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Co-evolutionary Multi-Colony Ant Colony Optimization Based on Adaptive Guidance Mechanism and Its Application

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Abstract

Ant colony optimization has insufficient convergence and tends to fall into the local optima when solving the traveling salesman problem. This paper proposes a co-evolutionary multi-colony ant colony optimization (MCGACO) to overcome this deficiency and applies it to the Robot Path Planning. First, a dynamic grou** cooperation algorithm, combined with Ant Colony System and Max-Min Ant System, is introduced to form a heterogeneous multi-population structure. Each population co-evolves and complements each other to improve the overall optimization performance. Second, an adaptive guidance mechanism is proposed to accelerate convergence speed. The mechanism includes two parts: One is a dynamic evaluation network, which is used to evaluate and divide all solutions by the evaluation function. The other is a positive-negative incentive strategy, which can enhance the guiding role of solutions with higher evaluation value. Besides, to jump out of the local optima, an inter-specific co-evolution mechanism based on the game model is proposed. By dynamically determining the optimal communication combination, the diversity among populations can be well balanced. Finally, the experimental results demonstrate that MCGACO outperforms in terms of solution accuracy and convergence. Meanwhile, the proposed algorithm is also feasible for application in Robot Path Planning.

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Authors and Affiliations

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

    Shundong Li & **aoming You

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

    Sheng Liu

Authors
  1. Shundong Li
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  2. **aoming You
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  3. Sheng Liu
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Corresponding author

Correspondence to **aoming You.

Additional information

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

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Li, S., You, X. & Liu, S. Co-evolutionary Multi-Colony Ant Colony Optimization Based on Adaptive Guidance Mechanism and Its Application. Arab J Sci Eng 46, 9045–9063 (2021). https://doi.org/10.1007/s13369-021-05694-5

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  • DOI: https://doi.org/10.1007/s13369-021-05694-5

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