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Cooperative Learning in Neural Networks using Particle Swarm Optimizers (2000) [6 citations — 1 self]

by Frans van den Bergh ,  Andries P. Engelbrecht ,  A. P. Engelbrecht
South African Computer Journal
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Abstract:

This paper presents a method to employ particle swarms optimizers in a cooperative configuration. This is achieved by splitting the input vector into several sub-vectors, each which is optimized cooperatively in its own swarm. The application of this technique to neural network training is investigated, with promising results. Keywords: Particle swarms, cooperative learning, optimization Computing Review Categories: G.1.6, I.2.6 1 Introduction Particle Swarm Optimizers (PSOs) have previously been used to train neural networks[6, 10] and generally met with success. The advantage of the PSO over many of the other optimization algorithms is its relative simplicity. This paper aims to improve the performance of the basic PSO by partitioning the input vector into several subvectors. Each sub-vector is then allocated its own swarm. In Section 2, a brief overview of PSOs is presented, followed by a discussion of how cooperative behavior can be implemented through a splitting technique i...

Citations

2229 UCI repository of machine learning databases – Blake, Merz - 1998
402 Particle Swarm Optimization, in – Kennedy, Eberhart - 1995
106 A new optimizer using particle swarm theory – Eberhart, Kennedy - 1995
96 A cooperative coevolutionary approach to function optimization – Potter, Jong - 1994
34 Comparing inertia weights and constriction factors in particle swarm optimization – Eberhart, Shi - 2000
30 Using selection to improve particle swarm optimization – Angeline - 1998
17 Stereotyping: Improving particle swarm performance with cluster analysis – Kennedy - 2000
2 Training product unit neural networks – Engelbrecht, Ismail - 1999
2 Particle Swarm Weight Initialization in Multi-layer Perceptron Artificial Neural Networks – Bergh - 1999