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A refined differential evolution algorithm for improving the performance of optimization process

A. R., Yusoff and Nafrizuan, Mat Yahya (2011) A refined differential evolution algorithm for improving the performance of optimization process. In: International Conference on Informatics Engineering and Information Science (ICIEIS 2011), 14-16 November 2011 , Kuala Lumpur. pp. 184-194., 252 (2). ISSN 1865-0929 ISBN 978-364225452-9

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Abstract

Various Artificial Intelligent (AI) algorithms can be applied in solving optimization problems. Among the latest Evaluation Algorithm (EA) have been developed is Differential Evolution (DE). DE is developed based on an improved Genetic Algorithm and come with different strategies for faster optimization. However, the population trapped in local optimality and premature convergence to cause in DE algorithm have cause poor performance during optimization process. To overcome the drawbacks, mixed population update and bounce back strategy were introduced to modify and improve current DE algorithm. A Himmelblau function and real case from engineering problem were used to show the performance improvements of refined DE in optimization process.

Item Type: Conference or Workshop Item (Lecture)
Additional Information: Indexed by Scopus
Uncontrolled Keywords: Differential evolution; Missed population; Bounce back; Himmelblau function
Subjects: T Technology > TJ Mechanical engineering and machinery
T Technology > TS Manufactures
Faculty/Division: Faculty of Manufacturing Engineering
Faculty of Mechanical Engineering
Depositing User: Mrs. Neng Sury Sulaiman
Date Deposited: 12 Dec 2019 05:00
Last Modified: 12 Dec 2019 05:00
URI: http://umpir.ump.edu.my/id/eprint/25563
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