An Adaptive Ant Colony Algorithm Improved and Simulation

Yue Shun He; Xiang Li

doi:10.4028/www.scientific.net/AMM.87.209

Paper Titles

High-Precision Moment of Inertia Testing System and Error Analysis
p.191

High-Temperature Response of TiC Particulate Reinforced Titanium Matrix Composite
p.196

Modeling and Validation of Ultra Precision Positioning System
p.200

Modelling of Unilateral Contact of Metal and Fiberglass Shells
p.206

An Adaptive Ant Colony Algorithm Improved and Simulation
p.209

Research on Preparation and Properties of Edible Composite Protein Films
p.213

Development of Cutting Force Model of Aluminum Nitride Ceramic Processed by Micro End Milling
p.223

Investigating Effect of Industrial Coatings on Fatigue Damage
p.230

Rock Damage Research Based on the Energy Principles
p.238

HomeApplied Mechanics and MaterialsApplied Mechanics and Materials Vol. 87An Adaptive Ant Colony Algorithm Improved and...

An Adaptive Ant Colony Algorithm Improved and Simulation

Abstract:

Ant colony algorithm is a new evolutionary algorithm, Ant colony algorithm is widely used to solve combinatorial optimization problems, But the ant colony algorithm has slow convergence speed and prone to stagnation phenomenon. This paper presents an evolution strategy based on adaptive selection and dynamic adjustment to improve ant colony algorithm, the simulation results show that the algorithm performance significantly improved, this method can not only accelerate convergence rate, and save search time, but also can overcome premature stagnation of behavior, and to find a better solution. This is very favorable for solving large-scale optimization problem.

You might also be interested in these eBooks

Applied Mechanics and Manufacturing Technology

View Preview

Info:

Periodical:

Applied Mechanics and Materials (Volume 87)

Pages:

209-212

DOI:

https://doi.org/10.4028/www.scientific.net/AMM.87.209

Citation:

Cite this paper

Online since:

August 2011

Authors:

Yue Shun He, Xiang Li

Keywords:

Ant Colony Algorithm (ACA), Convergence Speed, Improved Algorithm, Simulation Experiment

Export:

RIS, BibTeX

Price:

Permissions CCC:

Request Permissions

Permissions PLS:

Request Permissions

Сopyright:

Citation:

References

[1] Zhi-He, Wang. Settings of algorithm parameters in ant colony algorithm[C]. Proceedings of the International Conference on Computer Science and Information Technology, ICCSIT 2008, pp.724-728, (2008).

DOI: 10.1109/iccsit.2008.94

Google Scholar

[2] Duan, Haibin. Ma, Guanjun; Liu, Senqi. Experimental study of the adjustable parameters in basic ant colony optimization algorithm[C]. 2007 IEEE Congress on Evolutionary Computation, CEC 2007, pp.149-156, (2008).

DOI: 10.1109/cec.2007.4424466

Google Scholar

[3] Socha, Krzysztof. Dorigo, Marco. Ant colony optimization for continuous domains[J]. European Journal of Operational Research, v 185, n 3, pp.1155-1173, March 16, (2008).

DOI: 10.1016/j.ejor.2006.06.046

Google Scholar

[4] Ellabib, Issmail; Basir, Otman. A multiple ant colony system with different communication strategies[C]. WSEAS Transactions on Information Science and Applications, v 2, n 6, pp.663-670, June (2005).

Google Scholar

[5] Feng, Yuan-Jing. Feng, Zu-Ren; Adaptive ant colony optimization algorithms and its convergence[C]. Control Theory and Applications, v 22, n 5, pp.713-717, October 2005.

Google Scholar

[6] Wang, Jian. Liu, Yanheng. An ant colony algorithm with global adaptive optimization[J]. Journal of Computational and Theoretical Nanoscience, v 4, n 7-8, pp.1283-1289, November/December (2007).

DOI: 10.1166/jctn.2007.2412

Google Scholar

[7] Huo, Fengcai; Ren, Weijian; Ran, Ruijun; An improved ant colony algorithm and its application in TSP[C]. The World Congress on Intelligent Control and Automation, pp.2994-2997, (2010).

DOI: 10.1109/wcica.2010.5554111

Google Scholar