期刊
COMPUTERS & OPERATIONS RESEARCH
卷 67, 期 -, 页码 155-173出版社
PERGAMON-ELSEVIER SCIENCE LTD
DOI: 10.1016/j.cor.2015.09.006
关键词
Multiple sub-populations; Adaptive parameter control; Replacement strategy; Differential evolution; Global optimization
类别
资金
- National Natural Science Foundation of China [61402294, 61170283, 61402291]
- National High-Technology Research and Development Program (863 Program) of China [2013AA01A212]
- Ministry of Education in the New Century Excellent Talents Support Program [NCET-12-0649]
- Guangdong Natural Science Foundation [S2013040012895]
- Foundation for Distinguished Young Talents in Higher Education of Guangdong, China [2013LYM_0076]
- Major Fundamental Research Project in the Science and Technology Plan of Shenzhen [JCYJ20130329102017840, JCYJ20130329102032059, JCYJ20140828163633977]
Differential evolution (DE) algorithm has been shown to be a very effective and efficient approach for solving global numerical optimization problems, which attracts a great attention of scientific researchers. Generally, most of DE algorithms only evolve one population by using certain kind of DE operators. However, as observed in nature, the working efficiency can be improved by using the concept of work specialization, in which the entire group should be divided into several sub-groups that are responsible for different tasks according to their capabilities. Inspired by this phenomenon, a novel adaptive multiple sub-populations based DE algorithm is designed in this paper, named MPADE, in which the parent population is split into three sub-populations based on the fitness values and then three novel DE strategies are respectively performed to take on the responsibility for either exploitation or exploration. Furthermore, a simple yet effective adaptive approach is designed for parameter adjustment in the three DE strategies and a replacement strategy is put forward to fully exploit the useful information from the trial vectors and target vectors, which enhance the optimization performance. In order to validate the effectiveness of MPADE, it is tested on 55 benchmark functions and 15 real world problems. When compared with other DE variants, MPADE performs better in most of benchmark problems and real-world problems. Moreover, the impacts of the MPADE components and their parameter sensitivity are also analyzed experimentally. (C) 2015 Elsevier Ltd. All rights reserved.
作者
我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。
推荐
暂无数据