Improved Quantum Evolutionary Algorithm for Combinatorial Optimization Problem

The method of calculating rotation angle of quantum rotation gate plays an important role to the performance of quantum evolutionary algorithm (QEA). This paper proposes an improved quantum evolutionary algorithm (IQEA), whose core is that a new approach of adaptive calculating rotation angle of qua...

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Bibliographic Details
Published in2007 International Conference on Machine Learning and Cybernetics Vol. 6; pp. 3501 - 3505
Main Authors Rui Zhang, Hui Gao
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.08.2007
Subjects
Algorithm design and analysis
Automation
Biological cells
Combinatorial optimization
Convergence
Cybernetics
Evolutionary computation
Improved quantum evolutionary algorithm
Knapsack problem
Machine learning
Optimization methods
Probability
Quantum computing
Quantum evolutionary algorithm
Quantum rotation gate
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ISBN1424409721
9781424409723
ISSN2160-133X
DOI10.1109/ICMLC.2007.4370753

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Summary:The method of calculating rotation angle of quantum rotation gate plays an important role to the performance of quantum evolutionary algorithm (QEA). This paper proposes an improved quantum evolutionary algorithm (IQEA), whose core is that a new approach of adaptive calculating rotation angle of quantum rotation gate is designed on the basis of the probability amplitude ratio of the corresponding states. Rapid convergence and good global search capability characterize the performance of IQEA. Based on a typical combinatorial optimization problem - 0/1 knapsack problems, the influence of the relative parameter to the performance of IQEA is demonstrated, and then comparing experiments have been done. The results show that IQEA is superior to the previous quantum evolutionary algorithm.
ISBN:1424409721
9781424409723
ISSN:2160-133X
DOI:10.1109/ICMLC.2007.4370753