An adaptive memetic algorithm for multi-robot path-planning

Abstract

This paper provides a novel approach to design an adaptive memetic algorithm by utilizing the composite benefits of Differential Evolution for global search and Q-learning for local refinement. The performance of the proposed adaptive memetic algorithm has been studied on a real-time multi-robot path-planning problem. Experimental results obtained for both simulation and real frameworks indicate that the proposed algorithm based path-planning scheme outperforms real coded Genetic Algorithm, Particle Swarm Optimization and Differential Evolution, particularly its currently best version with respect to two standard metrics defined in the literature. © 2012 Springer-Verlag.