High-dimensional function optimization with a self adaptive differential evolution

Conference proceedings article

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Publication Details

Author list: Worasucheep C.

Publisher: Hindawi

Publication year: 2009

Volume number: 1

Start page: 668

End page: 673

Number of pages: 6

ISBN: 9781424447541

ISSN: 0146-9428

eISSN: 1745-4557

URL: https://www.scopus.com/inward/record.uri?eid=2-s2.0-77949646051&doi=10.1109%2fICICISYS.2009.5357711&partnerID=40&md5=42b42ad53a9c26ce2d084323fba64859

Languages: English-Great Britain (EN-GB)

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Abstract

A good optimization algorithm must be capable of handling high-dimensional problems, meaning that there are many decision variables to be optimized at the same time. The problems of this category are challenging. This paper tests the scalability of wDE, which is a differential evolution algorithm with self-adaptive parameters. The statistical results and convergence graphs from the experimentation using benchmark problems of 100-, 500-, and 2000-dimensions are analyzed and compared to three standard variants of differential evolution algorithm. ฉ2009 IEEE.

Keywords

High dimensional, Scalability, Self-adaptation