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Ant colony algorithm based on magnetic neighborhood and filtering recommendation

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Abstract

To deal with the problems that the ant colony algorithm has slow convergence speed and easy falling into the local optimum when solving TSP, an ant colony algorithm based on magnetic neighborhood and filtering recommendation (MRACS) is proposed to solve these problems. First, a dynamic magnetic neighborhood strategy is adopted to balance the convergence speed and the solution accuracy by magnetic attraction. It attracts ants to enlarge the exploration of a better neighborhood, thus improving the accuracy of the result. Second, a cross-excitation strategy based on filtering recommendation is applied to increase the diversity of the algorithm by dynamic weakening or enhancing local pheromones in the neighborhoods. It aids the algorithm get rid of the local optimum. Through the simulation experiments and the rank-sum test analysis, it is observed that the MRACS can effectively balance the convergence speed and the accuracy of the solution.

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Funding

This study is funded by the National Natural Science Foundation of China under Grant 61673258 and Grant 61075115 and in part by the Natural Science Foundation of Shanghai 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

    Jin Yu & Xiaoming You

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

    Sheng Liu

Authors
  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. Ant colony algorithm based on magnetic neighborhood and filtering recommendation. Soft Comput 25, 8035–8050 (2021). https://doi.org/10.1007/s00500-021-05851-w

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