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...
Saved in:
Main Authors: | , , , , |
---|---|
Format: | Article |
Published: |
Universiti Teknikal Malaysia Melaka
2017
|
Subjects: | |
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 |
Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
id |
my.utm.76564 |
---|---|
record_format |
eprints |
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 |