Restricted Boltzmann Machine-Assisted Estimation of Distribution Algorithm for Complex Problems
A novel algorithm, called restricted Boltzmann machine-assisted estimation of distribution algorithm, is proposed for solving computationally expensive optimization problems with discrete variables. First, the individuals are evaluated using expensive fitness functions of the complex problems, and s...
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| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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Wiley
2018-01-01
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| Series: | Complexity |
| Online Access: | http://dx.doi.org/10.1155/2018/2609014 |
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| _version_ | 1832547423247925248 |
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| author | Lin Bao Xiaoyan Sun Yang Chen Guangyi Man Hui Shao |
| author_facet | Lin Bao Xiaoyan Sun Yang Chen Guangyi Man Hui Shao |
| author_sort | Lin Bao |
| collection | DOAJ |
| description | A novel algorithm, called restricted Boltzmann machine-assisted estimation of distribution algorithm, is proposed for solving computationally expensive optimization problems with discrete variables. First, the individuals are evaluated using expensive fitness functions of the complex problems, and some dominant solutions are selected to construct the surrogate model. The restricted Boltzmann machine (RBM) is built and trained with the dominant solutions to implicitly extract the distributed representative information of the decision variables in the promising subset. The visible layer’s probability of the RBM is designed as the sampling probability model of the estimation of distribution algorithm (EDA) and is updated dynamically along with the update of the dominant subsets. Second, according to the energy function of the RBM, a fitness surrogate is developed to approximate the expensive individual fitness evaluations and participates in the evolutionary process to reduce the computational cost. Finally, model management is developed to train and update the RBM model with newly dominant solutions. A comparison of the proposed algorithm with several state-of-the-art surrogate-assisted evolutionary algorithms demonstrates that the proposed algorithm effectively and efficiently solves complex optimization problems with smaller computational cost. |
| format | Article |
| id | doaj-art-3a4f351aafc744598e8dab5a6145810c |
| institution | Kabale University |
| issn | 1076-2787 1099-0526 |
| language | English |
| publishDate | 2018-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Complexity |
| spelling | doaj-art-3a4f351aafc744598e8dab5a6145810c2025-02-03T06:44:39ZengWileyComplexity1076-27871099-05262018-01-01201810.1155/2018/26090142609014Restricted Boltzmann Machine-Assisted Estimation of Distribution Algorithm for Complex ProblemsLin Bao0Xiaoyan Sun1Yang Chen2Guangyi Man3Hui Shao4School of Information and Control Engineering, China University of Mining and Technology, Xuzhou, Jiangsu, ChinaSchool of Information and Control Engineering, China University of Mining and Technology, Xuzhou, Jiangsu, ChinaSchool of Information and Control Engineering, China University of Mining and Technology, Xuzhou, Jiangsu, ChinaSchool of Information and Control Engineering, China University of Mining and Technology, Xuzhou, Jiangsu, ChinaSchool of Information and Control Engineering, China University of Mining and Technology, Xuzhou, Jiangsu, ChinaA novel algorithm, called restricted Boltzmann machine-assisted estimation of distribution algorithm, is proposed for solving computationally expensive optimization problems with discrete variables. First, the individuals are evaluated using expensive fitness functions of the complex problems, and some dominant solutions are selected to construct the surrogate model. The restricted Boltzmann machine (RBM) is built and trained with the dominant solutions to implicitly extract the distributed representative information of the decision variables in the promising subset. The visible layer’s probability of the RBM is designed as the sampling probability model of the estimation of distribution algorithm (EDA) and is updated dynamically along with the update of the dominant subsets. Second, according to the energy function of the RBM, a fitness surrogate is developed to approximate the expensive individual fitness evaluations and participates in the evolutionary process to reduce the computational cost. Finally, model management is developed to train and update the RBM model with newly dominant solutions. A comparison of the proposed algorithm with several state-of-the-art surrogate-assisted evolutionary algorithms demonstrates that the proposed algorithm effectively and efficiently solves complex optimization problems with smaller computational cost.http://dx.doi.org/10.1155/2018/2609014 |
| spellingShingle | Lin Bao Xiaoyan Sun Yang Chen Guangyi Man Hui Shao Restricted Boltzmann Machine-Assisted Estimation of Distribution Algorithm for Complex Problems Complexity |
| title | Restricted Boltzmann Machine-Assisted Estimation of Distribution Algorithm for Complex Problems |
| title_full | Restricted Boltzmann Machine-Assisted Estimation of Distribution Algorithm for Complex Problems |
| title_fullStr | Restricted Boltzmann Machine-Assisted Estimation of Distribution Algorithm for Complex Problems |
| title_full_unstemmed | Restricted Boltzmann Machine-Assisted Estimation of Distribution Algorithm for Complex Problems |
| title_short | Restricted Boltzmann Machine-Assisted Estimation of Distribution Algorithm for Complex Problems |
| title_sort | restricted boltzmann machine assisted estimation of distribution algorithm for complex problems |
| url | http://dx.doi.org/10.1155/2018/2609014 |
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