A performance analysis of Basin hopping compared to established metaheuristics for global optimization

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Serval ID
serval:BIB_01C042490500
Type
Article: article from journal or magazin.
Collection
Publications
Institution
Title
A performance analysis of Basin hopping compared to established metaheuristics for global optimization
Journal
Journal of Global Optimization
Author(s)
Baioletti Marco, Santucci Valentino, Tomassini Marco
Publication state
Published
Issued date
28/02/2024
Peer-reviewed
Oui
Language
english
Abstract
During the last decades many metaheuristics for global numerical optimization have been proposed. Among them, Basin Hopping is very simple and straightforward to implement, although rarely used outside its original Physical Chemistry community. In this work, our aim is to compare Basin Hopping, and two population variants of it, with readily available implementations of the well known metaheuristics Differential Evolution, Particle Swarm Optimization, and Covariance Matrix Adaptation Evolution Strategy. We perform numerical experiments using the IOH profiler environment with the BBOB test function set and two difficult real-world problems. The experiments were carried out in two different but comple- mentary ways: by measuring the performance under a fixed budget of function evaluations and by considering a fixed target value. The general conclusion is that Basin Hopping and its newly introduced population variant are almost as good as Covariance Matrix Adaptation on the synthetic benchmark functions and better than it on the two hard cluster energy minimiza- tion problems. Thus, the proposed analyses show that Basin Hopping can be considered a good candidate for global numerical optimization problems along with the more established metaheuristics, especially if one wants to obtain quick and reliable results on an unknown problem.
Create date
08/03/2024 16:35
Last modification date
09/03/2024 7:09
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