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MLGSA: multi-leader gravitational search algorithm for multi-objective optimization problem

Recently, we have introduced Multi-Leader Particle Swarm Optimization (MLPSO) algorithm for multi-objective optimization problem. Better convergence and diversity have been observed over the conventional Multi-Objective Particle Swarm Optimization. In this paper, the same concept is extended to Grav...

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Main Authors: Ghazali, M. R., Abas, K. H., Muhammad, B., Aziz, N. A. A., Lim, K. S.
Format: Article
Published: Universiti Teknikal Malaysia Melaka 2017
Subjects:
TK Electrical engineering. Electronics Nuclear engineering
Online Access:http://eprints.utm.my/id/eprint/76564/
https://www.scopus.com/inward/record.uri?eid=2-s2.0-85020776586&partnerID=40&md5=325426354632679f6781c90cb5c36b98
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spelling my.utm.765642018-04-30T13:33:00Z http://eprints.utm.my/id/eprint/76564/ MLGSA: multi-leader gravitational search algorithm for multi-objective optimization problem Ghazali, M. R. Abas, K. H. Muhammad, B. Aziz, N. A. A. Lim, K. S. TK Electrical engineering. Electronics Nuclear engineering Recently, we have introduced Multi-Leader Particle Swarm Optimization (MLPSO) algorithm for multi-objective optimization problem. Better convergence and diversity have been observed over the conventional Multi-Objective Particle Swarm Optimization. In this paper, the same concept is extended to Gravitational Search Algorithm (GSA). The performance was investigated by solving a set of ZDT test problem. An analysis was also performed by varying the value of initial gravitational constant. Universiti Teknikal Malaysia Melaka 2017 Article PeerReviewed Ghazali, M. R. and Abas, K. H. and Muhammad, B. and Aziz, N. A. A. and Lim, K. S. (2017) MLGSA: multi-leader gravitational search algorithm for multi-objective optimization problem. Journal of Telecommunication, Electronic and Computer Engineering, 9 (1-4). pp. 119-123. ISSN 2180-1843 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85020776586&partnerID=40&md5=325426354632679f6781c90cb5c36b98
institution Universiti Teknologi Malaysia
building UTM Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Teknologi Malaysia
content_source UTM Institutional Repository
url_provider http://eprints.utm.my/
topic TK Electrical engineering. Electronics Nuclear engineering
spellingShingle TK Electrical engineering. Electronics Nuclear engineering
Ghazali, M. R.
Abas, K. H.
Muhammad, B.
Aziz, N. A. A.
Lim, K. S.
MLGSA: multi-leader gravitational search algorithm for multi-objective optimization problem
description Recently, we have introduced Multi-Leader Particle Swarm Optimization (MLPSO) algorithm for multi-objective optimization problem. Better convergence and diversity have been observed over the conventional Multi-Objective Particle Swarm Optimization. In this paper, the same concept is extended to Gravitational Search Algorithm (GSA). The performance was investigated by solving a set of ZDT test problem. An analysis was also performed by varying the value of initial gravitational constant.
format Article
author Ghazali, M. R.
Abas, K. H.
Muhammad, B.
Aziz, N. A. A.
Lim, K. S.
author_facet Ghazali, M. R.
Abas, K. H.
Muhammad, B.
Aziz, N. A. A.
Lim, K. S.
author_sort Ghazali, M. R.
title MLGSA: multi-leader gravitational search algorithm for multi-objective optimization problem
title_short MLGSA: multi-leader gravitational search algorithm for multi-objective optimization problem
title_full MLGSA: multi-leader gravitational search algorithm for multi-objective optimization problem
title_fullStr MLGSA: multi-leader gravitational search algorithm for multi-objective optimization problem
title_full_unstemmed MLGSA: multi-leader gravitational search algorithm for multi-objective optimization problem
title_sort mlgsa: multi-leader gravitational search algorithm for multi-objective optimization problem
publisher Universiti Teknikal Malaysia Melaka
publishDate 2017
url http://eprints.utm.my/id/eprint/76564/
https://www.scopus.com/inward/record.uri?eid=2-s2.0-85020776586&partnerID=40&md5=325426354632679f6781c90cb5c36b98
_version_ 1643657346253062144
score 13.18916

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