The use of Particle Swarm Optimization, a heuristic optimization technique based on the concept of swarm, is described to face the problem of classification of instances in multiclass databases. Three different fitness functions are taken into account, resulting in three versions being investigated. Their performance is contrasted on 13 typical test databases. The resulting best version is then compared against other nine classification techniques well known in literature. Results show the competitiveness of Particle Swarm Optimization. In particular, it turns out to be the best on 3 out of the 13 challenged problems.

Differential Evolution as a viable tool for Satellite Image Registration

DELLA CIOPPA, Antonio;
2008-01-01

Abstract

The use of Particle Swarm Optimization, a heuristic optimization technique based on the concept of swarm, is described to face the problem of classification of instances in multiclass databases. Three different fitness functions are taken into account, resulting in three versions being investigated. Their performance is contrasted on 13 typical test databases. The resulting best version is then compared against other nine classification techniques well known in literature. Results show the competitiveness of Particle Swarm Optimization. In particular, it turns out to be the best on 3 out of the 13 challenged problems.
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11386/1848609
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