An Adaptive Staged PSO Based on Particles’ Search Capabilities

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


This study proposes an adaptive staged particle swarm optimization (ASPSO) algorithm based on analyses of particles’ search capabilities. First, the search processes of the standard PSO (SPSO) and the linear decreasing inertia weight PSO (LDWPSO) are analyzed based on our previous definition of exploitation. Second, three stages of the search process in PSO are defined. Each stage has its own search preference, which is represented by the exploitation capability of swarm. Third, the mapping between inertia weight, learning factor (w-c) and the exploitation capability is given. At last, the ASPSO is proposed. By setting different values of w-c in three stages, one can make swarm search the space with particular strategy in each stage, and the particles can be directed to find the solution more effectively. The experimental results show that the proposed ASPSO has better performance than SPSO and LDWPSO on most of test functions.


Particle Swarm Optimization Exploitation and Exploration Staged Search Strategy Performances 


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© Springer-Verlag Berlin Heidelberg 2010

Authors and Affiliations

  • Kun Liu
    • 1
  • Ying Tan
    • 1
  • Xingui He
    • 1
  1. 1.Key laboratory of Machine Perception, Ministry of EducationPeking UniversityBeijingChina

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