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An Inclusive Survey on Marine Predators Algorithm: Variants and Applications

Marine Predators Algorithm (MPA) is the existing population-based meta-heuristic algorithms that falls under the category of Nature-Inspired Optimization Algorithm (NIOA) enthused by the foraging actions of the marine predators that principally pursues Levy or Brownian approach as its foraging strat...

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Autores principales: Rai, Rebika, Dhal, Krishna Gopal, Das, Arunita, Ray, Swarnajit
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Springer Netherlands 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9951854/
https://www.ncbi.nlm.nih.gov/pubmed/36855410
http://dx.doi.org/10.1007/s11831-023-09897-x
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author Rai, Rebika
Dhal, Krishna Gopal
Das, Arunita
Ray, Swarnajit
author_facet Rai, Rebika
Dhal, Krishna Gopal
Das, Arunita
Ray, Swarnajit
author_sort Rai, Rebika
collection PubMed
description Marine Predators Algorithm (MPA) is the existing population-based meta-heuristic algorithms that falls under the category of Nature-Inspired Optimization Algorithm (NIOA) enthused by the foraging actions of the marine predators that principally pursues Levy or Brownian approach as its foraging strategy. Furthermore, it employs the optimal encounter rate stratagem involving both the predator as well as prey. Since its introduction by Faramarzi in the year 2020, MPA has gained enormous popularity and has been employed in numerous application areas ranging from Mathematical and Engineering Optimization problems to Fog Computing to Image Processing to Photovoltaic System to Wind-Solar Generation System for resolving continuous optimization problems. Such huge interest from the research fraternity or the massive recognition of MPA is due to several factors such as its simplicity, ease of application, realistic execution time, superior convergence acceleration rate, soaring effectiveness, its ability to unravel continuous, multi-objective and binary problems when compared with other renowned optimization algorithms existing in the literature. This paper offers a detailed summary of the Marine Predators Algorithm (MPA) and its variants. Furthermore, the applications of MPA in a number of spheres such as Image processing, classification, electrical power system, Photovoltaic models, structural damage detection, distribution networks, engineering applications, Task Scheduling, optimization problems etc., are illustrated. To conclude, the paper highlights and thereby advocates few of the potential future research directions for MPA.
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spelling pubmed-99518542023-02-24 An Inclusive Survey on Marine Predators Algorithm: Variants and Applications Rai, Rebika Dhal, Krishna Gopal Das, Arunita Ray, Swarnajit Arch Comput Methods Eng Review Article Marine Predators Algorithm (MPA) is the existing population-based meta-heuristic algorithms that falls under the category of Nature-Inspired Optimization Algorithm (NIOA) enthused by the foraging actions of the marine predators that principally pursues Levy or Brownian approach as its foraging strategy. Furthermore, it employs the optimal encounter rate stratagem involving both the predator as well as prey. Since its introduction by Faramarzi in the year 2020, MPA has gained enormous popularity and has been employed in numerous application areas ranging from Mathematical and Engineering Optimization problems to Fog Computing to Image Processing to Photovoltaic System to Wind-Solar Generation System for resolving continuous optimization problems. Such huge interest from the research fraternity or the massive recognition of MPA is due to several factors such as its simplicity, ease of application, realistic execution time, superior convergence acceleration rate, soaring effectiveness, its ability to unravel continuous, multi-objective and binary problems when compared with other renowned optimization algorithms existing in the literature. This paper offers a detailed summary of the Marine Predators Algorithm (MPA) and its variants. Furthermore, the applications of MPA in a number of spheres such as Image processing, classification, electrical power system, Photovoltaic models, structural damage detection, distribution networks, engineering applications, Task Scheduling, optimization problems etc., are illustrated. To conclude, the paper highlights and thereby advocates few of the potential future research directions for MPA. Springer Netherlands 2023-02-24 2023 /pmc/articles/PMC9951854/ /pubmed/36855410 http://dx.doi.org/10.1007/s11831-023-09897-x Text en © The Author(s) under exclusive licence to International Center for Numerical Methods in Engineering (CIMNE) 2023. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. This article is made available via the PMC Open Access Subset for unrestricted research re-use and secondary analysis in any form or by any means with acknowledgement of the original source. These permissions are granted for the duration of the World Health Organization (WHO) declaration of COVID-19 as a global pandemic.
spellingShingle Review Article
Rai, Rebika
Dhal, Krishna Gopal
Das, Arunita
Ray, Swarnajit
An Inclusive Survey on Marine Predators Algorithm: Variants and Applications
title An Inclusive Survey on Marine Predators Algorithm: Variants and Applications
title_full An Inclusive Survey on Marine Predators Algorithm: Variants and Applications
title_fullStr An Inclusive Survey on Marine Predators Algorithm: Variants and Applications
title_full_unstemmed An Inclusive Survey on Marine Predators Algorithm: Variants and Applications
title_short An Inclusive Survey on Marine Predators Algorithm: Variants and Applications
title_sort inclusive survey on marine predators algorithm: variants and applications
topic Review Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9951854/
https://www.ncbi.nlm.nih.gov/pubmed/36855410
http://dx.doi.org/10.1007/s11831-023-09897-x
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