A Phased Adaptive PSO Algorithm for Multimodal Function Optimization

  • Haiping Yu
  • Fengying Yang
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7389)


Particle swarm optimization is a powerful algorithm that has been applied to various kinds of problems. However, it suffers from falling into local minimum and prematurity especially on multimodal function optimization problems. In this paper, a phased adaptive particle swarm optimization(PAPSO) is proposed to solve such problem. The process is divided into the initial particle pre-searching phase and the post-searching cooperative phase. In the post phase, the strategy of selecting randomly a certain number of particles for entering the reverse-learning is one of the most effective ways of escaping local stagnation. The illustrative example is provided to confirm the validity, as compared with the SPSO, Dynamic Inertia Weight PSO(PSO-W), and Tradeoff PSO(PSO-T) in terms of convergence speed and the ability of jumping out of the local optimal value. Simulation results confirm that the proposed algorithm is effective and feasible.


particle swarm optimization multimodal function adaptive 


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Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Haiping Yu
    • 1
  • Fengying Yang
    • 2
  1. 1.Faculty of Information EngineeringCity College Wuhan University of Science and TechnologyWuhanChina
  2. 2.College of Information EngineeringHuanghuai UniversityHenanChina

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