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2014 (Vol. 7, Issue: 1)
Article Information:

Function Optimization Based on Quantum Genetic Algorithm

Ying Sun and Hegen Xiong
Corresponding Author:  Ying Sun 

Key words:  Function optimization, optimization algorithm, quantum genetic algorithm, variable-boundary coding, , ,
Vol. 7 , (1): 144-149
Submitted Accepted Published
March 04, 2013 April 22, 2013 January 01, 2014
Abstract:

Optimization method is important in engineering design and application. Quantum genetic algorithm has the characteristics of good population diversity, rapid convergence and good global search capability and so on. It combines quantum algorithm with genetic algorithm. A novel quantum genetic algorithm is proposed, which is called Variable-boundary-coded Quantum Genetic Algorithm (vbQGA) in which qubit chromosomes are collapsed into variable-boundary-coded chromosomes instead of binary-coded chromosomes. Therefore much shorter chromosome strings can be gained. The method of encoding and decoding of chromosome is first described before a new adaptive selection scheme for angle parameters used for rotation gate is put forward based on the core ideas and principles of quantum computation. Eight typical functions are selected to optimize to evaluate the effectiveness and performance of vbQGA against standard Genetic Algorithm (sGA) and Genetic Quantum Algorithm (GQA). The simulation results show that vbQGA is significantly superior to sGA in all aspects and outperforms GQA in robustness and solving velocity, especially for multidimensional and complicated functions.
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  Cite this Reference:
Ying Sun and Hegen Xiong, 2014. Function Optimization Based on Quantum Genetic Algorithm.  Research Journal of Applied Sciences, Engineering and Technology, 7(1): 144-149.
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ISSN (Online):  2040-7467
ISSN (Print):   2040-7459
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