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Neural Computing and Applications

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28.02.2017 | Original Article

An improved ant colony optimization algorithm with strengthened pheromone updating mechanism for constraint satisfaction problem

verfasst von: Qin Zhang, Changsheng Zhang

Erschienen in: Neural Computing and Applications | Ausgabe 10/2018

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Abstract

Constraint satisfaction problem (CSP) is a fundamental problem in the field of constraint programming. To tackle this problem more efficiently, an improved ant colony optimization algorithm is proposed. In order to further improve the convergence speed under the premise of not influencing the quality of the solution, a novel strengthened pheromone updating mechanism is designed, which strengthens pheromone on the edge which had never appeared before, using the dynamic information in the process of the optimal path optimization. The improved algorithm is analyzed and tested on a set of CSP benchmark test cases. The experimental results show that the ant colony optimization algorithm with strengthened pheromone updating mechanism performs better than the compared algorithms both on the quality of solution obtained and on the convergence speed.

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Titel: An improved ant colony optimization algorithm with strengthened pheromone updating mechanism for constraint satisfaction problem
verfasst von: Qin Zhang
Changsheng Zhang
Publikationsdatum: 28.02.2017
Verlag: Springer London
Erschienen in: Neural Computing and Applications / Ausgabe 10/2018
Print ISSN: 0941-0643
Elektronische ISSN: 1433-3058
DOI: https://doi.org/10.1007/s00521-017-2912-0

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