Particle Swarm Optimization

摘要

We investigate the use of Particle Swarm Optimization (PSO), and compare with Genetic Algorithms (GA), for a particular robot behavior-learning task: the training of an animat behavior totally determined by a fully-recurrent neural network, and with which we try to fulfill a simple exploration and food foraging task.The target behavior is simple, but the learning task is challenging because of the dynamic complexity of fully-recurrent neural networks.We show that standard PSO yield very good results for this learning problem, and appears to be much more effective than simple GA.