A Novel Quantum Genetic Algorithm for Continuous Function Optimization

Abstract:

In this paper, a novel quantum genetic algorithm is proposed. This algorithm compares the probability expectation of the quantum chromosome with the best binary solution to determine rotation angle of rotation gate. Different individual in population evolve with different rate to complete local search and global search simultaneously. Hε gate is used to prevent the algorithm from premature convergence. After analyzing the algorithm and its global convergence, applying this approach to the optimization of function extremum, and comparing with the simple genetic algorithm and the quantum genetic algorithm, the simulation result illustrates that the algorithm has the characteristic of quick convergence speed and high solution precision.

• Periodical: Advanced Materials Research (Volumes 816-817)
• Pages: 907-914
• DOI: https://doi.org/10.4028/www.scientific.net/AMR.816-817.907
• Online since: September 2013
• Authors: Hao Li, Shi Yong Li
• Keywords: Function Optimization, Global Convergence, Hε Gate, Quantum Genetic Algorithm, Rotation Gate

References

[1] Narayanan A, Moore M. Quantum-inspired genetic algorithms[C]. Proc of IEEE Int Conf on Evolutionary Computation. Nagoya: IEEE Press, 1996: 61-66.

[2] Han K H, Kim J H. Genetic quantum algorithm and its application to combinatorial optimization problems[C]. Proc of the 2000 IEEE Congress on Evolutionary Computation. Piscataway: IEEE Press, 2000, 7: 1354-1360.

DOI: 10.1109/cec.2000.870809

[3] Han K H, Kim J H. Quantum-inspired evolutionary algorithm for a class of combinatorial optimization[J]. IEEE Trans on Evolutionary Computation, 2002, 6(6): 580-593.

DOI: 10.1109/tevc.2002.804320

[4] Han K H, Kim J H. On Setting the Parameters of Quantum-inspired Evolutionary Algorithm for Practical Applications[C] Proc of the 2003 Congress on Evolutionary Computation. IEEE Press, 2003, 12: 178-184.

DOI: 10.1109/cec.2003.1299572

[5] Han K H, Kim J H. Quantum-inspired evolutionary algorithms with a new termination criterion, Hε gate, and two-phase scheme[J]. IEEE Trans on Evolutionary Computation, 2004, 8(2): 156-169.

DOI: 10.1109/tevc.2004.823467

[6] Yang Jun-an, Li Bin, Zhuang Zhen-quan, et al. Quantum genetic algorithm and its application research in blind source separation. MINI-MICRO SYSTEMS. 2003, 24(8): 1518-1523.

DOI: 10.1109/icnnsp.2003.1279292

[7] Yang Shu-yuan, Jiao Li-cheng, Liu Fang. The quantum evolutionary algorithm. CHINESE JOURNAL OF ENGINEERING MATHEMATICS, 2006, 23(2): 235-246.

[8] Dai Xiaoming, Sun Rong, Zou Runming, et al. Global Convergence Analysis of Non-Crossover Genetic Algorithm and Its Application to Optimization. Journal of Systems Engineering and Electronics, 2002, 13 (2): 84-91.

[9] Günter Rudolph. Convergence Analysis of Canonical Genetic Algorithms. IEEE Trans on Neural Networks, 1994, 5(1): 96-101.

DOI: 10.1109/72.265964