Quantum-Behaved Particle Swarm Optimization with Binary Encoding

  • Authors:

  • Jun Sun;Wenbo Xu;Wei Fang;Zhilei Chai

  • Affiliations:

  • Center of Intelligent and High Performance Computing, School of Information Technology, Southern Yangtze University, No. 1800, Lihudadao Road, Wuxi, 214122 Jiangsu, China;Center of Intelligent and High Performance Computing, School of Information Technology, Southern Yangtze University, No. 1800, Lihudadao Road, Wuxi, 214122 Jiangsu, China;Center of Intelligent and High Performance Computing, School of Information Technology, Southern Yangtze University, No. 1800, Lihudadao Road, Wuxi, 214122 Jiangsu, China;Center of Intelligent and High Performance Computing, School of Information Technology, Southern Yangtze University, No. 1800, Lihudadao Road, Wuxi, 214122 Jiangsu, China

  • Venue:
  • ICANNGA '07 Proceedings of the 8th international conference on Adaptive and Natural Computing Algorithms, Part I
  • Year:
  • 2007

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Abstract

The purpose of this paper is to generalize Quantum-behaved Particle Swarm Optimization (QPSO) Algorithm to discrete binary search space. To design Binary QPSO (BQPSO), we redefine the position vector and the distance between two positions, and adjust the iterative equations of QPSO to binary search space. The operations designed for BQPSO are far different from those in BPSO, but somewhat like those in Genetic Algorithms (GAs). Therefore, BQPSO integrates strongpoint of GA with the features of PSO, which make it able to find out the global optimum of the problem more efficiently than BPSO, as shown by the experiment results of BQPSO and BPSO on De Jong's five test functions.