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Dynamic reproductive ant colony algorithm based on piecewise clustering

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Abstract

To address the lack of convergence speed and diversity of Ant Colony Optimization (ACO), a dynamic reproductive ant colony algorithm based on piecewise clustering (RCACS) is proposed to optimize the problems. First, the data is segmented by the clustering algorithm, and the exit and entrance of each cluster are obtained by the nearest neighbor strategy. The algorithm traversed all points of the cluster to find an unclosed shortest path according to the exit and entrance. Many fragment paths eventually merge into a full TSP. This strategy can accelerate the convergence speed of the algorithm and help it get higher accuracy. Second, when the algorithm stagnates, the dynamic regeneration mechanism based on feature transfer will transfer the excellent features of the mother-ants to the child-ants, so that it can further explore the neighborhood of the current optimal solution and help the algorithm jump out of the local optimum. From the results of simulation experiments and the rank-sum test, it can be found that the improved algorithm can effectively improve the diversity and accuracy of the algorithm, especially when solving large-scale problems.

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Authors and Affiliations

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

    Jin Yu

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

    Xiaoming You & Sheng Liu

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  1. Jin Yu
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  2. Xiaoming You
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  3. Sheng Liu
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Correspondence to Xiaoming You.

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Yu, J., You, X. & Liu, S. Dynamic reproductive ant colony algorithm based on piecewise clustering. Appl Intell 51, 8680–8700 (2021). https://doi.org/10.1007/s10489-021-02312-7

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