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Impact analysis of crossovers in a multi-objective evolutionary algorithm

Mashwani, WK and Salhi, A and Jan, MA and Khanum, RA and Sulaiman, M (2015) 'Impact analysis of crossovers in a multi-objective evolutionary algorithm.' Science International, 27 (6). 4943 - 4956. ISSN 1013-5316

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Multi-objective optimization has become mainstream because several real-world problems are naturally posed as a Multi-objective optimization problems (MOPs) in all fields of engineering and science. Usually MOPs consist of more than two conflicting objective functions and that demand trade-off solutions. Multi-objective evolutionary algorithms (MOEAs) are extremely useful and well-suited for solving MOPs due to population based nature. MOEAs evolve its population of solutions in a natural way and searched for compromise solutions in single simulation run unlike traditional methods. These algorithms make use of various intrinsic search operators in efficient manners. In this paper, we experimentally study the impact of different multiple crossovers in multi-objective evolutionary algorithm based on decomposition (MOEA/D) framework and evaluate its performance over test instances of 2009 IEEE congress on evolutionary computation (CEC?09) developed for MOEAs competition. Based on our carried out experiment, we observe that used variation operators are considered to main source to improve the algorithmic performance of MOEA/D for dealing with CEC?09 complicated test problems.

Item Type: Article
Uncontrolled Keywords: Multi-objective optimization problems (MOPs), Evolutionary Algorithms (EAs), Decomposition, Crossovers
Subjects: Q Science > QA Mathematics
Divisions: Faculty of Science and Health > Mathematical Sciences, Department of
Depositing User: Jim Jamieson
Date Deposited: 06 Dec 2016 10:54
Last Modified: 17 Aug 2017 17:21

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