Differential Cloud Particles Evolution Algorithm Based on Data-Driven Mechanism for Applications of ANN
Computational scientists have designed many useful algorithms by exploring a biological process or imitating natural evolution. These algorithms can be used to solve engineering optimization problems. Inspired by the change of matter state, we proposed a novel optimization algorithm called different...
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Series: | Computational Intelligence and Neuroscience |
Online Access: | http://dx.doi.org/10.1155/2017/8469103 |
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doaj-88f22dae198c4a299b41ce18e712cbde2020-11-24T20:54:59ZengHindawi LimitedComputational Intelligence and Neuroscience1687-52651687-52732017-01-01201710.1155/2017/84691038469103Differential Cloud Particles Evolution Algorithm Based on Data-Driven Mechanism for Applications of ANNWei Li0School of Computer Science and Engineering, Xi’an University of Technology, Xi’an 710048, ChinaComputational scientists have designed many useful algorithms by exploring a biological process or imitating natural evolution. These algorithms can be used to solve engineering optimization problems. Inspired by the change of matter state, we proposed a novel optimization algorithm called differential cloud particles evolution algorithm based on data-driven mechanism (CPDD). In the proposed algorithm, the optimization process is divided into two stages, namely, fluid stage and solid stage. The algorithm carries out the strategy of integrating global exploration with local exploitation in fluid stage. Furthermore, local exploitation is carried out mainly in solid stage. The quality of the solution and the efficiency of the search are influenced greatly by the control parameters. Therefore, the data-driven mechanism is designed for obtaining better control parameters to ensure good performance on numerical benchmark problems. In order to verify the effectiveness of CPDD, numerical experiments are carried out on all the CEC2014 contest benchmark functions. Finally, two application problems of artificial neural network are examined. The experimental results show that CPDD is competitive with respect to other eight state-of-the-art intelligent optimization algorithms.http://dx.doi.org/10.1155/2017/8469103 |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Wei Li |
spellingShingle |
Wei Li Differential Cloud Particles Evolution Algorithm Based on Data-Driven Mechanism for Applications of ANN Computational Intelligence and Neuroscience |
author_facet |
Wei Li |
author_sort |
Wei Li |
title |
Differential Cloud Particles Evolution Algorithm Based on Data-Driven Mechanism for Applications of ANN |
title_short |
Differential Cloud Particles Evolution Algorithm Based on Data-Driven Mechanism for Applications of ANN |
title_full |
Differential Cloud Particles Evolution Algorithm Based on Data-Driven Mechanism for Applications of ANN |
title_fullStr |
Differential Cloud Particles Evolution Algorithm Based on Data-Driven Mechanism for Applications of ANN |
title_full_unstemmed |
Differential Cloud Particles Evolution Algorithm Based on Data-Driven Mechanism for Applications of ANN |
title_sort |
differential cloud particles evolution algorithm based on data-driven mechanism for applications of ann |
publisher |
Hindawi Limited |
series |
Computational Intelligence and Neuroscience |
issn |
1687-5265 1687-5273 |
publishDate |
2017-01-01 |
description |
Computational scientists have designed many useful algorithms by exploring a biological process or imitating natural evolution. These algorithms can be used to solve engineering optimization problems. Inspired by the change of matter state, we proposed a novel optimization algorithm called differential cloud particles evolution algorithm based on data-driven mechanism (CPDD). In the proposed algorithm, the optimization process is divided into two stages, namely, fluid stage and solid stage. The algorithm carries out the strategy of integrating global exploration with local exploitation in fluid stage. Furthermore, local exploitation is carried out mainly in solid stage. The quality of the solution and the efficiency of the search are influenced greatly by the control parameters. Therefore, the data-driven mechanism is designed for obtaining better control parameters to ensure good performance on numerical benchmark problems. In order to verify the effectiveness of CPDD, numerical experiments are carried out on all the CEC2014 contest benchmark functions. Finally, two application problems of artificial neural network are examined. The experimental results show that CPDD is competitive with respect to other eight state-of-the-art intelligent optimization algorithms. |
url |
http://dx.doi.org/10.1155/2017/8469103 |
work_keys_str_mv |
AT weili differentialcloudparticlesevolutionalgorithmbasedondatadrivenmechanismforapplicationsofann |
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1716793079333126144 |