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Population set-based global optimization algorithms: some modifications and numerical studies [An article from: Computers and Operations Research]
Book Details
Author(s)M.M. Ali, A. Torn
PublisherElsevier
ISBN / ASINB000RR1762
ISBN-13978B000RR1764
AvailabilityAvailable for download now
Sales Rank12,488,617
MarketplaceUnited States 🇺🇸
Description
This digital document is a journal article from Computers and Operations Research, published by Elsevier in 2004. The article is delivered in HTML format and is available in your Amazon.com Media Library immediately after purchase. You can view it with any web browser.
Description:
This paper studies the efficiency and robustness of some recent and well known population set-based direct search global optimization methods such as Controlled Random Search, Differential Evolution and the Genetic Algorithm. Some modifications are made to Differential Evolution and to the Genetic Algorithm to improve their efficiency and robustness. All methods are tested on two sets of test problems, one composed of easy but commonly used problems and the other of a number of relatively difficult problems.
Description:
This paper studies the efficiency and robustness of some recent and well known population set-based direct search global optimization methods such as Controlled Random Search, Differential Evolution and the Genetic Algorithm. Some modifications are made to Differential Evolution and to the Genetic Algorithm to improve their efficiency and robustness. All methods are tested on two sets of test problems, one composed of easy but commonly used problems and the other of a number of relatively difficult problems.
