Particle swarm optimization

Date of Award

2012

Degree Name

M.S. in Electrical Engineering

Department

Department of Electrical and Computer Engineering

Advisor/Chair

Advisor: Raul Ordóñez

Abstract

The particle swarm algorithm is a computational method to optimize a problem iteratively. As the neighborhood determines the sufficiency and frequency of information flow, the static and dynamic neighborhoods are discussed. The characteristics of the different methods for the selection of the algorithm for a particular problem are summarized. The performance of particle swarm optimization with dynamic neighborhood is investigated by three different methods. In the present work two more benchmark functions are tested using the algorithm. Conclusions are drawn by testing the different benchmark functions that reflect the performance of the PSO with dynamic neighborhood. And all the benchmark functions are analyzed by both Synchronous and Asynchronous PSO algorithms.

Keywords

Swarm intelligence Mathematical models, Problem solving, Algorithms, Evolutionary computation, Iterative methods (Mathematics), Mathematical optimization

Rights Statement

Copyright © 2012, author

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