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Heterogeneous ant colony algorithm based on selective evolution mechanism and game strategy

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Abstract

In order to solve the problems of slow convergence and low accuracy of the traditional ant colony algorithm in solving the traveling salesman problem (TSP), this paper proposes a heterogeneous ant colony algorithm based on the selective evolution mechanism and game strategy (SGHACA). First, we propose a game mechanism. In each iteration, the best population and the worst population will play a game. At the end of the game, pheromone gains will be given to the participants according to the weight coefficients to motivate the populations to participate in the next round of the game, in order to promote cooperation among different populations and thus improve the diversity of the algorithm. Second, we introduce a selective evolution mechanism. The optimal paths of each iteration are compared with the historical optimal paths to evolve relatively better path fragments, thus improving the convergence speed of the algorithm. Finally, when the algorithm stagnates, we introduce a dynamic shaking mechanism, which is able to dynamically dither the pheromone on the current optimal path according to the number of iterations, increasing the probability that the algorithm chooses other paths, thus helping to jump out of the locally optimal solution. In order to verify the effectiveness of the improved algorithm in this paper, we experimentally verify through a large number of TSP instances that SGHACA is able to improve the convergence of the algorithm and the accuracy of the solution to a certain extent when solving large-scale TSP. Meanwhile, compared with the latest improved algorithms, the accuracy of the solution of SGHACA is better than most of the latest improved algorithms, which indicates that the improved algorithm of this paper has some competitiveness.

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Funding

This study was funded by the National Natural Science Foundation of China under Grant (61673258), Grant (61075115), and the Shanghai Natural Science Foundation under Grant (19ZR1421600).

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Authors and Affiliations

  1. College of Electronic and Electrical Engineering, Shanghai University of Engineering Science, Shanghai, 201620, China

    Lisheng Wu & Xiaoming You

  2. School of Management, Shanghai University of Engineering Science, Shanghai, 201620, China

    Sheng Liu

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  1. Lisheng Wu
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  2. Xiaoming You
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Contributions

The paper and the algorithm code were written by LW. Suggestions for revising the manuscript were given by XY. The material for the experiment was prepared by SL. The final manuscript is approved by all authors.

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Correspondence to Xiaoming You.

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Wu, L., You, X. & Liu, S. Heterogeneous ant colony algorithm based on selective evolution mechanism and game strategy. J Supercomput 80, 7171–7206 (2024). https://doi.org/10.1007/s11227-023-05706-1

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