KNOB Particle Swarm Optimizer

  • Junqi Zhang
  • Kun Liu
  • Ying Tan
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6145)


It is not trivial to tune the swarm behavior just by parameter setting because of the randomness, complexity and dynamic involved in particle swarm optimizer (PSO). Hundreds of variants in the literature of last decade, brought various mechanism or ideas, sometimes also from outside of the traditional metaheuristics field, to tune the swarm behavior. While, in the same time, additional parameters have to be afforded. This paper proposes a new mechanism, named KNOB, to directly tune the swarm behavior through parameter setting of PSO. KNOB is defined as the first principal component of the statistical probability sequence of exploration and exploitation allocation along the search process. The using of the KNOB to tune PSO by parameter setting is realized through a statistical mapping, between the parameter set and the KNOB, learned by a radial basis function neural network (RBFNN) simulation model. In this way, KNOB provides an easy way to tune PSO directly by its parameter setting. A simple application of KNOB to promote is presented to verify the mechanism of KNOB.


Particle Swarm Optimizer Exploitation Exploration search Strategy KNOB 


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Copyright information

© Springer-Verlag Berlin Heidelberg 2010

Authors and Affiliations

  • Junqi Zhang
    • 1
  • Kun Liu
    • 2
  • Ying Tan
    • 2
  1. 1.Key Laboratory of Embedded System and Service Computing, Ministry of Education, Department of Computer Science and TechnologyTongji UniversityShanghaiChina
  2. 2.Key Laboratory of Machine Perception, Ministry of Education, Department of Machine Intelligence, School of Electronics Engineering and Computer SciencePeking UniversityBeijingChina

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