Co-evolutionary particle swarm optimization to solve constrained optimization problems

@article{Kou2009CoevolutionaryPS,
  title={Co-evolutionary particle swarm optimization to solve constrained optimization problems},
  author={Xiaoli Kou and Sanyang Liu and Jianke Zhang and Wei Zheng},
  journal={Computers & Mathematics with Applications},
  year={2009},
  volume={57},
  pages={1776-1784}
}
This paper presents a co-evolutionary particle swarm optimization (CPSO) algorithm to solve global nonlinear optimization problems. A new co-evolutionary PSO (CPSO) is constructed. In the algorithm, a deterministic selection strategy is proposed to ensure the diversity of population. Meanwhile, based on the theory of extrapolation, the induction of evolving direction is enhanced by adding a co-evolutionary strategy, in which the particles make full use of the information each other by using… CONTINUE READING

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References

Publications referenced by this paper.
Showing 1-10 of 17 references

An efficient constraint handingmethod for genetic algorithms

  • K. Deb
  • ComputerMethods in AppliedMechanics and…
  • 2000
Highly Influential
3 Excerpts

Hybrid algorithmbased on particle swarmoptimization for solving constrained optimization problems

  • Y. S. Xiao B. Y. Li, Q. D. Wu
  • Control andDecision
  • 2004

Overview of particle swarm optimization

  • X. F. Xie, W. I. Zhang
  • Control and Decision 18 (2)
  • 2003
1 Excerpt

A swarm with an effective information sharing mechanism for unconstrained and constrained single objective optimization problems

  • T. Ray, M. K. Liew
  • in: IEEE Int. Conf. On Evolutionary Computation…
  • 2001
1 Excerpt

Use of a self-adaptive penalty approach for engineering optimization problems

  • A. Carlos
  • Computers in Industry 41 (2)
  • 2000
1 Excerpt

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