Population structure and particle swarm performance

@article{Kennedy2002PopulationSA,
  title={Population structure and particle swarm performance},
  author={James Kennedy and Rui Mendes},
  journal={Proceedings of the 2002 Congress on Evolutionary Computation. CEC'02 (Cat. No.02TH8600)},
  year={2002},
  volume={2},
  pages={1671-1676 vol.2}
}
  • J. Kennedy, R. Mendes
  • Published 12 May 2002
  • Computer Science
  • Proceedings of the 2002 Congress on Evolutionary Computation. CEC'02 (Cat. No.02TH8600)
The effects of various population topologies on the particle swarm algorithm were systematically investigated. Random graphs were generated to specifications, and their performance on several criteria was compared. What makes a good population structure? We discovered that previous assumptions may not have been correct. 

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References

SHOWING 1-10 OF 11 REFERENCES
Parameter Selection in Particle Swarm Optimization
This paper first analyzes the impact that inertia weight and maximum velocity have on the performance of the particle swarm optimizer, and then provides guidelines for selecting these two parameters.
Small worlds and mega-minds: effects of neighborhood topology on particle swarm performance
  • J. Kennedy
  • Computer Science
    Proceedings of the 1999 Congress on Evolutionary Computation-CEC99 (Cat. No. 99TH8406)
  • 1999
TLDR
The study manipulated the neighborhood topologies of particle swarms optimizing four test functions and Sociometric structure and the small-world manipulation interacted with function to produce a significant effect on performance.
Evolutionary Optimization Versus Particle Swarm Optimization: Philosophy and Performance Differences
  • P. Angeline
  • Computer Science
    Evolutionary Programming
  • 1998
TLDR
This paper investigates the philosophical and performance differences of particle swarm and evolutionary optimization by comparison experiments involving four non-linear functions well studied in the evolutionary optimization literature.
The particle swarm - explosion, stability, and convergence in a multidimensional complex space
TLDR
This paper analyzes a particle's trajectory as it moves in discrete time, then progresses to the view of it in continuous time, leading to a generalized model of the algorithm, containing a set of coefficients to control the system's convergence tendencies.
Knowledge-based self-adaptation in evolutionary programming using cultural algorithms
  • R. Reynolds, C. Chung
  • Computer Science
    Proceedings of 1997 IEEE International Conference on Evolutionary Computation (ICEC '97)
  • 1997
TLDR
The results suggest that the use of a cultural framework for self-adaptation in EP can produce substantial performance improvements as expressed in terms of CPU time.
Particle swarm optimization: surfing the waves
  • E. Ozcan, C. Mohan
  • Computer Science
    Proceedings of the 1999 Congress on Evolutionary Computation-CEC99 (Cat. No. 99TH8406)
  • 1999
TLDR
This paper takes the next step, generalizing to obtain closed form equations for trajectories of particles in a multi-dimensional search space.
Collective dynamics of ‘small-world’ networks
TLDR
Simple models of networks that can be tuned through this middle ground: regular networks ‘rewired’ to introduce increasing amounts of disorder are explored, finding that these systems can be highly clustered, like regular lattices, yet have small characteristic path lengths, like random graphs.
Adaptation in natural and artificial systems
TLDR
Names of founding work in the area of Adaptation and modiication, which aims to mimic biological optimization, and some (Non-GA) branches of AI.
Small worlds: the dynamics of networks between order and randomness
  • Jie Wu
  • Computer Science
    SGMD
  • 2002
Everyone knows the small-world phenomenon: soon after meeting a stranger, we are surprised to discover that we have a mutual friend, or we are connected through a short chain of acquaintances. In his
Collective dynamics of ‘small-world
  • L L
  • 1998
...
1
2
...