimization
Soft Computing ( IF 3.1 ) Pub Date : 2020-02-18 , DOI: 10.1007/s00500-020-04750-w
Arka Ghosh , Swagatam Das , Asit Kr. Das

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:

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,

更新日期:2020-03-24
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