A multi-strategy integration Pareto artificial bee colony algorithm for flexible job shop scheduling problems

To remedy the deficiency of the single algorithm in optimality and diversity when solving the multi-objective flexible job shop scheduling problem, we proposed a multi-strategy integration Pareto artificial bee colony algorithm (MSIPABC). First, this algorithm employs the hybrid heuristic strategy to initialize the food source colony to obtain the initiation colony with higher quality. Then the employed foragers use multiple local search operations to explore new food sources around the current food source. The onlookers use the tournament rules to select the superior food sources and perform crossover operation and the critical-path-based neighborhood search to further enhance the optimization search of the algorithm. At last, the scouters reconstruct the repetitive solutions, ensuring the diversity of the food source colony. The algorithm adapts multiple search strategies, realizes the autonomous and cooperative search of artificial swarm and hits a balance between global search and local search. The proposed algorithm is proved to be superior both in solution quality and diversity. © 2019, Editorial Board of Journal of Systems Engineering Society of China. All right reserved.