Improved multi-population gravitational search algorithm for dynamic optimization problems

被引：1

作者：

Bi, Xiaojun ^{[1
]}

Diao, Pengfei ^{[1
]}

Wang, Yanjiao ^{[2
]}

Xiao, Jing ^{[3
]}

机构：

[1] College of Information and Communication Engineering, Harbin Engineering University, Harbin

[2] College of Information Engineering, Northeast Dianli University, Jilin

[3] Department of Information Engineering, Liaoning Provincial College of Communications, Shenyang

来源：

Zhongnan Daxue Xuebao (Ziran Kexue Ban)/Journal of Central South University (Science and Technology) | 2015年 / 46卷 / 09期

基金：

高等学校博士学科点专项科研基金; 中国国家自然科学基金;

关键词：

Dynamic optimization problems (DOPs); Gravitational search algorithm (GSA); Multi-population strategy;

D O I：

10.11817/j.issn.1672-7207.2015.09.023

中图分类号：

学科分类号：

摘要：

To improve the redundant computing and low accuracy of solving dynamic optimization problems (DOPs) for multi-population algorithm, a novel improved multi-population gravitational search algorithm (IMGSA) was proposed. In IMGSA, the multi-population serial strategy was good for the present subpopulation to use evolutionary information of convergence population. A constraint initialization strategy was proposed to reduce the redundant computing which was generated by multiple populations searching repeatedly. Simultaneously, a distance decision strategy was used to stop multiple populations searching. Eventually, a monitoring and tracking strategy was used to monitor the environmental change and track the local peaks. The results show that IMGSA has a better performance in solving DOPs than those of other seven dynamic algorithms in different degree of environmental change or different peak number. It can prove the validity of proposed algorithm. © 2015, Central South University of Technology. All right reserved.

引用

页码：3325 / 3331

页数：6

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