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A novel ant colony optimization based on game for traveling salesman problem

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Abstract

Ant Colony Optimization (ACO) algorithms tend to fall into local optimal and have insufficient astringency when applied to solve Traveling Salesman Problem (TSP). To address this issue, a novel game-based ACO (NACO) is proposed in this report. NACO consists of two ACOs: Ant Colony System (ACS) and Max-Min Ant System (MMAS). First, an entropy-weighted learning strategy is proposed. By improving diversity adaptively, the optimal solution precision can be optimized. Then, to improve the astringency, a nucleolus game strategy is set for ACS colonies. ACS colonies under cooperation share pheromone distribution and distribute cooperative profits through nucleolus. Finally, to jump out of the local optimum, mean filtering is introduced to process the pheromone distribution when the algorithm stalls. From the experimental results, it is demonstrated that NACO has well performance in terms of both the solution precision and the astringency.

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Authors and Affiliations

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

    Kang Yang, Xiaoming You & Han Pan

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

    Shen Liu

Authors
  1. Kang Yang
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  2. Xiaoming You
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  3. Shen Liu
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  4. Han Pan
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Correspondence to Xiaoming You.

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This work was supported in part 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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Yang, K., You, X., Liu, S. et al. A novel ant colony optimization based on game for traveling salesman problem. Appl Intell 50, 4529–4542 (2020). https://doi.org/10.1007/s10489-020-01799-w

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