Authors: M.K.A. Ariyaratne; T.G.I. Fernando; Sunethra Weerakoon
Addresses: Department of Computer Science, Faculty of Applied Sciences, University of Sri Jayewardenepura, Sri Lanka ' Department of Computer Science, Faculty of Applied Sciences, University of Sri Jayewardenepura, Sri Lanka ' Department of Mathematics, Faculty of Applied Sciences, University of Sri Jayewardenepura, Sri Lanka
Abstract: Ant colony system (ACS) is a promising approach which has been widely used in problems such as travelling salesman problems (TSP), job shop scheduling problems (JSP) and quadratic assignment problems (QAP). In its original implementation, parameters of the algorithm were selected by trial and error approach. Over the last few years, novel approaches have been proposed on adapting the parameters of ACS in improving its performance. The aim of this paper is to use a framework introduced for self-tuning optimisation algorithms combined with the firefly algorithm (FA) to tune the parameters of the ACS solving symmetric TSP problems. The FA optimises the problem specific parameters of ACS while the parameters of the FA are tuned by the selected framework itself. With this approach, the user neither has to work with the parameters of ACS nor the parameters of FA. Using common symmetric TSP problems we demonstrate that the framework fits well for the ACS. A detailed statistical analysis further verifies the goodness of the new ACS over the existing ACS and also of the other techniques used to tune the parameters of ACS.
Keywords: self-tuning framework; ant colony system; ACS; travelling salesman problem; TSP; firefly algorithm.
International Journal of Swarm Intelligence, 2018 Vol.3 No.4, pp.309 - 331
Received: 14 Apr 2017
Accepted: 21 Aug 2017
Published online: 17 Apr 2018 *