Solving flexible job shop scheduling problem by a multi-swarm collaborative genetic algorithm

Abstract

The flexible job shop scheduling problem (FJSP), which is NP-hard, widely exists in many manufacturing industries. It is very hard to be solved. A multi-swarm collaborative genetic algorithm (MSCGA) based on the collaborative optimization algorithm is proposed for the FJSP. Multi-population structure is used to independently evolve two sub-problems of the FJSP in the MSCGA. Good operators are adopted and designed to ensure this algorithm to achieve a good performance. Some famous FJSP benchmarks are chosen to evaluate the effectiveness of the MSCGA. The adaptability and superiority of the proposed method are demonstrated by comparing with other reported algorithms.