A new hybrid gravitational particle swarm optimisation-ACO with local search mechanism, PSOGSA-ACO-Ls for TSP. (1st August 2019)

• Record Type: Journal Article
• Authors: Rokbani, Nizar; Kromer, Pavel; Twir, Ikram; Alimi, Adel M.
• Is Part Of: International journal of intelligent engineering informatics. Volume 7:Number 4(2019)
• Page Start: 384
• Page End: 398
• Publication Date: 2019-08-01
• Subjects: particle swarm optimisation -- PSO -- ant colony optimisation -- ACO -- travelling salesman problem -- TSP -- local search -- hybridisation; Artificial intelligence -- Engineering applications -- Periodicals; Engineering -- Computer programs -- Periodicals; Knowledge management -- Periodicals; 620.0028563
• Languages: English
• ISSNs: 1758-8715

Abstract

The travelling salesman problem (TSP) is a hard combinatorial optimisation problem and a popular benchmarking problem at the same time. The TSP has also a number of practical real-world and industrial applications, such as routing in internet of things, IoT, networks, path planning in robotics and many others. In this paper, a new hybrid algorithm for the TSP is proposed; it combines gravitational particle swarm optimisation (PSOGSA) and ACO, and is called ant supervised by gravitational particle swarm optimisation with a local search, PSOGSA-ACO-LS. PSOGSA is used to optimise ACO settings while a local search mechanism, 2-Opt is employed by ACO to ameliorate its local solutions. The proposed method is evaluated using a set a test benches from the TSPLib database including: eil51, berlin52, st70, eil76, rat99, eil101, kroA100, and kroA200. Experimental results show that ACO-GPSO-LS is able to solve the set of TSP instances listed below including the large TSP data sets: kroA100, eli101 and kroA200.