Quantum-Behaved Particle Swarm Optimization with Immune Operator

  • Jing Liu
  • Jun Sun
  • Wenbo Xu
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4203)


In the previous paper, we proposed Quantum-behaved Particle Swarm Optimization (QPSO) that outperforms traditional standard Particle Swarm Optimization (SPSO) in search ability as well as less parameter to control. However, although QPSO is a global convergent search method, the intelligence of simulating the ability of human beings is deficient. In this paper, the immune operator based on the vector distance to calculate the density of antibody is introduced into Quantum-behaved Particle Swarm Optimization. The proposed algorithm incorporates the immune mechanism in life sciences and global search method QPSO to improve the intelligence and performance of the algorithm and restrain the degeneration in the process of optimization effectively. The results of typical optimization functions showed that QPSO with immune operator performs better than SPSO and QPSO without immune operator.


Particle Swarm Optimization Particle Swarm Optimization Algorithm Search Ability Standard Particle Swarm Optimization Immune Memory 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Jing Liu
    • 1
  • Jun Sun
    • 1
  • Wenbo Xu
    • 1
  1. 1.Center of Intelligent and High Performance Computing, School of Information TechnologySouthern Yangtze UniversityWuxiChina

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