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Multi-ant colony optimization algorithm based on game strategy and hierarchical temporal memory model

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Abstract

To solve the problems of slow convergence and insufficient accuracy of traditional ant colony algorithms in solving large-scale problems, this paper proposes a multi-ant colony optimization algorithm based on game strategy and hierarchical temporal memory model (GHMACO). Firstly, the heterogeneous multi-ant colony model is constructed, and each colony collaborates to improve the performance of the algorithm. Secondly, in order to enhance the communication among the heterogeneous colonies, a non-cooperative game strategy is introduced. The heterogeneous ant colonies are divided into the propagating colony and the absorbing colonies, where the propagating colony propagates the optimal payoffs of the game, and the absorbing colonies choose optimal strategies for absorption to balance the convergence and diversity of the algorithm. Further, the hierarchical temporal memory model is adopted to perform hierarchical optimization strategies based on path memories which includes: local exploration strategy, pheromone redistribution strategy and path replacement strategy, thus improving the accuracy of the algorithm and helping the colonies to jump out of the local optimum. Experiments on the traveling salesman problem show that the improved algorithm is effective in improving the convergence and accuracy of the traditional ant colony algorithms, especially in large-scale problems.

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Funding

This work was supported in part by the National Natural Science Foundation of China under Grant 61673258, Grant 61075115 and in part by the Shanghai Natural Science Foundation under Grant 19ZR1421600.

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Authors and Affiliations

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

    Qihuan Wu & Xiaoming You

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

    Sheng Liu

Authors
  1. Qihuan Wu

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  2. Xiaoming You

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  3. Sheng Liu

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Contributions

All authors contributed to the study conception and design. Material preparation, data collection and analysis were performed by [Xiaoming You], [Sheng Liu] . The first draft of the manuscript was written by [Qihuan Wu] and all authors commented on previous versions of the manuscript. All authors read and approved the final manuscript.

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Correspondence toXiaoming You.

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Wu, Q., You, X. & Liu, S. Multi-ant colony optimization algorithm based on game strategy and hierarchical temporal memory model.Cluster Comput27, 3113–3133 (2024). https://doi.org/10.1007/s10586-023-04136-1

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