Quantum-Inspired Genetic Algorithm Based on Simulated Annealing for Combinatorial Optimization Problem

被引：10
|
作者：
Shu, Wanneng [1 ]
机构：
[1] S Cent Univ Nationalities, Coll Comp Sci, Wuhan 430074, Peoples R China
来源：
INTERNATIONAL JOURNAL OF DISTRIBUTED SENSOR NETWORKS | 2009年 / 5卷 / 01期
关键词：
Quantum computing; Knapsack problem;
D O I：
10.1080/15501320802554992
中图分类号：
TP [自动化技术、计算机技术];
学科分类号：
0812 ;
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.
引用
收藏
页码：64 / 65
页数：2
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