Evolutionary Multi-objective Optimization for Wheeled Robots Bio-inspired Cognition

This article describes a simulation model in which a multi-objective approach is utilized for evolving an artificial neural networks (ANNs) controller for an autonomous mobile robot. A mobile robot is simulated in a 3D, physics-based environment for the RF-localization behavior. The elitist Pareto-frontier differential evolution (PDE) algorithm is used to generate the Pareto optimal set of ANNs that could optimize two objectives in a single run; (1) maximize the mobile robot homing behavior whilst (2) minimize the hidden neurons involved in the feed-forward ANN. At testing phase, the generated controllers are tested with a different environment. The testing environment is different from the conducted evolution environment. Interestingly however, the testing results showed some of the mobile robots are still robust to the environment used. The controllers allowed the robots to home in toward the signal source with different paths.