Journal of Global Optimization

, Volume 41, Issue 3, pp 427–445 | Cite as

Self-adaptive velocity particle swarm optimization for solving constrained optimization problems

  • Haiyan Lu
  • Weiqi Chen


Particle swarm optimization (PSO) is originally developed as an unconstrained optimization technique, therefore lacks an explicit mechanism for handling constraints. When solving constrained optimization problems (COPs) with PSO, the existing research mainly focuses on how to handle constraints, and the impact of constraints on the inherent search mechanism of PSO has been scarcely explored. Motivated by this fact, in this paper we mainly investigate how to utilize the impact of constraints (or the knowledge about the feasible region) to improve the optimization ability of the particles. Based on these investigations, we present a modified PSO, called self-adaptive velocity particle swarm optimization (SAVPSO), for solving COPs. To handle constraints, in SAVPSO we adopt our recently proposed dynamic-objective constraint-handling method (DOCHM), which is essentially a constituent part of the inherent search mechanism of the integrated SAVPSO, i.e., DOCHM + SAVPSO. The performance of the integrated SAVPSO is tested on a well-known benchmark suite and the experimental results show that appropriately utilizing the knowledge about the feasible region can substantially improve the performance of the underlying algorithm in solving COPs.


Constrained optimization Particle swarm optimization Stochastic optimization Evolutionary algorithms Nonlinear programming Constraint-handling mechanism 


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Copyright information

© Springer Science+Business Media, LLC. 2007

Authors and Affiliations

  1. 1.School of ScienceJiangnan UniversityWuxiP.R. China
  2. 2.Department of MathematicsZhejiang UniversityHangzhouP.R. China
  3. 3.School of Information TechnologyJiangnan UniversityWuxiP.R. China
  4. 4.China Ship Scientific Research CenterWuxiP.R. China

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