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The Scientific World Journal
Volume 2013, Article ID 859701, 11 pages
http://dx.doi.org/10.1155/2013/859701
Research Article

An Algorithmic Framework for Multiobjective Optimization

1Department of Chemical Engineering, University Technology Petronas, 31750 Tronoh, Perak, Malaysia
2Department of Electrical & Electronic Engineering, University Technology Petronas, 31750 Tronoh, Perak, Malaysia
3Department of Fundamental & Applied Sciences, University Technology Petronas, 31750 Tronoh, Perak, Malaysia

Received 25 August 2013; Accepted 14 October 2013

Academic Editors: C. W. Ahn, B. Liu, and C.-W. Tsai

Copyright © 2013 T. Ganesan et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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.