parameter selection for ant colony algorithm based on bacterial foraging algorithm

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ID: 206027
2016
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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.
Reference Key
li2016mathematicalparameter Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors ;Peng Li;Hua Zhu
Journal journal of power sources
Year 2016
DOI
10.1155/2016/6469721
URL
http://dx.doi.org/10.1155/2016/6469721
Keywords
civil engineering (general)mathematics engineering (general)

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