An Adaptive Bacterial Foraging Optimization Algorithm with Lifecycle and Social Learning
Bacterial Foraging Algorithm (BFO) is a recently proposed swarm intelligence algorithm inspired by the foraging and chemotactic phenomenon of bacteria. However, its optimization ability is not so good compared with other classic algorithms as it has several shortages. This paper presents an improved...
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| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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Wiley
2012-01-01
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| Series: | Discrete Dynamics in Nature and Society |
| Online Access: | http://dx.doi.org/10.1155/2012/409478 |
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| _version_ | 1832552045344718848 |
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| author | Xiaohui Yan Yunlong Zhu Hao Zhang Hanning Chen Ben Niu |
| author_facet | Xiaohui Yan Yunlong Zhu Hao Zhang Hanning Chen Ben Niu |
| author_sort | Xiaohui Yan |
| collection | DOAJ |
| description | Bacterial Foraging Algorithm (BFO) is a recently proposed swarm intelligence algorithm inspired by the foraging and chemotactic phenomenon of bacteria. However, its optimization ability is not so good compared with other classic algorithms as it has several shortages. This paper presents an improved BFO Algorithm. In the new algorithm, a lifecycle model of bacteria is founded. The bacteria could split, die, or migrate dynamically in the foraging processes, and population size varies as the algorithm runs. Social learning is also introduced so that the bacteria will tumble towards better directions in the chemotactic steps. Besides, adaptive step lengths are employed in chemotaxis. The new algorithm is named BFOLS and it is tested on a set of benchmark functions with dimensions of 2 and 20. Canonical BFO, PSO, and GA algorithms are employed for comparison. Experiment results and statistic analysis show that the BFOLS algorithm offers significant improvements than original BFO algorithm. Particulary with dimension of 20, it has the best performance among the four algorithms. |
| format | Article |
| id | doaj-art-c38168a01dfc4fa1955ed3a33a658b77 |
| institution | Kabale University |
| issn | 1026-0226 1607-887X |
| language | English |
| publishDate | 2012-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Discrete Dynamics in Nature and Society |
| spelling | doaj-art-c38168a01dfc4fa1955ed3a33a658b772025-02-03T05:59:52ZengWileyDiscrete Dynamics in Nature and Society1026-02261607-887X2012-01-01201210.1155/2012/409478409478An Adaptive Bacterial Foraging Optimization Algorithm with Lifecycle and Social LearningXiaohui Yan0Yunlong Zhu1Hao Zhang2Hanning Chen3Ben Niu4Department of Information Service & Intelligent Control, Shenyang Institute of Automation, Chinese Academy of Sciences, Shenyang 110016, ChinaDepartment of Information Service & Intelligent Control, Shenyang Institute of Automation, Chinese Academy of Sciences, Shenyang 110016, ChinaDepartment of Information Service & Intelligent Control, Shenyang Institute of Automation, Chinese Academy of Sciences, Shenyang 110016, ChinaDepartment of Information Service & Intelligent Control, Shenyang Institute of Automation, Chinese Academy of Sciences, Shenyang 110016, ChinaCollege of Management, Shenzhen University, Shenzhen 518060, ChinaBacterial Foraging Algorithm (BFO) is a recently proposed swarm intelligence algorithm inspired by the foraging and chemotactic phenomenon of bacteria. However, its optimization ability is not so good compared with other classic algorithms as it has several shortages. This paper presents an improved BFO Algorithm. In the new algorithm, a lifecycle model of bacteria is founded. The bacteria could split, die, or migrate dynamically in the foraging processes, and population size varies as the algorithm runs. Social learning is also introduced so that the bacteria will tumble towards better directions in the chemotactic steps. Besides, adaptive step lengths are employed in chemotaxis. The new algorithm is named BFOLS and it is tested on a set of benchmark functions with dimensions of 2 and 20. Canonical BFO, PSO, and GA algorithms are employed for comparison. Experiment results and statistic analysis show that the BFOLS algorithm offers significant improvements than original BFO algorithm. Particulary with dimension of 20, it has the best performance among the four algorithms.http://dx.doi.org/10.1155/2012/409478 |
| spellingShingle | Xiaohui Yan Yunlong Zhu Hao Zhang Hanning Chen Ben Niu An Adaptive Bacterial Foraging Optimization Algorithm with Lifecycle and Social Learning Discrete Dynamics in Nature and Society |
| title | An Adaptive Bacterial Foraging Optimization Algorithm with Lifecycle and Social Learning |
| title_full | An Adaptive Bacterial Foraging Optimization Algorithm with Lifecycle and Social Learning |
| title_fullStr | An Adaptive Bacterial Foraging Optimization Algorithm with Lifecycle and Social Learning |
| title_full_unstemmed | An Adaptive Bacterial Foraging Optimization Algorithm with Lifecycle and Social Learning |
| title_short | An Adaptive Bacterial Foraging Optimization Algorithm with Lifecycle and Social Learning |
| title_sort | adaptive bacterial foraging optimization algorithm with lifecycle and social learning |
| url | http://dx.doi.org/10.1155/2012/409478 |
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