Bayesian optimization models for particle swarms

Authors:
Christopher K. Monson;Kevin D. Seppi
Affiliations:
Brigham Young University, Provo, UT;Brigham Young University, Provo, UT
Venue:
GECCO '05 Proceedings of the 7th annual conference on Genetic and evolutionary computation
Year:
2005

Citing 4
Cited 1

Swarm intelligence

Swarm intelligence
Artificial Intelligence: A Modern Approach

Artificial Intelligence: A Modern Approach
No free lunch theorems for optimization

IEEE Transactions on Evolutionary Computation
The particle swarm - explosion, stability, and convergence in amultidimensional complex space

IEEE Transactions on Evolutionary Computation

A graphical model for evolutionary optimization

Evolutionary Computation

Quantified Score

Hi-index	0.01

Visualization

Abstract

We explore the use of information models as a guide for the development of single objective optimization algorithms, giving particular attention to the use of Bayesian models in a PSO context. The use of an explicit information model as the basis for particle motion provides tools for designing successful algorithms. One such algorithm is developed and shown empirically to be effective. Its relationship to other popular PSO algorithms is explored and arguments are presented that those algorithms may be developed from the same model, potentially providing new tools for their analysis and tuning.