Friday 22nd of September 2017
 

Function Optimization Based on Quantum Genetic Algorithm


Ying Sun, Yuesheng Gu and Hegen Xiong

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 variableboundary- 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.

Keywords: function optimization; quantum genetic algorithm; variable-boundary coding; optimization algorithm

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ABOUT THE AUTHORS

Ying Sun
College of Machinery and Automation, B.O.X 242, Wuhan University of Science and Technology, Wuhan, 430081, China

Yuesheng Gu
2Department of Computer Science, Henan Institute of Science and Technology, Xinxiang 453003, Henan, China

Hegen Xiong
College of Machinery and Automation, B.O.X 242, Wuhan University of Science and Technology, Wuhan, 430081, China


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