Title: Random weight-based ant colony optimisation algorithm for the multi-objective optimisation problems
Authors: I.D.I.D. Ariyasingha; T.G.I. Fernando
Addresses: Department of Mathematics and Computer Science, Open University of Sri Lanka, Sri Lanka ' Department of Computer Science, University of Sri Jayewardenepura, Sri Lanka
Abstract: Over the years, ant colony optimisation (ACO) algorithms have been proposed particularly for solving the hard combinatorial optimisation problems, such as the travelling salesman problem (TSP) and the job-shop scheduling problem (JSSP). Also, most real-world applications are concerned with the multi-objective optimisation problems. In this paper a new ant colony optimisation (ACO) algorithm is proposed for solving two or more objective functions, simultaneously. It is based on the ant colony system (ACS) algorithm and uses the random weight-based method. It is applied on several benchmark instances of the TSP and the JSSP from the literature and compared with more recent multi-objective ant colony optimisation algorithms (MOACO). The experimental results have shown that the proposed algorithm achieves better performance for solving the travelling salesman problem and the job-shop scheduling problem with multiple objectives. It also obtained well distribution all over the Pareto-optimal front.
Keywords: ant colony optimisation; ACO; job shop scheduling problem; JSSP; multi-objective optimisation; travelling salesman problem; TSP; random weight; metaheuristics; swarm intelligence.
International Journal of Swarm Intelligence, 2017 Vol.3 No.1, pp.77 - 100
Received: 17 Jun 2016
Accepted: 17 Nov 2016
Published online: 17 Feb 2017 *