A novel differential evolution algorithm for bi-criteria no-wait flow shop scheduling problems
Computers and Operations Research
Hybrid algorithms based on harmony search and differential evolution for global optimization
Proceedings of the first ACM/SIGEVO Summit on Genetic and Evolutionary Computation
Proceedings of the first ACM/SIGEVO Summit on Genetic and Evolutionary Computation
Expert Systems with Applications: An International Journal
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
A quantum-inspired genetic algorithm for k-means clustering
Expert Systems with Applications: An International Journal
Expert Systems with Applications: An International Journal
Modified genetic algorithms for manufacturing process planning in multiple parts manufacturing lines
Expert Systems with Applications: An International Journal
Quantum-inspired evolutionary algorithms: a survey and empirical study
Journal of Heuristics
IUKM'11 Proceedings of the 2011 international conference on Integrated uncertainty in knowledge modelling and decision making
A quantum immune algorithm for multiobjective parallel machine scheduling
ICSI'10 Proceedings of the First international conference on Advances in Swarm Intelligence - Volume Part I
A quantum-inspired artificial immune system for multiobjective 0-1 knapsack problems
ISNN'10 Proceedings of the 7th international conference on Advances in Neural Networks - Volume Part I
Population-based dynamic scheduling optimisation for complex production process
International Journal of Computer Applications in Technology
Mathematics and Computers in Simulation
A genetic algorithm for finding a path subject to two constraints
Applied Soft Computing
Journal of Intelligent Manufacturing
Replica creation strategy based on quantum evolutionary algorithm in data gird
Knowledge-Based Systems
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This paper proposes a hybrid quantum-inspired genetic algorithm (HQGA) for the multiobjective flow shop scheduling problem (FSSP), which is a typical NP-hard combinatorial optimization problem with strong engineering backgrounds. On the one hand, a quantum-inspired GA (QGA) based on Q-bit representation is applied for exploration in the discrete 0-1 hyperspace by using the updating operator of quantum gate and genetic operators of Q-bit. Moreover, random-key representation is used to convert the Q-bit representation to job permutation for evaluating the objective values of the schedule solution. 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 multiobjective sense, a randomly weighted linear-sum function is used in QGA, and a nondominated sorting technique including classification of Pareto fronts and fitness assignment is applied in PGA with regard to both proximity and diversity of solutions. To maintain the diversity of the population, two trimming techniques for population are proposed. The proposed HQGA is tested based on some multiobjective FSSPs. Simulation results and comparisons based on several performance metrics demonstrate the effectiveness of the proposed HQGA