Advances in Natural Computation

Volume 3612 of the series Lecture Notes in Computer Science pp 417-423

A Quantum-Inspired Genetic Algorithm for Scheduling Problems

  • Ling WangAffiliated withDepartment of Automation, Tsinghua University
  • , Hao WuAffiliated withDepartment of Automation, Tsinghua University
  • , Da-zhong ZhengAffiliated withDepartment of Automation, Tsinghua University

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This paper is the first to propose a quantum-inspired genetic algorithm (QGA) for permutation flow shop scheduling problem to minimize the maximum completion time (makespan). In the QGA, Q-bit based representation is employed for exploration in discrete 0-1 hyperspace by using updating operator of quantum gate as well as genetic operators of Q-bit. Meanwhile, the Q-bit representation is converted to random key representation, which is then transferred to job permutation for objective evaluation. Simulation results and comparisons based on benchmarks demonstrate the effectiveness of the QGA, whose searching quality is much better than that of the famous NEH heuristic.