Abstract
In this paper, we present a hybrid optimisation algorithm with particle swarm optimisation (PSO) and ant colony optimisation (ACO). Unlike the conventional ACO or PSO, the new optimisation scheme makes full use of the attributes of both algorithms. In the proposed algorithm, the PSO is used to optimise the parameters in the ACO, which means that the selection of parameters does not depend on artificial experiences or trial and error, but relies on the adaptive search of the particles in the PSO. We also make an optimised implementation of ACO, by which the running time of ants routing is largely reduced. The results of the simulated experiments show that the improved algorithm not only reduces the number of routes in the ACO, but also surpasses existing algorithms in performance for solving large-scale TSP problems. Simulation results also show that the speed of convergence of ACO algorithm could be enhanced greatly.
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