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A continuous artificial bee colony algorithm for solving uncapacitated facility location problems.

Meiqing An1, Wanli Xiang2, Yuxing Jiang1

  • 1School of Traffic & Transportation, Lanzhou Jiaotong University, Lanzhou, 730070, Gansu, People's Republic of China.

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|February 12, 2026
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Summary
This summary is machine-generated.

A new continuous artificial bee colony (ABC) algorithm, cABC, effectively solves discrete optimization problems like the uncapacitated facility location problem (UFLP). This enhanced swarm intelligence method improves solution accuracy and robustness compared to existing approaches.

Keywords:
Artificial bee colony algorithmDynamic repair strategyRandom guiding mechanismTime varying perturbationUncapacitated facility location problem

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Area of Science:

  • Optimization Algorithms
  • Swarm Intelligence
  • Operations Research

Background:

  • Artificial Bee Colony (ABC) algorithm is a popular swarm intelligence method for continuous optimization.
  • Standard ABC struggles with discrete optimization problems and uncapacitated facility location problems (UFLP) without complex modifications.
  • Existing intelligent algorithms often require enhancement in solution quality and deviation for UFLP.

Purpose of the Study:

  • To propose a continuous ABC (cABC) algorithm specifically designed to address the limitations of traditional ABC for discrete optimization problems, particularly UFLP.
  • To enhance the performance of ABC by incorporating novel mechanisms for initialization, solution conversion, and search intensification.
  • To validate the effectiveness and superiority of the proposed cABC algorithm against established methods on benchmark datasets.

Main Methods:

  • Developed a continuous ABC (cABC) algorithm capable of evolving in continuous space using chaotic initialization.
  • Implemented a probability discretizing mechanism to convert continuous solutions to binary vectors for UFLP.
  • Introduced a dynamic repair strategy for infeasible solutions, a random guiding mechanism, and a time-varying perturbation scheme to improve search.
  • Incorporated a modified probability choice mechanism and opposition-based learning to enhance exploration and exploitation.

Main Results:

  • cABC demonstrated effectiveness when initially compared against the traditional ABC algorithm on the CAP dataset.
  • Further validation showed cABC outperforming eleven other well-known approaches on both the CAP and M* datasets.
  • Experimental results indicate superior solution accuracy and robustness of cABC over state-of-the-art methods.

Conclusions:

  • The proposed continuous ABC (cABC) algorithm offers a robust and accurate solution for discrete optimization problems, specifically UFLP.
  • cABC successfully overcomes the limitations of traditional ABC in handling discrete optimization tasks.
  • The novel mechanisms integrated into cABC significantly enhance its search performance and solution quality, establishing it as a state-of-the-art method.