Optimized Particle Swarm Optimization (OPSO) and its application to artificial neural network training

Background: Particle Swarm Optimization (PSO) is an established method for parameter optimization. It represents a population-based adaptive optimization technique that is influenced by several "strategy parameters". Cho
Background: Particle Swarm Optimization (PSO) is an established method for parameter optimization. It represents a population-based adaptive optimization technique that is influenced by several "strategy parameters". Choosing reasonable parameter values for the PSO is crucial for its convergence behavior, and depends on the optimization task. We present a method for parameter meta-optimization based on PSO and its application to neural network training. The concept of the Optimized Particle Swarm Optimization (OPSO) is to optimize the free parameters of the PSO by having swarms within a swarm. We assessed the performance of the OPSO method on a set of five artificial fitness functions and compared it to the performance of two popular PSO implementations. Results: Our results indicate that PSO performance can be improved if meta-optimized parameter sets are applied. In addition, we could improve optimization speed and quality on the other PSO methods in the majority of our experiments. We applied the OPSO method to neural network training with the aim to build a quantitative model for predicting blood-brain barrier permeation of small organic molecules. On average, training time decreased by a factor of four and two in comparison to the other PSO methods, respectively. By applying the OPSO method, a prediction model showing good correlation with training-, test- and validation data was obtained. Conclusion: Optimizing the free parameters of the PSO method can result in performance gain. The OPSO approach yields parameter combinations improving overall optimization performance. Its conceptual simplicity makes implementing the method a straightforward task.
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Metadaten
Author:Michael Meissner, Michael Schmuker, Gisbert Schneider
URN:urn:nbn:de:hebis:30-27431
Document Type:Article
Language:German
Date of Publication (online):2006/06/07
Year of first Publication:2006
Publishing Institution:Univ.-Bibliothek Frankfurt am Main
Release Date:2006/06/07
Note:
© 2006 Meissner et al; licensee BioMed Central Ltd.
This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Source:BMC Bioinformatics2006, 7:125 ; http://www.biomedcentral.com/1471-2105/7/125
HeBIS PPN:189980761
Institutes:Biochemie und Chemie
Dewey Decimal Classification:570 Biowissenschaften; Biologie
Sammlungen:Universitätspublikationen
Sondersammelgebiets-Volltexte
Licence (German):License LogoCreative Commons - Namensnennung 2.0

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