Deep Evolution Algorithm Under Competitive and Cooperative Behavior
Combining depth with evolutionary algorithms, a deep evolutionary algorithm, the group competition cooperation optimization (GCCO) algorithm, is proposed. Firstly, the bio-group model is introduced to simulate the natural phenomenon that groups search prey. The algorithm can easily solve the optimiz...
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| Format: | Article |
| Language: | zho |
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Journal of Computer Engineering and Applications Beijing Co., Ltd., Science Press
2020-07-01
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| Series: | Jisuanji kexue yu tansuo |
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| Online Access: | http://fcst.ceaj.org/CN/abstract/abstract2260.shtml |
| _version_ | 1819146035264487424 |
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| author | CHEN Haijuan, FENG Xiang, YU Huiqun |
| author_facet | CHEN Haijuan, FENG Xiang, YU Huiqun |
| author_sort | CHEN Haijuan, FENG Xiang, YU Huiqun |
| collection | DOAJ |
| description | Combining depth with evolutionary algorithms, a deep evolutionary algorithm, the group competition cooperation optimization (GCCO) algorithm, is proposed. Firstly, the bio-group model is introduced to simulate the natural phenomenon that groups search prey. The algorithm can easily solve the optimization problem through multi-step iteration. In bio-group model, the follower adopts a variable step size region replication method to balance the convergence speed and optimization precision. The wanderer adopts random walk mode based on feature transfor-mation to avoid local optimization. Secondly, the introduction of competition model and cooperation model increases the complexity of the algorithm, and improves the search performance of the algorithm through competition and information sharing among groups. In addition, the mathematical model of the algorithm is derived from group theory, dynamics and imperial competition theory. The convergence of the algorithm is also verified theoretically. Finally, the performance of the proposed algorithm is tested through comparing it with the other three optimization algorithms on ten optimization benchmark functions. At the same time, GCCO achieves better results than other algorithms in setting up gas stations in Shanghai to improve the on-time rate. |
| first_indexed | 2024-12-22T13:07:31Z |
| format | Article |
| id | doaj.art-53242469a0ff4b9bbbea13be1172091d |
| institution | Directory Open Access Journal |
| issn | 1673-9418 |
| language | zho |
| last_indexed | 2024-12-22T13:07:31Z |
| publishDate | 2020-07-01 |
| publisher | Journal of Computer Engineering and Applications Beijing Co., Ltd., Science Press |
| record_format | Article |
| series | Jisuanji kexue yu tansuo |
| spelling | doaj.art-53242469a0ff4b9bbbea13be1172091d2022-12-21T18:24:49ZzhoJournal of Computer Engineering and Applications Beijing Co., Ltd., Science PressJisuanji kexue yu tansuo1673-94182020-07-011471114112510.3778/j.issn.1673-9418.1905088Deep Evolution Algorithm Under Competitive and Cooperative BehaviorCHEN Haijuan, FENG Xiang, YU Huiqun01. School of Information Science and Engineering, East China University of Science and Technology, Shanghai 200237, China 2. Shanghai Engineering Research Center of Smart Energy, Shanghai 200237, ChinaCombining depth with evolutionary algorithms, a deep evolutionary algorithm, the group competition cooperation optimization (GCCO) algorithm, is proposed. Firstly, the bio-group model is introduced to simulate the natural phenomenon that groups search prey. The algorithm can easily solve the optimization problem through multi-step iteration. In bio-group model, the follower adopts a variable step size region replication method to balance the convergence speed and optimization precision. The wanderer adopts random walk mode based on feature transfor-mation to avoid local optimization. Secondly, the introduction of competition model and cooperation model increases the complexity of the algorithm, and improves the search performance of the algorithm through competition and information sharing among groups. In addition, the mathematical model of the algorithm is derived from group theory, dynamics and imperial competition theory. The convergence of the algorithm is also verified theoretically. Finally, the performance of the proposed algorithm is tested through comparing it with the other three optimization algorithms on ten optimization benchmark functions. At the same time, GCCO achieves better results than other algorithms in setting up gas stations in Shanghai to improve the on-time rate.http://fcst.ceaj.org/CN/abstract/abstract2260.shtmldeep evolutionfeature transformationcompetition modelcooperation model |
| spellingShingle | CHEN Haijuan, FENG Xiang, YU Huiqun Deep Evolution Algorithm Under Competitive and Cooperative Behavior Jisuanji kexue yu tansuo deep evolution feature transformation competition model cooperation model |
| title | Deep Evolution Algorithm Under Competitive and Cooperative Behavior |
| title_full | Deep Evolution Algorithm Under Competitive and Cooperative Behavior |
| title_fullStr | Deep Evolution Algorithm Under Competitive and Cooperative Behavior |
| title_full_unstemmed | Deep Evolution Algorithm Under Competitive and Cooperative Behavior |
| title_short | Deep Evolution Algorithm Under Competitive and Cooperative Behavior |
| title_sort | deep evolution algorithm under competitive and cooperative behavior |
| topic | deep evolution feature transformation competition model cooperation model |
| url | http://fcst.ceaj.org/CN/abstract/abstract2260.shtml |
| work_keys_str_mv | AT chenhaijuanfengxiangyuhuiqun deepevolutionalgorithmundercompetitiveandcooperativebehavior |