Parameter Selection for Ant Colony Algorithm Based on Bacterial Foraging Algorithm

The optimal performance of the ant colony algorithm (ACA) mainly depends on suitable parameters; therefore, parameter selection for ACA is important. We propose a parameter selection method for ACA based on the bacterial foraging algorithm (BFA), considering the effects of coupling between different...

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Published in:	Mathematical problems in engineering Vol. 2016; no. 2016; pp. 1 - 12
Main Authors:	Li, Peng, Zhu, Hua
Format:	Journal Article
Language:	English
Published:	Cairo, Egypt Hindawi Publishing Corporation 01.01.2016 John Wiley & Sons, Inc
Subjects:	Algorithms Ant colony optimization Bacteria Formicidae Genetic algorithms Global optimization Mathematical analysis Methods Operators Optimization Parameters Particle swarm optimization Robots Standard deviation
ISSN:	1024-123X, 1563-5147
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Abstract	The optimal performance of the ant colony algorithm (ACA) mainly depends on suitable parameters; therefore, parameter selection for ACA is important. We propose a parameter selection method for ACA based on the bacterial foraging algorithm (BFA), considering the effects of coupling between different parameters. Firstly, parameters for ACA are mapped into a multidimensional space, using a chemotactic operator to ensure that each parameter group approaches the optimal value, speeding up the convergence for each parameter set. Secondly, the operation speed for optimizing the entire parameter set is accelerated using a reproduction operator. Finally, the elimination-dispersal operator is used to strengthen the global optimization of the parameters, which avoids falling into a local optimal solution. In order to validate the effectiveness of this method, the results were compared with those using a genetic algorithm (GA) and a particle swarm optimization (PSO), and simulations were conducted using different grid maps for robot path planning. The results indicated that parameter selection for ACA based on BFA was the superior method, able to determine the best parameter combination rapidly, accurately, and effectively.
AbstractList	The optimal performance of the ant colony algorithm (ACA) mainly depends on suitable parameters; therefore, parameter selection for ACA is important. We propose a parameter selection method for ACA based on the bacterial foraging algorithm (BFA), considering the effects of coupling between different parameters. Firstly, parameters for ACA are mapped into a multidimensional space, using a chemotactic operator to ensure that each parameter group approaches the optimal value, speeding up the convergence for each parameter set. Secondly, the operation speed for optimizing the entire parameter set is accelerated using a reproduction operator. Finally, the elimination-dispersal operator is used to strengthen the global optimization of the parameters, which avoids falling into a local optimal solution. In order to validate the effectiveness of this method, the results were compared with those using a genetic algorithm (GA) and a particle swarm optimization (PSO), and simulations were conducted using different grid maps for robot path planning. The results indicated that parameter selection for ACA based on BFA was the superior method, able to determine the best parameter combination rapidly, accurately, and effectively.
Author	Zhu, Hua Li, Peng
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CitedBy_id	crossref_primary_10_1016_j_apacoust_2021_108078 crossref_primary_10_1007_s42235_023_00414_1 crossref_primary_10_1016_j_scs_2022_103682 crossref_primary_10_1080_0954898X_2018_1539260 crossref_primary_10_1007_s11042_021_10986_x crossref_primary_10_1109_ACCESS_2023_3304708
Cites_doi	10.1109/MCS.2002.1004010 10.1007/s11771-013-1864-5 10.1016/j.cor.2006.07.003 10.1155/2014/368208 10.4156/jcit.vol6.issue9.12 10.1360/aas-007-0279 10.1007/s11045-011-0164-1 10.1155/2016/7672839 10.1007/s11280-015-0369-6 10.1109/3477.484436 10.1080/0951192x.2015.1130240 10.1023/b:jmma.0000038614.39977.6f 10.1016/j.ijar.2015.11.003 10.1016/S0303-2647(97)01708-5 10.1109/4235.585892
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Copyright	Copyright © 2016 Peng Li and Hua Zhu. Copyright © 2016 Peng Li and Hua Zhu. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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Snippet	The optimal performance of the ant colony algorithm (ACA) mainly depends on suitable parameters; therefore, parameter selection for ACA is important. We...
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SubjectTerms	Algorithms Ant colony optimization Bacteria Formicidae Genetic algorithms Global optimization Mathematical analysis Methods Operators Optimization Parameters Particle swarm optimization Robots Standard deviation
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Title	Parameter Selection for Ant Colony Algorithm Based on Bacterial Foraging Algorithm
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