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Multi-strategy ant colony optimization with k-means clustering algorithm for capacitated vehicle routing problem

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Abstract

The capacitated vehicle routing problem (CVRP) is a well-known optimization issue in transportation logistics. As a typical representative of swarm intelligence algorithm, ant colony optimization (ACO) has shown encouraging outcomes in CVRP. In contrast, ACO has limitations such as undesirable solutions and susceptibility to getting stuck in local optima. To address these challenges, a multi-strategy adaptive ant colony optimization with the k-means clustering algorithm (KMACO) is proposed for solving CVRP in this study. In the initial stage of KMACO, k-means clustering algorithm is introduced to enhance the quality of the initial solution. Simultaneously, a path-saving factor is added to the state transition rules to improve the success rate of planning. Moreover, the algorithm’s global search capability is further enhanced by dynamically adjusting the pheromone volatilization coefficient. Then, a problem-specific crossover operator and three-stage local operators are designed to strike a balance between the global optimization and local search of KMACO. Finally, to confirm the effectiveness of KMACO, simulation experiments are conducted on three types of datasets. Compared with ACO and six other intelligent algorithms, the KMACO achieves the best-known solution in 17, 12, and 10 instances in benchmark sets A, B, and P, respectively.

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The data used in this paper are all from public datasets.

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Funding

This work was supported by the Xuzhou Science and Technology Program (KC21001) and National Natural Science Foundation of China (62173166).

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Authors and Affiliations

  1. School of Electrical Engineering and Automation, Jiangsu Normal University, Xuzhou, 221116, Jiangsu, China

    Zhaojun Zhang, Simeng Tan, Jiale Qin & Kuansheng Zou

  2. Land Surveying and Mapping Institute of Shandong Province, Jinan, 250102, Shandong, China

    Shengwu Zhou

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  1. Zhaojun Zhang
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  2. Simeng Tan
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Contributions

Zhaojun Zhang: Methodology, Validation, Supervision. Simeng Tan: Conceptualization, Investigation, Writing - Original Draft. Jiale Qin: Visualization, Formal analysis. Kuansheng Zou: Data Curation. Shengwu Zhou: Writing - Review and Editing. All authors reviewed the manuscript.

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Correspondence to Zhaojun Zhang.

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Zhang, Z., Tan, S., Qin, J. et al. Multi-strategy ant colony optimization with k-means clustering algorithm for capacitated vehicle routing problem. Cluster Comput 28, 202 (2025). https://doi.org/10.1007/s10586-024-04860-2

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