A novel quantum-inspired genetic algorithm for a weekly university scheduling optimization

Yu Zheng, Jingfa Liu
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引用次数: 1

Abstract

This paper presents a novel quantum-inspired algorithm(QGA) for the heavily constrained university scheduling problems(CUSP). The CUSP is a common problem for all institutions of higher education. It has been proved a NP problems. We propose a solving of CUSP based on the use of quantum-inspired algorithms. In the QGA, Q-bits based representation is employed by updating operator of quantum gate which is introduced as a variation operator to drive the individuals toward better solutions. The experimental results show that a set of high quality timetables can be achieved.
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一种新的量子遗传算法用于大学每周调度优化
本文提出了一种新的量子启发算法(QGA)来解决重约束大学调度问题(CUSP)。CUSP是所有高等教育机构面临的共同问题。它已被证明是一个NP问题。我们提出了一种基于量子启发算法的CUSP求解方法。在量子遗传算法中,量子门的更新算子作为变异算子被引入,以驱动个体向更好的解移动,从而采用基于q位的表示。实验结果表明,该方法可以获得一组高质量的时间表。
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