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A New Fast Ant Colony Optimization Algorithm: The Saltatory Evolution Ant Colony Optimization Algorithm

Author

Listed:
  • Shugang Li

    (School of Management, Shanghai University, Shanghai 200444, China)

  • Yanfang Wei

    (School of Management, Shanghai University, Shanghai 200444, China)

  • Xin Liu

    (School of Management, Shanghai University, Shanghai 200444, China)

  • He Zhu

    (School of Management, Shanghai University, Shanghai 200444, China)

  • Zhaoxu Yu

    (Department of Automation, East China University of Science and Technology, Shanghai 200237, China)

Abstract

Various studies have shown that the ant colony optimization (ACO) algorithm has a good performance in approximating complex combinatorial optimization problems such as traveling salesman problem (TSP) for real-world applications. However, disadvantages such as long running time and easy stagnation still restrict its further wide application in many fields. In this study, a saltatory evolution ant colony optimization (SEACO) algorithm is proposed to increase the optimization speed. Different from the past research, this study innovatively starts from the perspective of near-optimal path identification and refines the domain knowledge of near-optimal path identification by quantitative analysis model using the pheromone matrix evolution data of the traditional ACO algorithm. Based on the domain knowledge, a near-optimal path prediction model is built to predict the evolutionary trend of the path pheromone matrix so as to fundamentally save the running time. Extensive experiment results on a traveling salesman problem library (TSPLIB) database demonstrate that the solution quality of the SEACO algorithm is better than that of the ACO algorithm, and it is more suitable for large-scale data sets within the specified time window. This means it can provide a promising direction to deal with the problem about slow optimization speed and low accuracy of the ACO algorithm.

Suggested Citation

  • Shugang Li & Yanfang Wei & Xin Liu & He Zhu & Zhaoxu Yu, 2022. "A New Fast Ant Colony Optimization Algorithm: The Saltatory Evolution Ant Colony Optimization Algorithm," Mathematics, MDPI, vol. 10(6), pages 1-22, March.
    • Handle: RePEc:gam:jmathe:v:10:y:2022:i:6:p:925-:d:770671
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    References listed on IDEAS

    as
    1. Lipowski, Adam & Lipowska, Dorota, 2012. "Roulette-wheel selection via stochastic acceptance," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 391(6), pages 2193-2196.
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    Cited by:

    1. Shugang Li & Hui Chen & Xin Liu & Jiayi Li & Kexin Peng & Ziming Wang, 2023. "Online Personalized Learning Path Recommendation Based on Saltatory Evolution Ant Colony Optimization Algorithm," Mathematics, MDPI, vol. 11(13), pages 1-19, June.
    2. Yunshan Sun & Qian Huang & Ting Liu & Yuetong Cheng & Yanqin Li, 2023. "Multi-Strategy Enhanced Harris Hawks Optimization for Global Optimization and Deep Learning-Based Channel Estimation Problems," Mathematics, MDPI, vol. 11(2), pages 1-28, January.

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