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In this paper, an environment selection for Artificial Bee Colony (ABC) algorithm is developed for solving multi-objective optimization problems. In this selection, \(\theta -\) dominance and reference-lines framework are used. The employee, onlooker, and scout bee phases are modified using the environment selection. An external archive is maintained that stores the best solutions from the three phases of bees. The proposed algorithm is tested on DTLZ 1–4 problems for 3, 5, 8, 10, and 15 objectives. Results demonstrate the equivalent performance of the proposed ABC algorithm with respect to NSGA-III.

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Multi-objective Artificial Bee Colony Algorithm Using \(\theta -\) Dominance and Reference Lines

  • 会议论文

  • 发表日期：

    2025年

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  • Deepak Sharma,
  • Sandesh Deshmukh,
  • Abhishek Sarathe

摘要

In this paper, an environment selection for Artificial Bee Colony (ABC) algorithm is developed for solving multi-objective optimization problems. In this selection, \(\theta -\) dominance and reference-lines framework are used. The employee, onlooker, and scout bee phases are modified using the environment selection. An external archive is maintained that stores the best solutions from the three phases of bees. The proposed algorithm is tested on DTLZ 1–4 problems for 3, 5, 8, 10, and 15 objectives. Results demonstrate the equivalent performance of the proposed ABC algorithm with respect to NSGA-III.