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". 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 ex...

• Type: Journal
• Year: 2006
• Where: BMCBI
• Authors: Michael Meissner, Michael Schmuker, Gisbert Schneider
• Keywords: BMCBI 2006, Parameter, Particle Swarm Optimization, PSO Method