Application Research for Multiobjective Low-Carbon Flexible Job-Shop Scheduling Problem Based on Hybrid Artificial Bee Colony Algorithm
Xiaolin Gu
Abstract
This paper proposes a hybrid artificial bee colony (HABC) to solve the multiobjective low-carbon flexible job-shop scheduling problem (MLFJSP). HABC algorithm uses a two-layer coding method to establish the initial population as the nectar source for the employed bees. In the optimization process, the employed bee phase and the onlooker bee phase adopt improved crossover mutation strategies and adaptive neighborhood search strategies to generate new nectar sources, and the greedy method is used to retain better solutions. The scout bee update mechanism prevents the algorithm from falling into a local optimum and enhances the convergence of the algorithm. In order to prevent the loss of the optimal solution, the optimization results of each phase are saved in the Pareto archive (PA). Finally, two sets of international standard instances are used to carry out simulation experiments. After analyzing the simulation results, it is concluded that HABC is an effective algorithm to solve the multiobjective low-carbon flexible job shop scheduling problem.