A Quantum-Inspired Genetic Algorithm for Scheduling Problems
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.
Unable to display preview. Download preview PDF.
- 1.Grover, L.K.: A fast quantum mechanical algorithm for database search. In: Proceedings of the 28th Annual ACM Symposium on the Theory of Computing, Pennsylvania, pp. 212–221 (1996)Google Scholar
- 2.Shor, P.W.: Algorithms for quantum computation: discrete logarithms and factoring. In: Proceedings of the 35th Annual Symposium on the Foundation of Computer Sciences, Los Alamitos, pp. 20–22 (1994)Google Scholar
- 3.Wang, L.: Intelligent Optimization with Applications. Tsinghua University & Springer Press, Beijing (2001)Google Scholar
- 4.Narayanan, A., Moore, M.: Quantum inspired genetic algorithm. In: IEEE International Conference on Evolutionary Computation, Piscataway, pp. 61–66 (1996)Google Scholar