ABSTRACT The Artificial Bee Colony (ABC) algorithm, which simulates the intelligent foraging behavior of the honeybee colony, is one of the most preferred swarm intelligence-based metaheuristic methods for combinatorial optimization problems. In this study, the local search ability of the ABC algorithm, which can be spread to different regions of the solution space, is developed with the pheromone approach of ant colony optimization (ACO). The effects of the method, named pheromonal ABC (pABC), to the standard ABC and its competitiveness with other metaheuristic methods was presented with testing with popular benchmark problems in the NP-hard problem class. For 40 different benchmark problems, while 15 results with ABC have reached the most successful results were obtained in the literature, 25 results obtained with pABC have reached to literature. While ABC best results were behind literature with a percentage of up to 1.12%, pABC best results were behind the percentage of up to 0.63%

中文翻译：

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用于离散优化问题的费洛蒙人工蜂群 (pABC) 算法

摘要 人工蜂群（ABC）算法模拟蜜蜂群的智能觅食行为，是组合优化问题中最受青睐的基于群体智能的元启发式方法之一。本研究利用蚁群优化（ACO）的信息素方法开发了ABC算法的局部搜索能力，可以扩展到解空间的不同区域。该方法称为信息素 ABC (pABC)，对标准 ABC 的影响及其与其他元启发式方法的竞争力通过对 NP 难问题类中的流行基准问题进行测试来展示。对于 40 个不同的基准问题，使用 ABC 的 15 个结果达到了文献中最成功的结果，使用 pABC 获得的结果达到了文献中的 25 个。