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Reseach Article

An Experimental Study of the Search Stagnation in Ants Algorithms

by Alaa Aljanaby
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 148 - Number 14
Year of Publication: 2016
Authors: Alaa Aljanaby
10.5120/ijca2016910861

Alaa Aljanaby . An Experimental Study of the Search Stagnation in Ants Algorithms. International Journal of Computer Applications. 148, 14 ( Aug 2016), 1-4. DOI=10.5120/ijca2016910861

@article{ 10.5120/ijca2016910861,
author = { Alaa Aljanaby },
title = { An Experimental Study of the Search Stagnation in Ants Algorithms },
journal = { International Journal of Computer Applications },
issue_date = { Aug 2016 },
volume = { 148 },
number = { 14 },
month = { Aug },
year = { 2016 },
issn = { 0975-8887 },
pages = { 1-4 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume148/number14/25837-2016910861/ },
doi = { 10.5120/ijca2016910861 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T23:53:20.400118+05:30
%A Alaa Aljanaby
%T An Experimental Study of the Search Stagnation in Ants Algorithms
%J International Journal of Computer Applications
%@ 0975-8887
%V 148
%N 14
%P 1-4
%D 2016
%I Foundation of Computer Science (FCS), NY, USA
Abstract

This paper conducts experimental tests to study the stagnation behavior the Interacted Multiple Ant Colonies Optimization (IMACO) framework. The idea of different ant colonies use different types of problem dependent heuristics has been proposed as well. The performance of IMACO was demonstrated by comparing it with the Ant Colony System (ACS) the best performing ant algorithm. The computational results show the dominance of IMACO and that IMACO suffers less from stagnation than ACS.

References
  1. Aljanaby A., K.R. Ku-Mahamud and N.M. Norwawi, 2010. Interacted multiple ant colonies to enhance the performance of ant colony optimization algorithms. Journal of Computer and Information Science (CIS), Canada, vol. 3, no. 1, pp. 29-34.
  2. Aljanaby A., K.R. Ku-Mahamud and N.M. Norwawi, 2010. Revisiting pheromone evaluation mechanism in the interacted multiple ant colonies framework. Proc. of 10th international conference on Artificial Intelligence and Applications (AIA2010), Austria, pp.12-15.
  3. Aljanaby A., K.R. Ku-Mahamud and N.M. Norwawi, 2010. An exploration Technique for the interacted multiple ant colonies framework. Proc. of 1st international conference on Intelligent Systems, Modelling, and Simulation (ISMS2010), Liverpool, UK, pp. 92-95.
  4. Baggio, G., J. Wainer and C. Ellis, 2004. Applying Scheduling Techniques to Minimize the Number of Late Jobs in Workflow Systems. Proc. of ACM symposium on Applied computing, Nicosia, Cyprus, pp. 1396-1403.
  5. Besten, M., T. Stützle and M. Dorigo, 2000. Ant Colony Optimization for the Total Weighted Tardiness Problem. Proc. of Parallel Problem Solving from Nature Conference, Paris, France, pp. 611-620.
  6. Blum, C. and A. Roli, 2003. Meta-heuristics in combinatorial optimization: Overview and conceptual comparisons. ACM Computing Surveys, vol. 35, no.3, pp. 268-308.
  7. Blum, C. and M. Dorigo, 2005. Search bias in ant colony optimization: On the role of competition-balanced systems. IEEE Trans. on Evolutionary Computation, vol. 9, no. 2, pp. 159-174.
  8. Congram, R., Potts, C., and van de Velde, S, 2002. An Iterated Dynasearch Algorithm for the Single-Machine Total Weighted Tardiness Scheduling Problem. INFORMS Journal on Computing, vol. 14, no. 1, pp. 52-67.
  9. Crauwels, H., C. Potts and L. van Wassenhove, 1998. Local Search Heuristics for the Single Machine Total Weighted Tardiness Scheduling Problem. INFORMS Journal on Computing, vol. 10, no. 3, pp. 341-350.
  10. Dorigo, M. and T. Stützle, 2002. The Ant Colony Optimization Meta-heuristic: Algorithms, Applications, and Advances. In: Handbook of Meta-heuristics (Eds. F. Glover and G. Kochenberger), pp. 250-285, Kluwer Academic Publishers.
  11. Dorigo, M. and T. Stützle, 2004. Ant colony optimization, London: The MIT Press.
  12. Guo J. E. and W. G. Diao, 2014. An improved ant colony optimization algorithm with crossover operator. Open Mechanical Engineering Journal, vol. 8, no. 1, pp. 96-100.
  13. Pang, S. C., T. M. Ma and T. Liu, 2015. An improved ant colony optimization with optimal search library for solving the traveling salesman problem. Journal of Computational and Theoretical Nanoscience, vol. 12, no. 7, pp. 1440-1444.
  14. Yan X. S., 2012. Efficiency analysis of swarm intelligence and randomization techniques. Journal of Computational and Theoretical Nanoscience, vol. 9, no. 2, pp. 189-198.
  15. Yue Y and X. Wang, 2015. An Improved Ant Colony Optimization Algorithm for Solving TSP. International Journal of Multimedia and Ubiquitous Engineering vol.10, no.12, pp.153-164.
Index Terms

Computer Science
Information Sciences

Keywords

Ant colony optimization combinatorial optimization problems search stagnation.