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A Smart Particle Swarm Optimization Algorithm for Multi-objective Problems

  • Xiaohua Huo
  • Lincheng Shen
  • Huayong Zhu
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4115)

Abstract

Maintaining the diversity and convergence of Pareto optimal solutions is a desired task of optimization methods for multi-objective optimization problems(MOP). While accelerating the computing speed is important for algorithms to solve real-life MOP also. A Smart Particle Swarm Optimization algorithm for MOP(SMOPSO) is proposed. By setting the cooperative action of all the objective functions as the global best guide of swarm and selecting the closest or farthest archive member as the personal best guide of each particle, the SMOPSO method can find many Pareto optimal solutions in less iteration steps. Three well-known test functions have been used to validate our approach. Results show that the SMOPSO method is available and rapid.

Keywords

Particle Swarm Optimization Pareto Front Pareto Optimal Solution Inertia Weight Good Particle 
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

  • Xiaohua Huo
    • 1
  • Lincheng Shen
    • 1
  • Huayong Zhu
    • 1
  1. 1.Mechatronics and Automation SchoolNational University of Defense TechnologyChangshaChina

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