Computers and Intractability: A Guide to the Theory of NP-Completeness
Computers and Intractability: A Guide to the Theory of NP-Completeness
No free lunch theorems for optimization
IEEE Transactions on Evolutionary Computation
A fast and elitist multiobjective genetic algorithm: NSGA-II
IEEE Transactions on Evolutionary Computation
Quantum-inspired evolutionary algorithm for a class of combinatorial optimization
IEEE Transactions on Evolutionary Computation
IEEE Transactions on Evolutionary Computation
IEEE Transactions on Evolutionary Computation
A hybrid quantum chaotic swarm evolutionary algorithm for DNA encoding
Computers & Mathematics with Applications
Improved Quantum Evolutionary Algorithm Combined with Chaos and Its Application
ISNN 2009 Proceedings of the 6th International Symposium on Neural Networks: Advances in Neural Networks - Part III
Quantum-inspired evolutionary algorithms: a survey and empirical study
Journal of Heuristics
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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.