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

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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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Acknowledgements

This work was supported by the National Natural Science Foundation Program of China (61572116, 61572117, 61602105), CERNET Innovation Project (NGII20160126), and the Special Fund for Fundamental Research of Central Universities of Northeastern University (N150408001, N150404009).

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  1. College of Computer Science and Engineering, Northeastern University, Shenyang, 110819, People’s Republic of China

    Qin Zhang & Changsheng Zhang

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  1. Qin Zhang
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  2. Changsheng Zhang
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Correspondence to Changsheng Zhang.

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We declare that we have no financial and personal relationships with other people or organizations that can inappropriately influence our work, and there is no professional or other personal interest of any nature or kind in any product, service and/or company that could be construed as influencing the position presented in or the review of the manuscript entitled, “An improved ant colony optimization algorithm with strengthened pheromone updating mechanism for constraint satisfaction problem.”

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Zhang, Q., Zhang, C. An improved ant colony optimization algorithm with strengthened pheromone updating mechanism for constraint satisfaction problem. Neural Comput & Applic 30, 3209–3220 (2018). https://doi.org/10.1007/s00521-017-2912-0

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