Improved Quantum Evolutionary Algorithm for Combinatorial Optimization Problem

• Published in: 2007 International Conference on Machine Learning and Cybernetics Vol. 6; pp. 3501 - 3505
• Main Authors: Rui Zhang, Hui Gao
• Format: Conference Proceeding
• Language: English
• 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
• ISBN: 1424409721; 9781424409723
• ISSN: 2160-133X
• DOI: 10.1109/ICMLC.2007.4370753

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.