An algorithmic framework for multiobjective optimization

Multiobjective (MO) optimization is an emerging field which is increasingly being encountered in many fields globally. Various metaheuristic techniques such as differential evolution (DE), genetic algorithm (GA), gravitational search algorithm (GSA), and particle swarm optimization (PSO) have been u...

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Main Authors: Ganesan, T., Elamvazuthi, I., Shaari, K.Z.K., Vasant, P.
Format: Article
Institution: Universiti Teknologi Petronas
Record Id / ISBN-0: utp-eprints.32901 /
Published: ScientificWorld Ltd. 2013
Online Access: https://www.scopus.com/inward/record.uri?eid=2-s2.0-84896332898&doi=10.1155%2f2013%2f859701&partnerID=40&md5=5fd7579c53644b6d2d6a1021b2916a18
http://eprints.utp.edu.my/32901/
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spelling utp-eprints.329012022-03-30T01:11:49Z An algorithmic framework for multiobjective optimization Ganesan, T. Elamvazuthi, I. Shaari, K.Z.K. Vasant, P. Multiobjective (MO) optimization is an emerging field which is increasingly being encountered in many fields globally. Various metaheuristic techniques such as differential evolution (DE), genetic algorithm (GA), gravitational search algorithm (GSA), and particle swarm optimization (PSO) have been used in conjunction with scalarization techniques such as weighted sum approach and the normal-boundary intersection (NBI) method to solve MO problems. Nevertheless, many challenges still arise especially when dealing with problems with multiple objectives (especially in cases more than two). In addition, problems with extensive computational overhead emerge when dealing with hybrid algorithms. This paper discusses these issues by proposing an alternative framework that utilizes algorithmic concepts related to the problem structure for generating efficient and effective algorithms. This paper proposes a framework to generate new high-performance algorithms with minimal computational overhead for MO optimization. © 2013 T. Ganesan et al. ScientificWorld Ltd. 2013 Article NonPeerReviewed https://www.scopus.com/inward/record.uri?eid=2-s2.0-84896332898&doi=10.1155%2f2013%2f859701&partnerID=40&md5=5fd7579c53644b6d2d6a1021b2916a18 Ganesan, T. and Elamvazuthi, I. and Shaari, K.Z.K. and Vasant, P. (2013) An algorithmic framework for multiobjective optimization. The Scientific World Journal, 2013 . http://eprints.utp.edu.my/32901/
institution Universiti Teknologi Petronas
collection UTP Institutional Repository
description Multiobjective (MO) optimization is an emerging field which is increasingly being encountered in many fields globally. Various metaheuristic techniques such as differential evolution (DE), genetic algorithm (GA), gravitational search algorithm (GSA), and particle swarm optimization (PSO) have been used in conjunction with scalarization techniques such as weighted sum approach and the normal-boundary intersection (NBI) method to solve MO problems. Nevertheless, many challenges still arise especially when dealing with problems with multiple objectives (especially in cases more than two). In addition, problems with extensive computational overhead emerge when dealing with hybrid algorithms. This paper discusses these issues by proposing an alternative framework that utilizes algorithmic concepts related to the problem structure for generating efficient and effective algorithms. This paper proposes a framework to generate new high-performance algorithms with minimal computational overhead for MO optimization. © 2013 T. Ganesan et al.
format Article
author Ganesan, T.
Elamvazuthi, I.
Shaari, K.Z.K.
Vasant, P.
spellingShingle Ganesan, T.
Elamvazuthi, I.
Shaari, K.Z.K.
Vasant, P.
An algorithmic framework for multiobjective optimization
author_sort Ganesan, T.
title An algorithmic framework for multiobjective optimization
title_short An algorithmic framework for multiobjective optimization
title_full An algorithmic framework for multiobjective optimization
title_fullStr An algorithmic framework for multiobjective optimization
title_full_unstemmed An algorithmic framework for multiobjective optimization
title_sort algorithmic framework for multiobjective optimization
publisher ScientificWorld Ltd.
publishDate 2013
url https://www.scopus.com/inward/record.uri?eid=2-s2.0-84896332898&doi=10.1155%2f2013%2f859701&partnerID=40&md5=5fd7579c53644b6d2d6a1021b2916a18
http://eprints.utp.edu.my/32901/
_version_ 1741197785792774144
score 11.62408

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