A Hybrid Quantum-Inspired Genetic Algorithm for Multi-objective Scheduling
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.
KeywordsPareto Front Flow Shop Schedule Problem Flow Shop Schedule Problem Good Half Permutation Flow Shop Schedule Problem
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