A Hybrid Quantum-Inspired Genetic Algorithm for Multiobjective Flow Shop Scheduling

• Published in: IEEE transactions on systems, man and cybernetics. Part B, Cybernetics Vol. 37; no. 3; pp. 576 - 591
• Main Authors: Li, Bin-Bin, Wang, Ling
• Format: Journal Article
• Language: English
• Published: United States IEEE 01-06-2007
• Subjects: Algorithms; Artificial Intelligence; Combinatorial analysis; Computer Simulation; Cybernetics; Decision Support Techniques; Diversity; Electronics packaging; Exploration; fitness assignment; Genetic algorithms; Genetic engineering; Job shop scheduling; Measurement; Models, Genetic; Models, Theoretical; multiobjective flow shop scheduling; nondominated sorting; Numerical Analysis, Computer-Assisted; Operators; Pareto fronts; permutation; Personnel Staffing and Scheduling; population trimming; Processor scheduling; Quantum computing; quantum-inspired genetic algorithm (QGA); Representations; Schedules; Scheduling; Scheduling algorithm; Sorting; Testing
• ISSN: 1083-4419; 1941-0492
• DOI: 10.1109/TSMCB.2006.887946

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