An Improved Brain Storm Optimization with Differential Evolution Strategy for Applications of ANNs
Brain Storm Optimization (BSO) algorithm is a swarm intelligence algorithm inspired by human being’s behavior of brainstorming. The performance of BSO is maintained by the creating process of ideas, but when it cannot find a better solution for some successive iterations, the result will be so ineff...
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Series: | Mathematical Problems in Engineering |
Online Access: | http://dx.doi.org/10.1155/2015/923698 |
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doaj-b408bfffc97949feacaeff91184672a72020-11-24T22:55:57ZengHindawi LimitedMathematical Problems in Engineering1024-123X1563-51472015-01-01201510.1155/2015/923698923698An Improved Brain Storm Optimization with Differential Evolution Strategy for Applications of ANNsZijian Cao0Xinhong Hei1Lei Wang2Yuhui Shi3Xiaofeng Rong4School of Computer Science and Engineering, Xi’an University of Technology, Xi’an 710048, ChinaSchool of Computer Science and Engineering, Xi’an University of Technology, Xi’an 710048, ChinaSchool of Computer Science and Engineering, Xi’an University of Technology, Xi’an 710048, ChinaXi’an Jiaotong-Liverpool University, Suzhou 215123, ChinaSchool of Computer Science and Engineering, Xi’an Technological University, Xi’an 710021, ChinaBrain Storm Optimization (BSO) algorithm is a swarm intelligence algorithm inspired by human being’s behavior of brainstorming. The performance of BSO is maintained by the creating process of ideas, but when it cannot find a better solution for some successive iterations, the result will be so inefficient that the population might be trapped into local optima. In this paper, we propose an improved BSO algorithm with differential evolution strategy and new step size method. Firstly, differential evolution strategy is incorporated into the creating operator of ideas to allow BSO jump out of stagnation, owing to its strong searching ability. Secondly, we introduce a new step size control method that can better balance exploration and exploitation at different searching generations. Finally, the proposed algorithm is first tested on 14 benchmark functions of CEC 2005 and then is applied to train artificial neural networks. Comparative experimental results illustrate that the proposed algorithm performs significantly better than the original BSO.http://dx.doi.org/10.1155/2015/923698 |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Zijian Cao Xinhong Hei Lei Wang Yuhui Shi Xiaofeng Rong |
spellingShingle |
Zijian Cao Xinhong Hei Lei Wang Yuhui Shi Xiaofeng Rong An Improved Brain Storm Optimization with Differential Evolution Strategy for Applications of ANNs Mathematical Problems in Engineering |
author_facet |
Zijian Cao Xinhong Hei Lei Wang Yuhui Shi Xiaofeng Rong |
author_sort |
Zijian Cao |
title |
An Improved Brain Storm Optimization with Differential Evolution Strategy for Applications of ANNs |
title_short |
An Improved Brain Storm Optimization with Differential Evolution Strategy for Applications of ANNs |
title_full |
An Improved Brain Storm Optimization with Differential Evolution Strategy for Applications of ANNs |
title_fullStr |
An Improved Brain Storm Optimization with Differential Evolution Strategy for Applications of ANNs |
title_full_unstemmed |
An Improved Brain Storm Optimization with Differential Evolution Strategy for Applications of ANNs |
title_sort |
improved brain storm optimization with differential evolution strategy for applications of anns |
publisher |
Hindawi Limited |
series |
Mathematical Problems in Engineering |
issn |
1024-123X 1563-5147 |
publishDate |
2015-01-01 |
description |
Brain Storm Optimization (BSO) algorithm is a swarm intelligence algorithm inspired by human being’s behavior of brainstorming. The performance of BSO is maintained by the creating process of ideas, but when it cannot find a better solution for some successive iterations, the result will be so inefficient that the population might be trapped into local optima. In this paper, we propose an improved BSO algorithm with differential evolution strategy and new step size method. Firstly, differential evolution strategy is incorporated into the creating operator of ideas to allow BSO jump out of stagnation, owing to its strong searching ability. Secondly, we introduce a new step size control method that can better balance exploration and exploitation at different searching generations. Finally, the proposed algorithm is first tested on 14 benchmark functions of CEC 2005 and then is applied to train artificial neural networks. Comparative experimental results illustrate that the proposed algorithm performs significantly better than the original BSO. |
url |
http://dx.doi.org/10.1155/2015/923698 |
work_keys_str_mv |
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