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.
['Jim Pugh', 'Alcherio Martinoli', 'Yizhen Zhang']
Particle swarm optimization for unsupervised robotic learning