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Comparative Analysis of Ant Colony and Particle Swarm Optimization Techniques

by Dr.R.Umarani, V.Selvi
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 5 - Number 4
Year of Publication: 2010
Authors: Dr.R.Umarani, V.Selvi
10.5120/908-1286

Dr.R.Umarani, V.Selvi . Comparative Analysis of Ant Colony and Particle Swarm Optimization Techniques. International Journal of Computer Applications. 5, 4 ( August 2010), 1-6. DOI=10.5120/908-1286

@article{ 10.5120/908-1286,
author = { Dr.R.Umarani, V.Selvi },
title = { Comparative Analysis of Ant Colony and Particle Swarm Optimization Techniques },
journal = { International Journal of Computer Applications },
issue_date = { August 2010 },
volume = { 5 },
number = { 4 },
month = { August },
year = { 2010 },
issn = { 0975-8887 },
pages = { 1-6 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume5/number4/908-1286/ },
doi = { 10.5120/908-1286 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T19:53:20.472197+05:30
%A Dr.R.Umarani
%A V.Selvi
%T Comparative Analysis of Ant Colony and Particle Swarm Optimization Techniques
%J International Journal of Computer Applications
%@ 0975-8887
%V 5
%N 4
%P 1-6
%D 2010
%I Foundation of Computer Science (FCS), NY, USA
Abstract

For a decade swarm Intelligence, an artificial intelligence discipline, is concerned with the design of intelligent multi-agent systems by taking inspiration from the collective behaviors of social insects and other animal societies. They are characterized by a decentralized way of working that mimics the behavior of the swarm. Swarm Intelligence is a successful paradigm for the algorithm with complex problems. This paper focuses on the comparative analysis of most successful methods of optimization techniques inspired by Swarm Intelligence (SI) : Ant Colony Optimization (ACO) and Particle Swarm Optimization (PSO). An elaborate comparative analysis is carried out to endow these algorithms with fitness sharing, aiming to investigate whether this improves performance which can be implemented in the evolutionary algorithms.

References
  1. Ajith Abraham,Swagatam Das,Sandip Roy, “Swarm Intelligence Algorithms for Data Cluctering.
  2. E.Corchado, PSO and ACO in Optimization Problems , Publishers : Springer - Verlag, pp. 1390 – 1398, 2006.
  3. Tsang W and Kwongs S, Ant Colony Clustering and Feature Extraction for Anomaly Intrusion Detection, in Swarm intelligence in Data Mining, Abraham A,(2006).
  4. Macro Dorigo, The Ant System: Optimization by a colony of cooperating agents.
  5. Christian Blum and Xiaodong Li, Swarm Intelligence in Optimization.
  6. P.Mathiyalagan, Grid scheduling Using Enhanced PSO Algorithm International Journal on Computer Science and Engineering,Vol. 02, No. 02, 2010, 140-145
  7. R. S. Parpinelli, H. S. Lopes, and A.A. Freiatas. Data mining with an ant colony optimization algorithm. IEEE Transactions on evolutionary computation, 6(4):321-332, 2002.
  8. De Falco, A. Della Cioppa, and E. Tarntino, “Evalution of Particle Swarm Optimization Effectiveness in Classification.
  9. Qinghai Bai,Analysis of Particle Swarm Optimization Algorithm Computer and Information Science Vol:3 ,No.1,February 2010.
Index Terms

Computer Science
Information Sciences

Keywords

Particle swarm optimization Swarm intelligence Ant Colony Optimization

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