Optimized Particle Swarm Optimization (OPSO) and its application to artificial neural network training |
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Authors: | Michael Meissner Michael Schmuker Gisbert Schneider |
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Institution: | 1. Johann Wolfgang Goethe-Universit?t, Institut für Organische Chemie und Chemische Biologie, Siesmayerstra?e 70, D-60323, Frankfurt, Germany
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Abstract: | 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. |
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