An effective shuffled frog-leaping algorithm for multi-objective flexible job shop scheduling problems

In this paper, we propose a hybrid shuffled frog-leaping algorithm (HSFLA) for solving the multi-objective flexible job shop scheduling problem. Three minimization objectives – the maximum completion time (makespan), the total workload of all machines, and the workload of the critical machine are considered simultaneously. In the proposed algorithm, several approaches are presented to construct the initial population with a high level of quality. Then each frog in the population is assigned to a corresponding memeplex according to the number of individuals who dominate it and then the number of frogs who are dominated by it. In the memetic evolution process, two crossover operators are presented to share information among the best frogs and the worst frog. Meanwhile, several local search methods are embedded in the algorithm to enhance the exploitation capability. Experimental results on the well-known benchmark instances and comparisons with other recently published algorithms show the efficiency and effectiveness of the proposed algorithm.