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PyDDRBG: A Python framework for benchmarking and evaluating static and dynamic multimodal optimization methods

Authors :
Ali Ahrari
Saber Elsayed
Ruhul Sarker
Daryl Essam
Carlos A. Coello Coello
Source :
SoftwareX, Vol 17, Iss , Pp 100961- (2022)
Publication Year :
2022
Publisher :
Elsevier, 2022.

Abstract

PyDDRBG is a Python framework for generating tunable test problems for static and dynamic multimodal optimization. It allows for quick and simple generation of a set of predefined problems for non-experienced users, as well as highly customized problems for more experienced users. It easily integrates with an arbitrary optimization method. It can calculate the optimization performance when measured according to the robust mean peak ratio. PyDDRBG is expected to advance the fields of static and dynamic multimodal optimization by providing a common platform to facilitate the numerical analysis, evaluation, and comparison in these fields.

Details

Language :
English
ISSN :
23527110
Volume :
17
Issue :
100961-
Database :
Directory of Open Access Journals
Journal :
SoftwareX
Publication Type :
Academic Journal
Accession number :
edsdoj.3653a25333849659321c6ac8e672d73
Document Type :
article
Full Text :
https://doi.org/10.1016/j.softx.2021.100961