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An Algorithmic Framework for Multiobjective Optimization
Joint Authors
Ku Shaari, Ku Zilati
Ganesan, T.
Elamvazuthi, I.
Vasant, Pandian
Source
Issue
Vol. 2013, Issue 2013 (31 Dec. 2013), pp.1-11, 11 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2013-12-02
Country of Publication
Egypt
No. of Pages
11
Main Subjects
Medicine
Information Technology and Computer Science
Abstract EN
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.
American Psychological Association (APA)
Ganesan, T.& Elamvazuthi, I.& Ku Shaari, Ku Zilati& Vasant, Pandian. 2013. An Algorithmic Framework for Multiobjective Optimization. The Scientific World Journal،Vol. 2013, no. 2013, pp.1-11.
https://search.emarefa.net/detail/BIM-1033376
Modern Language Association (MLA)
Ganesan, T.…[et al.]. An Algorithmic Framework for Multiobjective Optimization. The Scientific World Journal No. 2013 (2013), pp.1-11.
https://search.emarefa.net/detail/BIM-1033376
American Medical Association (AMA)
Ganesan, T.& Elamvazuthi, I.& Ku Shaari, Ku Zilati& Vasant, Pandian. An Algorithmic Framework for Multiobjective Optimization. The Scientific World Journal. 2013. Vol. 2013, no. 2013, pp.1-11.
https://search.emarefa.net/detail/BIM-1033376
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1033376