Abstract
To address the lack of convergence speed and diversity of traditional ant colony algorithm when solving large-scale travelling salesmen problem (TSP), a multi-ant colony optimization based on bidirectional induction mechanism and cooperative game is proposed. First, a novel heterogeneous multi-population is introduced, different populations cooperate to enhance the algorithm’s overall performance. Second, a cooperative game in multi-population is proposed, the pheromone income is distributed to participants based on their contribution, which can balance the convergence and diversity. Third, we introduce a bidirectional induction mechanism, by expanding the difference of pheromone concentration between outstanding solutions and mediocre solutions, the convergence can be further improved. In addition, the current optimal clip will be recommended to other populations for learning, each population will continue search based on the current optimal clip, which can improve the accuracy of the solutions. If the algorithm falls into stagnation, the current optimal solution will be inducted reversely, which can help the algorithm jump out of the local optimum. Finally, the experimental results of the large-scale TSP instances show that the improved algorithm can effectively balance the convergence and diversity, and it has better stability.
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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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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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Wu, L., You, X. & Liu, S. Multi-ant colony optimization based on bidirectional induction mechanism and cooperative game. Soft Comput 27, 15075–15093 (2023). https://doi.org/10.1007/s00500-023-08689-6
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DOI: https://doi.org/10.1007/s00500-023-08689-6