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primary topic: An algorithmic framework for multiobjective optimization

An algorithmic framework for multiobjective optimization
type: AcademicArticle
type: Article
abstract: 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.
date: 2013
volume: 2013
is part of: The Scientific World Journal
status: peerReviewed
status: published
author list: authors
type: EPrint
type: ArticleEPrint
is part of: repository

ScientificWorld Ltd.
type: Organization

The Scientific World Journal
type: Collection
issn: 1537744X

K.Z.K. Shaari
type: Person
given name: K.Z.K.
family name: Shaari

I. Elamvazuthi
type: Person
given name: I.
family name: Elamvazuthi

T. Ganesan
type: Person
given name: T.
family name: Ganesan

P. Vasant
type: Person
given name: P.
family name: Vasant

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primary topic: An algorithmic framework for multiobjective optimization