Analysis of stagnation behaviour of competitive coevolutionary trained neuro-controllers

A new variant of the competitive coevolutionary team-based particle swarm optimiser (CCPSO(t)) algorithm is developed to train multi-agent teams from zero knowledge. Analysis show that the CCPSO algorithm stagnates during the training of simple soccer players. It is hypothesised that the stagnation is caused by saturation of the neural network weights. The CCPSO(t) algorithm is developed to overcome the stagnation problem. CCPSO(t) is based on the previously developed CCPSO algorithm with two additions. The first addition is the introduction of a restriction on the personal best particle positions. The second addition is the introduction of a linearly decreasing perception and core limit of the charged particle swarm optimiser. The final results show that the CCPSO(t) algorithm successfully addresses the CCPSO algorithm´s neural network weight saturation problem.