Abstract
This paper proposes a hybrid quantum-inspired genetic algorithm (HQGA) for multi-objective flow shop scheduling problem. On one hand, a quantum-inspired GA (QGA) based on Q-bit representation is applied for exploration in discrete 0-1 hyperspace by using updating operator of quantum gate and genetic operators of Q-bit. Random key representation is used to convert the Q-bit representation to job permutation. On the other hand, permutation-based GA (PGA) is applied for both performing exploration in permutation-based scheduling space and stressing exploitation for good schedule solutions. To evaluate solutions in multi-objective sense, randomly weighted linear sum function is used in QGA, while non-dominated sorting techniques including classification of Pareto fronts and fitness assignment are applied in PGA regarding to both proximity and diversity of solutions in multi-objective sense. Simulation results and comparisons demonstrate the effectiveness and robustness of the proposed HQGA.
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