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<p>The uncapacitated facility location problem (UFLP) is a typical NP-hard problem, while most existing algorithms cannot efficiently address it. To tackle this issue, we propose a discrete crow search algorithm (DCSA). In DCSA, a dynamic awareness probability is introduced to balance global exploration and local exploitation. A binary encoding scheme is employed for each crow individual, aligning with the discrete nature of UFLP. Furthermore, a discrete evolutionary mechanism coupled with a neighborhood operation is adopted to enhance solution quality. To evaluate the performance of DCSA, computational experiments are conducted on 15 benchmark instances from the ORLib dataset and 20 instances from the M* dataset. Experimental results demonstrate that DCSA outperforms 20 existing algorithms in terms of solution quality, robustness, runtime, and stability. Consequently, DCSA proves to be an effective and reliable approach for addressing the UFLP. The population-based structure of DCSA makes it well suited for parallel implementation on high-performance computing systems to tackle large-scale instances.</p>

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A discrete crow search algorithm for solving the uncapacitated facility location problem

  • 期刊论文

  • 发表日期：

    2026年3月11日

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  • Le Xu,
  • Yong Xu,
  • Jiang Li

摘要

The uncapacitated facility location problem (UFLP) is a typical NP-hard problem, while most existing algorithms cannot efficiently address it. To tackle this issue, we propose a discrete crow search algorithm (DCSA). In DCSA, a dynamic awareness probability is introduced to balance global exploration and local exploitation. A binary encoding scheme is employed for each crow individual, aligning with the discrete nature of UFLP. Furthermore, a discrete evolutionary mechanism coupled with a neighborhood operation is adopted to enhance solution quality. To evaluate the performance of DCSA, computational experiments are conducted on 15 benchmark instances from the ORLib dataset and 20 instances from the M* dataset. Experimental results demonstrate that DCSA outperforms 20 existing algorithms in terms of solution quality, robustness, runtime, and stability. Consequently, DCSA proves to be an effective and reliable approach for addressing the UFLP. The population-based structure of DCSA makes it well suited for parallel implementation on high-performance computing systems to tackle large-scale instances.