An Improved Hybrid Quantum Particle Swarm Optimization Algorithm for FJSP

Abstract

Aiming at minimizing makespan (the end time of the final machine) in flexible job shop scheduling problems (FJSP), a hybrid quantum behaved particle swarm optimization algorithm based on Lévy flights is proposed in this paper. Firstly, the algorithm uses the quantum probability amplitude coding method to establish a relationship between the process sequence and the particle position to solve job process sequencing sub-problem. Then uses the global selection, local selection and probability random selection to select the machine for each process. Finally, the Lévy flights is used to improve variant mode and enhance the effect of variation, the elitist strategy combined with neighborhood search is used after each iteration to improve the quality of the results. Experiments in a classical case show that the algorithm is effective and feasible for solving flexible job shop scheduling problems.