Enhancing Artificial Bee Colony Algorithm with Self-Adaptive Searching Strategy and Artificial Immune Network Operators for Global Optimization
Artificial bee colony (ABC) algorithm, inspired by the intelligent foraging behavior of honey bees, was proposed by Karaboga. It has been shown to be superior to some conventional intelligent algorithms such as genetic algorithm (GA), artificial colony optimization (ACO), and particle swarm optimiza...
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| Format: | Article |
| Language: | English |
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Wiley
2014-01-01
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| Series: | The Scientific World Journal |
| Online Access: | http://dx.doi.org/10.1155/2014/438260 |
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| _version_ | 1832558531173154816 |
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| author | Tinggui Chen Renbin Xiao |
| author_facet | Tinggui Chen Renbin Xiao |
| author_sort | Tinggui Chen |
| collection | DOAJ |
| description | Artificial bee colony (ABC) algorithm, inspired by the intelligent foraging behavior of honey bees, was proposed by Karaboga. It has been shown to be superior to some conventional intelligent algorithms such as genetic algorithm (GA), artificial colony optimization (ACO), and particle swarm optimization (PSO). However, the ABC still has some limitations. For example, ABC can easily get trapped in the local optimum when handing in functions that have a narrow curving valley, a high eccentric ellipse, or complex multimodal functions. As a result, we proposed an enhanced ABC algorithm called EABC by introducing self-adaptive searching strategy and artificial immune network operators to improve the exploitation and exploration. The simulation results tested on a suite of unimodal or multimodal benchmark functions illustrate that the EABC algorithm outperforms ACO, PSO, and the basic ABC in most of the experiments. |
| format | Article |
| id | doaj-art-61f0427cb6614b85ba6d52bd9c1920c9 |
| institution | Kabale University |
| issn | 2356-6140 1537-744X |
| language | English |
| publishDate | 2014-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | The Scientific World Journal |
| spelling | doaj-art-61f0427cb6614b85ba6d52bd9c1920c92025-02-03T01:32:10ZengWileyThe Scientific World Journal2356-61401537-744X2014-01-01201410.1155/2014/438260438260Enhancing Artificial Bee Colony Algorithm with Self-Adaptive Searching Strategy and Artificial Immune Network Operators for Global OptimizationTinggui Chen0Renbin Xiao1College of Computer Science & Information Engineering, Zhejiang Gongshang University, Zhejiang Province, Hangzhou 310018, ChinaInstitute of Systems Engineering, Huazhong University of Science and Technology, Hubei Province, Wuhan 430074, ChinaArtificial bee colony (ABC) algorithm, inspired by the intelligent foraging behavior of honey bees, was proposed by Karaboga. It has been shown to be superior to some conventional intelligent algorithms such as genetic algorithm (GA), artificial colony optimization (ACO), and particle swarm optimization (PSO). However, the ABC still has some limitations. For example, ABC can easily get trapped in the local optimum when handing in functions that have a narrow curving valley, a high eccentric ellipse, or complex multimodal functions. As a result, we proposed an enhanced ABC algorithm called EABC by introducing self-adaptive searching strategy and artificial immune network operators to improve the exploitation and exploration. The simulation results tested on a suite of unimodal or multimodal benchmark functions illustrate that the EABC algorithm outperforms ACO, PSO, and the basic ABC in most of the experiments.http://dx.doi.org/10.1155/2014/438260 |
| spellingShingle | Tinggui Chen Renbin Xiao Enhancing Artificial Bee Colony Algorithm with Self-Adaptive Searching Strategy and Artificial Immune Network Operators for Global Optimization The Scientific World Journal |
| title | Enhancing Artificial Bee Colony Algorithm with Self-Adaptive Searching Strategy and Artificial Immune Network Operators for Global Optimization |
| title_full | Enhancing Artificial Bee Colony Algorithm with Self-Adaptive Searching Strategy and Artificial Immune Network Operators for Global Optimization |
| title_fullStr | Enhancing Artificial Bee Colony Algorithm with Self-Adaptive Searching Strategy and Artificial Immune Network Operators for Global Optimization |
| title_full_unstemmed | Enhancing Artificial Bee Colony Algorithm with Self-Adaptive Searching Strategy and Artificial Immune Network Operators for Global Optimization |
| title_short | Enhancing Artificial Bee Colony Algorithm with Self-Adaptive Searching Strategy and Artificial Immune Network Operators for Global Optimization |
| title_sort | enhancing artificial bee colony algorithm with self adaptive searching strategy and artificial immune network operators for global optimization |
| url | http://dx.doi.org/10.1155/2014/438260 |
| work_keys_str_mv | AT tingguichen enhancingartificialbeecolonyalgorithmwithselfadaptivesearchingstrategyandartificialimmunenetworkoperatorsforglobaloptimization AT renbinxiao enhancingartificialbeecolonyalgorithmwithselfadaptivesearchingstrategyandartificialimmunenetworkoperatorsforglobaloptimization |