Authors: Zhang Yi; Zhang Meng; Li Xiao-qi; Lv Yan
Addresses: Department of Computer Science, Jilin Business and Technology College, Changchun 130062, China; Military Simulation Technology Institute, Air Force Aviation University, Changchun 130022, China. ' College of Computer Science and Technology, Key Laboratory of Symbol Computation and Knowledge Engineering of Ministry of Education, Jilin University, Changchun 130012, China. ' Military Simulation Technology Institute, Air Force Aviation University, Changchun 130022, China. ' Military Simulation Technology Institute, Air Force Aviation University, Changchun 130022, China
Abstract: In this paper, we present some practical solutions to the uncertainties of the ant colony algorithm. First, in order to find application and popularise the ACO, we try to find some techniques which can eliminate the impact of uncertainties in our algorithm; second, we have done a series of experiments in order to find the best assembly of the parameters. We also present a hybrid optimisation algorithm with artificial bee colony (ABC) and ant colony optimisation (ACO). The ABC is used for optimising multivariable function, and in the proposed algorithm, we use it to optimise the parameters in the ACO, which makes the selection of parameters independent on artificial experiences or trial and error, but rely on the adaptive search of the particles in the ABC. The results of the simulated experiments show that the improved algorithm not only reduces the number of routing 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.
Keywords: ant colony algorithm; ACO; artificial bee colony; ABC; travelling salesman problem; TSP; parallel strategy; self-adaptive; uncertainty.
International Journal of Computer Applications in Technology, 2012 Vol.43 No.4, pp.327 - 334
Received: 08 May 2021
Accepted: 12 May 2021
Published online: 31 May 2012 *