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A hybrid quantum chaotic swarm evolutionary algorithm for.pdf

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A hybrid quantum chaotic swarm evolutionary algorithm for

Computers and Mathematics with Applications 57 (2009) 1949–1958 Contents lists available at ScienceDirect Computers and Mathematics with Applications journal homepage: /locate/camwa A hybrid quantum chaotic swarm evolutionary algorithm for DNA encodingI Jianhua Xiao a, Jin Xu a,b, Zhihua Chen a, Kai Zhang a, Linqiang Pan a,? a Key Laboratory of Image Processing and Intelligent Control, Department of Control Science and Engineering, Huazhong University of Science and Technology, Wuhan 430074, China b School of Electronic Engineering and Computer Science, Peking University, Beijing 100871, China a r t i c l e i n f o Keywords: DNA computing Quantum swarm evolutionary algorithm Chaotic search DNA encoding a b s t r a c t 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. ? 2008 Elsevier Ltd. All rights reserved. 1. Introduction DNA computing is a new computation vista, which has been extensively researched in recent years. In 1994, Adleman [1] first demonstrated the feasibility of solving NP-complete problems by DNA molecules. Because DNA computing has many good characteristics such as massive parallelism, exceptional energy e