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
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;
Conference_Titel :
Swarm Intelligence Symposium, 2005. SIS 2005. Proceedings 2005 IEEE
Print_ISBN :
0-7803-8916-6
DOI :
10.1109/SIS.2005.1501607
