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Experimental study of the adjustable parameters in basic ant colony optimization algorithm

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3 Author(s)
Haibin Duan ; Beihang Univ., Beijing ; Guanjun Ma ; Senqi Liu

Ant colony optimization(ACO) algorithm was originally presented under the inspiration during collective behavior study results on real ant system, and it has strong robustness and easy to combine with other methods in optimization. Although basic ACO algorithm for the heuristic solution of hard combinational optimization problems enjoy a rapidly growing popularity, but little research is conducted on the optimum configuration strategy for the adjustable parameters in the ACO algorithm. In order to deeply study the optimum configuration strategy for the adjustable parameters in the ACO algorithm, an effective Matlab GUI(graphical user interface)-based ACO simulation platform is developed in this paper. In order to investigate the relative strengths and weaknesses of these adjustable parameters, series of experiments on EIL51TSP are conducted on the developed ACO simulation platform. On the basis of the experimental results presented above, a novel effective "three-step" optimum configuration strategy for the adjustable parameters in basic ACO algorithm is drawn. This "three-step" optimum configuration strategy for the adjustable parameters in basic ACO algorithm is also beneficial to the application and development of ACO algorithm in various kinds of optimization problems.

Published in:

Evolutionary Computation, 2007. CEC 2007. IEEE Congress on

Date of Conference:

25-28 Sept. 2007

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