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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Veröffentlicht in:Mathematical problems in engineering Jg. 2016; H. 2016; S. 1 - 12
Hauptverfasser: Li, Peng, Zhu, Hua
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Cairo, Egypt Hindawi Publishing Corporation 01.01.2016
John Wiley & Sons, Inc
Schlagworte:
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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Zusammenfassung: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.
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ISSN:1024-123X
1563-5147
DOI:10.1155/2016/6469721