Novel Adaptive Bacteria Foraging Algorithms for Global Optimization
This paper presents improved versions of bacterial foraging algorithm (BFA). The chemotaxis feature of bacteria through random motion is an effective strategy for exploring the optimum point in a search area. The selection of small step size value in the bacteria motion leads to high accuracy in the...
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| Format: | Article |
| Language: | English |
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Wiley
2014-01-01
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| Series: | Applied Computational Intelligence and Soft Computing |
| Online Access: | http://dx.doi.org/10.1155/2014/494271 |
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| _version_ | 1832558509886013440 |
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| author | Ahmad N. K. Nasir M. O. Tokhi N. Maniha Abd. Ghani |
| author_facet | Ahmad N. K. Nasir M. O. Tokhi N. Maniha Abd. Ghani |
| author_sort | Ahmad N. K. Nasir |
| collection | DOAJ |
| description | This paper presents improved versions of bacterial foraging algorithm (BFA). The chemotaxis feature of bacteria through random motion is an effective strategy for exploring the optimum point in a search area. The selection of small step size value in the bacteria motion leads to high accuracy in the solution but it offers slow convergence. On the contrary, defining a large step size in the motion provides faster convergence but the bacteria will be unable to locate the optimum point hence reducing the fitness accuracy. In order to overcome such problems, novel linear and nonlinear mathematical relationships based on the index of iteration, index of bacteria, and fitness cost are adopted which can dynamically vary the step size of bacteria movement. The proposed algorithms are tested with several unimodal and multimodal benchmark functions in comparison with the original BFA. Moreover, the application of the proposed algorithms in modelling of a twin rotor system is presented. The results show that the proposed algorithms outperform the predecessor algorithm in all test functions and acquire better model for the twin rotor system. |
| format | Article |
| id | doaj-art-30913583064046a1a1890de6c0e13828 |
| institution | Kabale University |
| issn | 1687-9724 1687-9732 |
| language | English |
| publishDate | 2014-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Applied Computational Intelligence and Soft Computing |
| spelling | doaj-art-30913583064046a1a1890de6c0e138282025-02-03T01:32:08ZengWileyApplied Computational Intelligence and Soft Computing1687-97241687-97322014-01-01201410.1155/2014/494271494271Novel Adaptive Bacteria Foraging Algorithms for Global OptimizationAhmad N. K. Nasir0M. O. Tokhi1N. Maniha Abd. Ghani2Department of Automatic Control & Systems Engineering, The University of Sheffield, Sheffield S1 3JD, UKDepartment of Automatic Control & Systems Engineering, The University of Sheffield, Sheffield S1 3JD, UKDepartment of Automatic Control & Systems Engineering, The University of Sheffield, Sheffield S1 3JD, UKThis paper presents improved versions of bacterial foraging algorithm (BFA). The chemotaxis feature of bacteria through random motion is an effective strategy for exploring the optimum point in a search area. The selection of small step size value in the bacteria motion leads to high accuracy in the solution but it offers slow convergence. On the contrary, defining a large step size in the motion provides faster convergence but the bacteria will be unable to locate the optimum point hence reducing the fitness accuracy. In order to overcome such problems, novel linear and nonlinear mathematical relationships based on the index of iteration, index of bacteria, and fitness cost are adopted which can dynamically vary the step size of bacteria movement. The proposed algorithms are tested with several unimodal and multimodal benchmark functions in comparison with the original BFA. Moreover, the application of the proposed algorithms in modelling of a twin rotor system is presented. The results show that the proposed algorithms outperform the predecessor algorithm in all test functions and acquire better model for the twin rotor system.http://dx.doi.org/10.1155/2014/494271 |
| spellingShingle | Ahmad N. K. Nasir M. O. Tokhi N. Maniha Abd. Ghani Novel Adaptive Bacteria Foraging Algorithms for Global Optimization Applied Computational Intelligence and Soft Computing |
| title | Novel Adaptive Bacteria Foraging Algorithms for Global Optimization |
| title_full | Novel Adaptive Bacteria Foraging Algorithms for Global Optimization |
| title_fullStr | Novel Adaptive Bacteria Foraging Algorithms for Global Optimization |
| title_full_unstemmed | Novel Adaptive Bacteria Foraging Algorithms for Global Optimization |
| title_short | Novel Adaptive Bacteria Foraging Algorithms for Global Optimization |
| title_sort | novel adaptive bacteria foraging algorithms for global optimization |
| url | http://dx.doi.org/10.1155/2014/494271 |
| work_keys_str_mv | AT ahmadnknasir noveladaptivebacteriaforagingalgorithmsforglobaloptimization AT motokhi noveladaptivebacteriaforagingalgorithmsforglobaloptimization AT nmanihaabdghani noveladaptivebacteriaforagingalgorithmsforglobaloptimization |