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Using Simulated Binary Crossover in Particle Swarm Optimization

  • Xiaoyu Huang
  • Enqiang Lin
  • Yujie Ji
  • Shijun Qiao
Part of the Advances in Intelligent and Soft Computing book series (AINSC, volume 123)

Abstract

Simulated binary crossover (SBX) operator is widely used in real-coded genetic algorithms. Particle swarm optimization (PSO) is a well-studied optimization scheme. In this paper, we combine SBX together with particle swarm optimization (PSO) procedures to prevent possible premature convergence. Benchmark tests are implemented and the result turns out that such modification enhances the exploitation ability of PSO.

Keywords

Particle Swarm Optimization Standard Particle Swarm Optimization Exploitation Ability Conventional Genetic Algorithm Adaptive Particle Swarm Optimization 
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 2011

Authors and Affiliations

  • Xiaoyu Huang
    • 1
  • Enqiang Lin
    • 1
  • Yujie Ji
    • 1
  • Shijun Qiao
    • 2
  1. 1.School of Software EngineeringNortheastern UniversityShenyangChina
  2. 2.School of ScienceNortheastern UniversityShenyangChina

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