DocumentCode
1879290
Title
Particle swarm optimization for unsupervised robotic learning
Author
Pugh, Jim ; Martinoli, Alcherio ; Zhang, Yizhen
Author_Institution
Swarm-Intelligent Syst. Res. Group, Ecole Polytech. Fed. de Lausanne, Switzerland
fYear
2005
fDate
8-10 June 2005
Firstpage
92
Lastpage
99
Abstract
We explore using particle swarm optimization on problems with noisy performance evaluation, focusing on unsupervised robotic learning. We adapt a technique of overcoming noise used in genetic algorithms for use with particle swarm optimization, and evaluate the performance of both the original algorithm and the noise-resistant method for several numerical problems with added noise, as well as unsupervised learning of obstacle avoidance using one or more robots.
Keywords
collision avoidance; particle swarm optimisation; unsupervised learning; genetic algorithm; noise-resistant method; noisy performance evaluation; obstacle avoidance; particle swarm optimization; unsupervised robotic learning; Artificial neural networks; Design engineering; Gaussian noise; Genetic algorithms; Laboratories; Orbital robotics; Particle swarm optimization; Robots; Unsupervised learning; Working environment noise;
fLanguage
English
Publisher
ieee
Conference_Titel
Swarm Intelligence Symposium, 2005. SIS 2005. Proceedings 2005 IEEE
Print_ISBN
0-7803-8916-6
Type
conf
DOI
10.1109/SIS.2005.1501607
Filename
1501607
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