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An Adaptive Ant System using Momentum Least Mean Square Algorithm

Published on December 2013 by Abhishek Paul, Sumitra Mukhopadhyay
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2nd International conference on Computing Communication and Sensor Network 2013
Foundation of Computer Science USA
CCSN2013 - Number 1
December 2013
Authors: Abhishek Paul, Sumitra Mukhopadhyay
b1d14d16-e946-4ddb-acc5-6d197db90d70

Abhishek Paul, Sumitra Mukhopadhyay . An Adaptive Ant System using Momentum Least Mean Square Algorithm. 2nd International conference on Computing Communication and Sensor Network 2013. CCSN2013, 1 (December 2013), 23-28.

@article{
author = { Abhishek Paul, Sumitra Mukhopadhyay },
title = { An Adaptive Ant System using Momentum Least Mean Square Algorithm },
journal = { 2nd International conference on Computing Communication and Sensor Network 2013 },
issue_date = { December 2013 },
volume = { CCSN2013 },
number = { 1 },
month = { December },
year = { 2013 },
issn = 0975-8887,
pages = { 23-28 },
numpages = 6,
url = { /proceedings/ccsn2013/number1/14754-1307/ },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Proceeding Article
%1 2nd International conference on Computing Communication and Sensor Network 2013
%A Abhishek Paul
%A Sumitra Mukhopadhyay
%T An Adaptive Ant System using Momentum Least Mean Square Algorithm
%J 2nd International conference on Computing Communication and Sensor Network 2013
%@ 0975-8887
%V CCSN2013
%N 1
%P 23-28
%D 2013
%I International Journal of Computer Applications
Abstract

In this paper, a novel model has been proposed for pheromone updation of the Ant-System, entitled as Momentum Adaptive Ant System (MAAS). MAAS exploits the properties of Adaptive Filters. The proposed algorithm is implemented using momentum-LMS (Least Mean Square) based algorithm. It imparts information about the previous occurrence of the system so as to keep the system active even in the region close to the minimum (i. e. , minimum optimal) solution. MAAS modifies its properties in accordance to the requirement of surrounding realm and for the betterment of its performance in dynamic environment. The proposed algorithm overcomes stagnation and offers better searching capability. Also it helps the ants (i. e. , co-operating agents) not to get stuck at local optima. The results of experimental study are well described and it establishes the usefulness of the new strategy. Proposed algorithm shows effective performance when applied to the Traveling Salesman Problem (TSP).

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Index Terms

Computer Science
Information Sciences

Keywords

Ant System (as) Momentum Least Mean Square (momentum-lms) Algorithm Momentum Adaptive Ant System (maas) Traveling Salesman Problem (tsp).