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The Journal of Supercomputing

Erschienen in:

12.10.2021

A symbiosis of arithmetic optimizer with slime mould algorithm for improving global optimization and conventional design problem

verfasst von: Sumika Chauhan, Govind Vashishtha, Anil Kumar

Erschienen in: The Journal of Supercomputing | Ausgabe 5/2022

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Abstract

An arithmetic optimizer algorithm (AOA) is hybridized with slime mould algorithm (SMA) to address the issue of less internal memory and slow convergence at local minima which is termed as HAOASMA. Lens opposition-based learning strategy is also integrated with the hybrid algorithm which enhances the population diversity of the hybrid optimizer to accelerate the convergence. The local best (\(P_{{{\text{best}}}} )\) and global best (\(g_{{{\text{best}}}} )\) of SMA initializes the AOA’s search process. The \(P_{{{\text{best}}}}\) obtained from AOA again initializes the SMA to further exploit the search space. In this way, the developed hybrid algorithm utilizes the exploitation and exploration capabilities of SMA and AOA, respectively. The developed HAOASMA has been compared on twenty-three benchmark functions at different dimensions with basic SMA, AOA and six renowned meta-heuristic algorithms. The HAOASMA has also been applied to classical engineering design problems. The performance of HAOASMA is significantly superior compared to basic SMA, AOA and other meta-heuristic algorithms.

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Titel: A symbiosis of arithmetic optimizer with slime mould algorithm for improving global optimization and conventional design problem
verfasst von: Sumika Chauhan
Govind Vashishtha
Anil Kumar
Publikationsdatum: 12.10.2021
Verlag: Springer US
Erschienen in: The Journal of Supercomputing / Ausgabe 5/2022
Print ISSN: 0920-8542
Elektronische ISSN: 1573-0484
DOI: https://doi.org/10.1007/s11227-021-04105-8

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