A swarm-based artificial immune system for solving multimodal functions

Artificial Immune Systems (AIS) have attracted enormous attention among researchers because the algorithms are able to improve global searching ability and efficiency. Nevertheless, the rate of convergence for AIS is relatively slow compared to other metaheuristic algorithms. On the other hand, gene...

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Main Authors: Yap, D.F.W., Koh, S.P., Tiong, S.K., Prajindra, S.K.
Format: Article
Language:en_US
Published: 2017
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spelling my.uniten.dspace-60002018-01-04T02:47:51Z A swarm-based artificial immune system for solving multimodal functions Yap, D.F.W. Koh, S.P. Tiong, S.K. Prajindra, S.K. Artificial Immune Systems (AIS) have attracted enormous attention among researchers because the algorithms are able to improve global searching ability and efficiency. Nevertheless, the rate of convergence for AIS is relatively slow compared to other metaheuristic algorithms. On the other hand, genetic algorithms (GAs) and particle swarm optimization (PSO) have been used successfully in solving optimization problems, although they tend to converge prematurely. Therefore, the good attributes of AIS and PSO are merged in order to reduce this limitation. It is observed that the proposed hybrid AIS (HAIS) achieved better performances in terms of convergence rate, accuracy, and stability against GA and AIS by comparing the optimization results of the mathematical functions. A similar result was achieved by HAIS in the engineering problem when compared to GA, PSO, and AIS. Copyright © 2011 Taylor & Francis Group, LLC. 2017-12-08T07:49:37Z 2017-12-08T07:49:37Z 2011 Article 10.1080/08839514.2011.606662 en_US Applied Artificial Intelligence Volume 25, Issue 8, September 2011, Pages 693-707
institution Universiti Tenaga Nasional
building UNITEN Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Tenaga Nasional
content_source UNITEN Institutional Repository
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language en_US
description Artificial Immune Systems (AIS) have attracted enormous attention among researchers because the algorithms are able to improve global searching ability and efficiency. Nevertheless, the rate of convergence for AIS is relatively slow compared to other metaheuristic algorithms. On the other hand, genetic algorithms (GAs) and particle swarm optimization (PSO) have been used successfully in solving optimization problems, although they tend to converge prematurely. Therefore, the good attributes of AIS and PSO are merged in order to reduce this limitation. It is observed that the proposed hybrid AIS (HAIS) achieved better performances in terms of convergence rate, accuracy, and stability against GA and AIS by comparing the optimization results of the mathematical functions. A similar result was achieved by HAIS in the engineering problem when compared to GA, PSO, and AIS. Copyright © 2011 Taylor & Francis Group, LLC.
format Article
author Yap, D.F.W.
Koh, S.P.
Tiong, S.K.
Prajindra, S.K.
spellingShingle Yap, D.F.W.
Koh, S.P.
Tiong, S.K.
Prajindra, S.K.
A swarm-based artificial immune system for solving multimodal functions
author_facet Yap, D.F.W.
Koh, S.P.
Tiong, S.K.
Prajindra, S.K.
author_sort Yap, D.F.W.
title A swarm-based artificial immune system for solving multimodal functions
title_short A swarm-based artificial immune system for solving multimodal functions
title_full A swarm-based artificial immune system for solving multimodal functions
title_fullStr A swarm-based artificial immune system for solving multimodal functions
title_full_unstemmed A swarm-based artificial immune system for solving multimodal functions
title_sort swarm-based artificial immune system for solving multimodal functions
publishDate 2017
_version_ 1644493819095285760
score 13.211869