An Adaptive Parameter Control Strategy for ACO

Author

Hao, Zhi-Feng ; Cai, Rui-chu ; Huang, Han

Author_Institution

Coll. of Comput. Sci. & Eng., South China Univ. of Technol., Guangzhou

fYear

2006

fDate

13-16 Aug. 2006

Firstpage

203

Lastpage

206

Abstract

Ant colony optimization (ACO) has been proved to be one of the best performing algorithms for NP-hard problems as TSP. Many strategies for ACO have been studied, but fewer tuning methodologies have been done on ACO´s parameters which influence the algorithm directly. The setting of ACO´s parameters is considered as a combinational optimization problem in this paper. The particle swarm optimization (PSO) is introduced to solve this problem, and an adaptive parameter setting strategy is proposed. It´s proved to be effective by the experiment based on TSPLIB test

Keywords

adaptive control; combinatorial mathematics; particle swarm optimisation; NP-hard problems; PSO; TSPLIB test; adaptive parameter control strategy; ant colony optimization; combinational optimization problem; particle swarm optimization; Adaptive control; Ant colony optimization; Computer aided instruction; Computer science; Convergence; Cybernetics; Educational institutions; Machine learning; NP-hard problem; Particle swarm optimization; Programmable control; Testing; Adaptive parameters; Ant Colony Optimization; Particle Swarm Optimization; TSP;

fLanguage

English

Publisher

ieee

Conference_Titel

Machine Learning and Cybernetics, 2006 International Conference on

Conference_Location

Dalian, China

Print_ISBN

1-4244-0061-9

Type

conf

DOI

10.1109/ICMLC.2006.258954

Filename

4028059