Adaptive Firefly Algorithm for nonholonomic motion planning of car-like system

This paper provides a novel approach to design an Adaptive Firefly Algorithm using self-adaptation of the algorithm control parameter values by learning from their previous experiences in generating quality solutions. Computer simulations undertaken on a well-known set of 25 benchmark functions reveals that incorporation of Q-learning in Firefly Algorithm makes the corresponding algorithm more efficient in both runtime and accuracy. The performance of the proposed adaptive firefly algorithm has been studied on an automatic motion planing problem of nonholonomic car-like system. Experimental results obtained indicate that the proposed algorithm based parking scheme outperforms classical Firefly Algorithm and Particle Swarm Optimization with respect to two standard metrics defined in the literature.