CFP last date
20 January 2025
Call for Paper
February Edition
IJCA solicits high quality original research papers for the upcoming February edition of the journal. The last date of research paper submission is 20 January 2025

Submit your paper
Know more
The week's pick
Responsive image
SFLA-Based Line Balancing and Task Optimization in Master-Slave Controlled Hanger Transportation Systems for Garment Production

Duc Hoang Nguyen
Random Articles
Reseach Article

Signed LMS based Adaptive Ant System

Published on None 2011 by Abhishek Paul, Sumitra Mukhopadhyay
journal_cover_thumbnail

2nd National Conference on Computing, Communication and Sensor Network
Foundation of Computer Science USA
CCSN - Number 4
None 2011
Authors: Abhishek Paul, Sumitra Mukhopadhyay
bc673222-a380-4e5e-bca5-e0339512e603

Abhishek Paul, Sumitra Mukhopadhyay . Signed LMS based Adaptive Ant System. 2nd National Conference on Computing, Communication and Sensor Network. CCSN, 4 (None 2011), 7-12.

@article{
author = { Abhishek Paul, Sumitra Mukhopadhyay },
title = { Signed LMS based Adaptive Ant System },
journal = { 2nd National Conference on Computing, Communication and Sensor Network },
issue_date = { None 2011 },
volume = { CCSN },
number = { 4 },
month = { None },
year = { 2011 },
issn = 0975-8887,
pages = { 7-12 },
numpages = 6,
url = { /specialissues/ccsn/number4/4189-ccsn026/ },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Special Issue Article
%1 2nd National Conference on Computing, Communication and Sensor Network
%A Abhishek Paul
%A Sumitra Mukhopadhyay
%T Signed LMS based Adaptive Ant System
%J 2nd National Conference on Computing, Communication and Sensor Network
%@ 0975-8887
%V CCSN
%N 4
%P 7-12
%D 2011
%I International Journal of Computer Applications
Abstract

There are various metaheuristic algorithms which are used to solve the Traveling Salesman problem. Ant colony optimization (ACO) is one such algorithm, which is inspired by the foraging behavior of ants. In this paper, we have proposed a modified model, entitled as Signed Adaptive Ant System (SAAS) for pheromone updation of the Ant-System; SAAS exploits the properties of Adaptive Filters. The proposed algorithm is implemented using sign-LMS (Least Mean Square) based algorithm. It imparts no information about the correction factor of the LMS adaptive algorithm but provides the sign value of each function in the correction factor of the LMS algorithm. SAAS modifies its properties in accordance to the requirement of surrounding domain and for the betterment of its performance in dynamic environment. The proposed algorithm is also easier for hardware implementation. The results of an experimental evaluation, conducted to evaluate the usefulness of the new strategy, are well described. Our algorithm shows effective results as compared to other existing approaches.

References
  1. D.G. Goldberg, “Genetic Algorithms in Search, Optimization and Machine Learning”, Addison- Wesley, Reading, MA, 1989.
  2. H.G. Bayer and H.P. Schwefel, “Evolutionary Strategis: A Comprehensive Introduction”, Journal of Natural Computing, 2004, pp. 3-52.
  3. J. Kennedy and R.C. Eberhart, “ Particle Swarm Optimization”, in Proc. IEEE, Int. Conf. On Neural Networks, Piscataway, NJ, 1995, pp. 1942-1948.
  4. M. Dorigo and L.M. Gambardella, “Ant Colony System: A cooperative learning approach to the travelling salesman problem”, in IEEE Trans. Evol. Comput., 1997, pp. 53-66.
  5. K M. Dorigo, V. Maniezzo, and A. Colorni, “The ant system: Optimization by a colony of cooperating agents”, in IEEE Trans. Syst., Man Cybern. Part B, 1996, pp. 29–41.
  6. A. Paul and S.Mukhopadhyay, “An Adaptive Pheromone Updation of the Ant System using LMS Technique”, in Proc. Int. Conf. on Modelling, Optimization and Computing (ICMOC-2010), AIP, 2010, vol. 1298, pp. 498-503.
  7. S. Haykin, Adaptive Filter Theory, Prentice Hall, 3rd edition, 1996.
  8. [online] [TSPLIB]: http://www.iwr.uni-heidelberg.de/groups/comopt/software/TSPLIB95/tsp/.
  9. V. Maniezzo and A. Colorni, “ The Ant System applied to the quadratic assignment problem”, IEEE Trans. of Data Knowledge Engrg., 1999, vol.11, no. 5, pp. 769-778.
  10. A. Colorni, M. Dorigo, V. Maniezzo, and M. Trubian, “ Ant System for job-shop scheduling”, JORBEL-Belgian J. Operations Res., Stat. Comput. Sci., 1994, vol. 34, no. 1, pp.39.
  11. L.M.Gambardella, E.D.Taillard, andG.Agazzi, “MACS-VRPTW: A multiple ant colony system for vehicle routing problems with time windows,” in New Ideas in Optimization, D.Corne et.al.,Eds. McGraw Hill, London, UK, 1999, pp.63–76.
Index Terms

Computer Science
Information Sciences

Keywords

Ant System (AS) Ant Colony Optimization (ACO) Adaptive Filter Least Mean Square (LMS) Algorithm Sign Least Mean Square (sign-LMS) Algorithm Adaptive Ant System (AAS) Sign Adaptive Ant System (SAAS) Traveling Salesman Problem (TSP)