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Application of Hybrid Quantum Behaved Particle Swarm Optimization in Multi-objective Flexible Job Shop Scheduling

In the flexible job shop scheduling (FJSP) environment, the complexity of solving multiple objectives is still a hot topic. When the hybrid quantum particle swarm optimization(QPSO)algorithm is used to solve the flexible job shop, a rule initialization is used to ensure the quality of the initial solution when initializing the particle, so that it begins to search in the better direction. Aiming at the weakness of quantum particle group which is easy to fall into local optimum, this paper proposes to use the fast non-dominated sorting in NSGAII to find the non-dominated solution in the external repository, calculate the congestion degree using the crowded distance, and select the congestion degree according to the bidding selection method. The largest particle is used as the global optimal position, and its purpose is to guide each particle to search for the non-dominated solution of the sparse region, which is closer to the pareto solution. Experiments show that the hybrid quantum particle population has better convergence and the ability to maintain population diversity than other algorithms.