文章基本信息

标题：Quantum-Inspired Genetic Algorithm Based on Simulated Annealing for Combinatorial Optimization Problem
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作者：Wanneng Shu
期刊名称：International Journal of Distributed Sensor Networks
印刷版ISSN：1550-1329
电子版ISSN：1550-1477
出版年度：2009
卷号：5
DOI：10.1080/15501320802554992
出版社：Hindawi Publishing Corporation
摘要：Quantum-inspired genetic algorithm (QGA) is applied to simulated annealing (SA) to develop a class of quantum-inspired simulated annealing genetic algorithm (QSAGA) for combinatorial optimization. With the condition of preserving QGA advantages, QSAGA takes advantage of the SA algorithm so as to avoid premature convergence. To demonstrate its effectiveness and applicability, experiments are carried out on the knapsack problem. The results show that QSAGA performs well, without premature convergence as compared to QGA.