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Cluster Computing

Erschienen in:

03.06.2023

Framework for wrapping binary swarm optimizers to the hybrid parallel cooperative coevolving version

verfasst von: Mohammadreza Ipchi Sheshgelani, Saeid Pashazadeh, Pedram Salehpoor

Erschienen in: Cluster Computing | Ausgabe 2/2024

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Abstract

In recent decades with the increase in the complexity of the problems, the need for high-performance and scalable optimization tools has been inevitable. Among different phenomena introduced to optimization problems, naturally inspired algorithms are favored. Also, encountering large-scale problems, high-performance tools like parallel implementations should be needed. In order to tackle this problem, the framework has been proposed that can wrap any swarm algorithm into an outperformer parallel and hybrid version. Six accepted swarm algorithms are selected to evaluate performance and compare the wrapped version with standard versions. Six nonlinear high-dimension benchmark functions are used to test the proposed algorithms. The experimental results show that wrapped versions outperform standard versions with the measurement of average best fitness.

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Titel: Framework for wrapping binary swarm optimizers to the hybrid parallel cooperative coevolving version
verfasst von: Mohammadreza Ipchi Sheshgelani
Saeid Pashazadeh
Pedram Salehpoor
Publikationsdatum: 03.06.2023
Verlag: Springer US
Erschienen in: Cluster Computing / Ausgabe 2/2024
Print ISSN: 1386-7857
Elektronische ISSN: 1573-7543
DOI: https://doi.org/10.1007/s10586-023-04029-3

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