A hybrid quantum chaotic swarm evolutionary algorithm for DNA encoding

Authors:
Jianhua Xiao;Jin Xu;Zhihua Chen;Kai Zhang;Linqiang Pan
Affiliations:
Key Laboratory of Image Processing and Intelligent Control, Department of Control Science and Engineering, Huazhong University of Science and Technology, Wuhan 430074, China;Key Laboratory of Image Processing and Intelligent Control, Department of Control Science and Engineering, Huazhong University of Science and Technology, Wuhan 430074, China and School of Electron ...;Key Laboratory of Image Processing and Intelligent Control, Department of Control Science and Engineering, Huazhong University of Science and Technology, Wuhan 430074, China;Key Laboratory of Image Processing and Intelligent Control, Department of Control Science and Engineering, Huazhong University of Science and Technology, Wuhan 430074, China;Key Laboratory of Image Processing and Intelligent Control, Department of Control Science and Engineering, Huazhong University of Science and Technology, Wuhan 430074, China
Venue:
Computers & Mathematics with Applications
Year:
2009

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Abstract

DNA encoding is crucial to successful DNA computation, which has been extensively researched in recent years. It is difficult to solve by the traditional optimization methods for DNA encoding as it has to meet simultaneously several constraints, such as physical, chemical and logical constraints. In this paper, a novel quantum chaotic swarm evolutionary algorithm (QCSEA) is presented, and is first used to solve the DNA sequence optimization problem. By merging the particle swarm optimization and the chaotic search, the hybrid algorithm cannot only avoid the disadvantage of easily getting to the local optional solution in the later evolution period, but also keeps the rapid convergence performance. The simulation results demonstrate that the proposed quantum chaotic swarm evolutionary algorithm is valid and outperforms the genetic algorithm and conventional evolutionary algorithm for DNA encoding.