Artificial Bee Colony Algorithm with Time-Varying Strategy
Artificial bee colony (ABC) is one of the newest additions to the class of swarm intelligence. ABC algorithm has been shown to be competitive with some other population-based algorithms. However, there is still an insufficiency that ABC is good at exploration but poor at exploitation. To make a prop...
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| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Wiley
2015-01-01
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| Series: | Discrete Dynamics in Nature and Society |
| Online Access: | http://dx.doi.org/10.1155/2015/674595 |
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| _version_ | 1850104700711469056 |
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| author | Quande Qin Shi Cheng Qingyu Zhang Li Li Yuhui Shi |
| author_facet | Quande Qin Shi Cheng Qingyu Zhang Li Li Yuhui Shi |
| author_sort | Quande Qin |
| collection | DOAJ |
| description | Artificial bee colony (ABC) is one of the newest additions to the class of swarm intelligence. ABC algorithm has been shown to be competitive with some other population-based algorithms. However, there is still an insufficiency that ABC is good at exploration but poor at exploitation. To make a proper balance between these two conflictive factors, this paper proposed a novel ABC variant with a time-varying strategy where the ratio between the number of employed bees and the number of onlooker bees varies with time. The linear and nonlinear time-varying strategies can be incorporated into the basic ABC algorithm, yielding ABC-LTVS and ABC-NTVS algorithms, respectively. The effects of the added parameters in the two new ABC algorithms are also studied through solving some representative benchmark functions. The proposed ABC algorithm is a simple and easy modification to the structure of the basic ABC algorithm. Moreover, the proposed approach is general and can be incorporated in other ABC variants. A set of 21 benchmark functions in 30 and 50 dimensions are utilized in the experimental studies. The experimental results show the effectiveness of the proposed time-varying strategy. |
| format | Article |
| id | doaj-art-41d5ce60caac4f3fbaa9ea91b505ea16 |
| institution | DOAJ |
| issn | 1026-0226 1607-887X |
| language | English |
| publishDate | 2015-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Discrete Dynamics in Nature and Society |
| spelling | doaj-art-41d5ce60caac4f3fbaa9ea91b505ea162025-08-20T02:39:16ZengWileyDiscrete Dynamics in Nature and Society1026-02261607-887X2015-01-01201510.1155/2015/674595674595Artificial Bee Colony Algorithm with Time-Varying StrategyQuande Qin0Shi Cheng1Qingyu Zhang2Li Li3Yuhui Shi4Department of Management Science, College of Management, Shenzhen University, Shenzhen 518060, ChinaDivision of Computer Science, University of Nottingham Ningbo China, Ningbo 315100, ChinaDepartment of Management Science, College of Management, Shenzhen University, Shenzhen 518060, ChinaDepartment of Management Science, College of Management, Shenzhen University, Shenzhen 518060, ChinaDepartment of Electrical & Electronic Engineering, Xi’an Jiaotong-Liverpool University, Suzhou 215123, ChinaArtificial bee colony (ABC) is one of the newest additions to the class of swarm intelligence. ABC algorithm has been shown to be competitive with some other population-based algorithms. However, there is still an insufficiency that ABC is good at exploration but poor at exploitation. To make a proper balance between these two conflictive factors, this paper proposed a novel ABC variant with a time-varying strategy where the ratio between the number of employed bees and the number of onlooker bees varies with time. The linear and nonlinear time-varying strategies can be incorporated into the basic ABC algorithm, yielding ABC-LTVS and ABC-NTVS algorithms, respectively. The effects of the added parameters in the two new ABC algorithms are also studied through solving some representative benchmark functions. The proposed ABC algorithm is a simple and easy modification to the structure of the basic ABC algorithm. Moreover, the proposed approach is general and can be incorporated in other ABC variants. A set of 21 benchmark functions in 30 and 50 dimensions are utilized in the experimental studies. The experimental results show the effectiveness of the proposed time-varying strategy.http://dx.doi.org/10.1155/2015/674595 |
| spellingShingle | Quande Qin Shi Cheng Qingyu Zhang Li Li Yuhui Shi Artificial Bee Colony Algorithm with Time-Varying Strategy Discrete Dynamics in Nature and Society |
| title | Artificial Bee Colony Algorithm with Time-Varying Strategy |
| title_full | Artificial Bee Colony Algorithm with Time-Varying Strategy |
| title_fullStr | Artificial Bee Colony Algorithm with Time-Varying Strategy |
| title_full_unstemmed | Artificial Bee Colony Algorithm with Time-Varying Strategy |
| title_short | Artificial Bee Colony Algorithm with Time-Varying Strategy |
| title_sort | artificial bee colony algorithm with time varying strategy |
| url | http://dx.doi.org/10.1155/2015/674595 |
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