To solve some problems of ant system algorithm, such as the slow speed of convergence and falling into the phenomenon of “ant colony group loss” easily, we introduce the collective action into the traditional ant system algorithm. Based on the collective action, we propose a novel collective action-based ant system algorithm, namely CAAS, for solving the traveling salesman problem. In the CAAS algorithm, a collective action “optimal solution approval” is defined for ant colony and each ant of the ant colony is assigned a threshold, and then each ant decides whether to join into the collective action according to its own threshold in the iteration process. When all ants approved the same solution, the iteration is stopped and output the final optimal solution. At last, we conduct extensive experiments on six public datasets to verify the performance of the proposed CAAS algorithm. The experimental results show that the CAAS algorithm can get a better solution under a less iteration.
Traveling salesman problem Ant system Ant colony optimization Collective action
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Conflict of interest
The authors declare that they have no conflict of interest.
This article does not contain any studies with human participants or animals performed by any of the authors.
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