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

Authors
Publisher
International Journal of Distributed Sensor Networks
Publication Date
Disciplines
  • Computer Science
  • Mathematics
  • Physics

Abstract

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.

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