An Adaptive Multi-population Artificial Bee Colony Algorithm for Multi-objective Flexible Job Shop Scheduling Problem

In this paper, we propose a novel artificial bee colony algorithm for solving the multi-objective flexible job shop scheduling problem. In this algorithm, the whole population is divided into multiple subpopulations at each generation, and the size of each subpopulation is adaptively adjusted based on the information derived from its search results. Furthermore, the two mutation strategies implemented in the differential evolution algorithm are embedded in the proposed algorithm to facilitate the exchange of information in each subpopulation and between different subpopulations, respectively. Experimental results on the well-known benchmark multi-objective problems show that the improvements of the strategies are positive and that the proposed algorithm is better than or at least competitive to some previous multi-objective evolutionary algorithms.