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Recent approaches to global optimization problems through Particle Swarm Optimization (2002)
| Venue: | Natural Computing |
| Citations: | 200 - 34 self |
BibTeX
@ARTICLE{Parsopoulos02recentapproaches,
author = {K. E. Parsopoulos and M. N. Vrahatis},
title = {Recent approaches to global optimization problems through Particle Swarm Optimization},
journal = {Natural Computing},
year = {2002},
volume = {1},
pages = {235--306}
}
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Abstract
This paper presents an overview of our most recent results concerning the Particle Swarm Optimization (PSO) method. Techniques for the alleviation of local minima, and for detecting multiple minimizers are described. Moreover, results on the ability of the PSO in tackling Multiobjective, Minimax, Integer Programming and # 1 errors-in-variables problems, as well as problems in noisy and continuously changing environments, are reported. Finally, a Composite PSO, in which the heuristic parameters of PSO are controlled by a Differential Evolution algorithm during the optimization, is described, and results for many well-known and widely used test functions are given.
