Solving Multiobjective Optimization Problems Using Artificial Bee Colony Algorithm
Multiobjective optimization has been a difficult problem and focus for research in fields of science and engineering. This paper presents a novel algorithm based on artificial bee colony (ABC) to deal with multi-objective optimization problems. ABC is one of the most recently introduced algorithms b...
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| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Wiley
2011-01-01
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| Series: | Discrete Dynamics in Nature and Society |
| Online Access: | http://dx.doi.org/10.1155/2011/569784 |
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| _version_ | 1832550773971484672 |
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| author | Wenping Zou Yunlong Zhu Hanning Chen Beiwei Zhang |
| author_facet | Wenping Zou Yunlong Zhu Hanning Chen Beiwei Zhang |
| author_sort | Wenping Zou |
| collection | DOAJ |
| description | Multiobjective optimization has been a difficult problem and focus for research in fields of science and engineering. This paper presents a novel algorithm based on artificial bee colony (ABC) to deal with multi-objective optimization problems. ABC is one of the most recently introduced algorithms based on the intelligent foraging behavior of a honey bee swarm. It uses less control parameters, and it can be efficiently used for solving multimodal and multidimensional optimization problems. Our algorithm uses the concept of Pareto dominance to determine the flight direction of a bee, and it maintains nondominated solution vectors which have been found in an external archive. The proposed algorithm is validated using the standard test problems, and simulation results show that the proposed approach is highly competitive and can be considered a viable alternative to solve multi-objective optimization problems. |
| format | Article |
| id | doaj-art-dde0e264c3dd472fba446bd47b2d2c1f |
| institution | Kabale University |
| issn | 1026-0226 1607-887X |
| language | English |
| publishDate | 2011-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Discrete Dynamics in Nature and Society |
| spelling | doaj-art-dde0e264c3dd472fba446bd47b2d2c1f2025-02-03T06:05:56ZengWileyDiscrete Dynamics in Nature and Society1026-02261607-887X2011-01-01201110.1155/2011/569784569784Solving Multiobjective Optimization Problems Using Artificial Bee Colony AlgorithmWenping Zou0Yunlong Zhu1Hanning Chen2Beiwei Zhang3Key Laboratory of Industrial Informatics, Shenyang Institute of Automation, Chinese Academy of Sciences, Shenyang 110016, ChinaKey Laboratory of Industrial Informatics, Shenyang Institute of Automation, Chinese Academy of Sciences, Shenyang 110016, ChinaKey Laboratory of Industrial Informatics, Shenyang Institute of Automation, Chinese Academy of Sciences, Shenyang 110016, ChinaKey Laboratory of Industrial Informatics, Shenyang Institute of Automation, Chinese Academy of Sciences, Shenyang 110016, ChinaMultiobjective optimization has been a difficult problem and focus for research in fields of science and engineering. This paper presents a novel algorithm based on artificial bee colony (ABC) to deal with multi-objective optimization problems. ABC is one of the most recently introduced algorithms based on the intelligent foraging behavior of a honey bee swarm. It uses less control parameters, and it can be efficiently used for solving multimodal and multidimensional optimization problems. Our algorithm uses the concept of Pareto dominance to determine the flight direction of a bee, and it maintains nondominated solution vectors which have been found in an external archive. The proposed algorithm is validated using the standard test problems, and simulation results show that the proposed approach is highly competitive and can be considered a viable alternative to solve multi-objective optimization problems.http://dx.doi.org/10.1155/2011/569784 |
| spellingShingle | Wenping Zou Yunlong Zhu Hanning Chen Beiwei Zhang Solving Multiobjective Optimization Problems Using Artificial Bee Colony Algorithm Discrete Dynamics in Nature and Society |
| title | Solving Multiobjective Optimization Problems Using Artificial Bee Colony Algorithm |
| title_full | Solving Multiobjective Optimization Problems Using Artificial Bee Colony Algorithm |
| title_fullStr | Solving Multiobjective Optimization Problems Using Artificial Bee Colony Algorithm |
| title_full_unstemmed | Solving Multiobjective Optimization Problems Using Artificial Bee Colony Algorithm |
| title_short | Solving Multiobjective Optimization Problems Using Artificial Bee Colony Algorithm |
| title_sort | solving multiobjective optimization problems using artificial bee colony algorithm |
| url | http://dx.doi.org/10.1155/2011/569784 |
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