Advanced gravitational search algorithm with modified exploitation strategy

Khan, TA; Ling, SH; Mohan, AS

Advanced gravitational search algorithm with modified exploitation strategy

Khan, TA Ling, SH

Mohan, AS

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Publication Type:: Conference Proceeding
Citation:: Conference Proceedings - IEEE International Conference on Systems, Man and Cybernetics, 2019, 2019-October pp. 1056 - 1061
Issue Date:: 2019-10-01

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Field	Value	Language
dc.contributor.author	Khan, TA	en_US
dc.contributor.author	Ling, SH https://orcid.org/0000-0003-0849-5098	en_US
dc.contributor.author	Mohan, AS https://orcid.org/0000-0002-4503-5851	en_US
dc.date.issued	2019-10-01	en_US
dc.identifier.citation	Conference Proceedings - IEEE International Conference on Systems, Man and Cybernetics, 2019, 2019-October pp. 1056 - 1061	en_US
dc.identifier.isbn	9781728145693	en_US
dc.identifier.issn	1062-922X	en_US
dc.identifier.uri	http://hdl.handle.net/10453/138167
dc.description.abstract	© 2019 IEEE. Gravitational search algorithm (GSA) is a novel technique as compared to other heuristic methods and depends pon the gravitational forces between masses. It showed better performance in terms of convergence but has slow exploitation ability due to the fitness function effect on masses; they are getting heavier after every iteration. Therefore, masses are getting closer to each other and nullify the gravitational forces on each other avoiding them from swiftly exploiting the optimum. In order to solve this problem in this paper, an advanced gravitational search algorithm (AGSA) with modified exploitation strategy is proposed. The reason for the modification is that the agents will reach the optimum point swiftly and the convergence is much faster as compared to the standard and other improved versions of GSA available in the literature. AGSA is also compared with the standard and modified Particle Swarm optimization algorithm in this paper. Five benchmark functions have been implemented to assess the efficiency of the presented algorithm. In addition, a standard, constrained, design problem of a pressure vessel design is also used to examine the efficiency of the proposed technique. Simulation results empirically validated that the presented algorithm has remarkably better results in accordance with convergence and solution stability when compared to the other methods.	en_US
dc.relation.ispartof	Conference Proceedings - IEEE International Conference on Systems, Man and Cybernetics	en_US
dc.relation.isbasedon	10.1109/SMC.2019.8914478	en_US
dc.rights	info:eu-repo/semantics/embargoedAccess
dc.title	Advanced gravitational search algorithm with modified exploitation strategy	en_US
dc.type	Conference Proceeding
utslib.citation.volume	2019-October	en_US
utslib.for	0802 Computation Theory and Mathematics	en_US
utslib.for	0906 Electrical and Electronic Engineering	en_US
pubs.embargo.period	Not known	en_US
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology/School of Biomedical Engineering
pubs.organisational-group	/University of Technology Sydney/Strength - CHT - Health Technologies
utslib.copyright.status	open_access	*
utslib.copyright.embargo	2021-11-28T00:00:00+1100
pubs.publication-status	Published	en_US
pubs.volume	2019-October	en_US

Abstract:

© 2019 IEEE. Gravitational search algorithm (GSA) is a novel technique as compared to other heuristic methods and depends pon the gravitational forces between masses. It showed better performance in terms of convergence but has slow exploitation ability due to the fitness function effect on masses; they are getting heavier after every iteration. Therefore, masses are getting closer to each other and nullify the gravitational forces on each other avoiding them from swiftly exploiting the optimum. In order to solve this problem in this paper, an advanced gravitational search algorithm (AGSA) with modified exploitation strategy is proposed. The reason for the modification is that the agents will reach the optimum point swiftly and the convergence is much faster as compared to the standard and other improved versions of GSA available in the literature. AGSA is also compared with the standard and modified Particle Swarm optimization algorithm in this paper. Five benchmark functions have been implemented to assess the efficiency of the presented algorithm. In addition, a standard, constrained, design problem of a pressure vessel design is also used to examine the efficiency of the proposed technique. Simulation results empirically validated that the presented algorithm has remarkably better results in accordance with convergence and solution stability when compared to the other methods.

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http://hdl.handle.net/10453/138167