A new hybrid gravitational particle swarm optimisation-ACO with local search mechanism, PSOGSA-ACO-Ls for TSP

摘要

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 swami optimisation (PSOGSA) and ACO, and is called ant supervised by gravitational particle swami 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.