Pugh, JimZhang, YizhenMartinoli, Alcherio2005-05-252005-05-252005-05-25200510.1109/SIS.2005.1501607https://infoscience.epfl.ch/handle/20.500.14299/213989WOS:000233683900013We 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 algorithmand the noise-resistantmethod for several numerical problems with added noise, as well as unsupervised learning of obstacle avoidance using one or more robots.particle swarm optimizationunsupervised learningnoisy optimizationswarm roboticstext::conference output::conference proceedings::conference paper