A Hybrid Artificial Bee Colony Algorithm for Flexible Job Shop Scheduling Problems

Jun-qing Li, Sheng-xian Xie, Quan-ke Pan, Song Wang


In this paper, we propose a hybrid Pareto-based artificial bee colony (HABC) algorithm for solving the multi-objective flexible job shop scheduling problem. In the hybrid algorithm, each food sources is represented by two vectors, i.e., the machine assignment vector and the operation scheduling vector. The artificial bee is divided into three groups, namely, employed bees, onlookers, and scouts bees. Furthermore, an external Pareto archive set is introduced to record non-dominated solutions found so far. To balance the exploration and exploitation capability of the algorithm, the scout bees in the hybrid algorithm are divided into two parts. The scout bees in one part perform randomly search in the predefined region while each scout bee in another part randomly select one non-dominated solution from the Pareto archive set. Experimental results on the well-known benchmark instances and comparisons with other recently published algorithms show the efficiency and effectiveness of the proposed algorithm.


Flexbile job shop scheduling problem, artificial bee colony, multiobjective optimization, hybrid algorithm

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DOI: https://doi.org/10.15837/ijccc.2011.2.2177

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