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Advances in manta ray foraging optimization:A Comprehensive Survey

Journal of Bionic Engineering 2024年第2期21卷 953-990页

作者： Farhad Soleimanian Gharehchopogh Shafi Ghafouri Mohammad Namazi Bahman Arasteh Department of Computer Engineering Urmia BranchIslamic Azad UniversityUrmiaIran Department of Computer Engineering Tabriz BranchIslamic Azad UniversityTabrizIran Department of Computer Engineering Maybod BranchIslamic Azad UniversityMaybodIran Department of Software Engineering Faculty of Engineering and Natural ScienceIstinye UniversityIstanbulTurkey

This paper comprehensively analyzes the manta ray foraging optimization(MRFO)algorithm and its integration into diverse academic *** in 2020,the MRFO stands as a novel metaheuristic algorithm,drawing inspiration from manta rays’unique foraging behaviors—specifically cyclone,chain,and somersault *** biologically inspired strategies allow for effective solutions to intricate physical *** its potent exploitation and exploration capabilities,MRFO has emerged as a promising solution for complex optimization *** utility and benefits have found traction in numerous academic *** its inception in 2020,a plethora of MRFO-based research has been featured in esteemed international journals such as IEEE,Wiley,Elsevier,Springer,MDPI,Hindawi,and Taylor&Francis,as well as at international conference *** paper consolidates the available literature on MRFO applications,covering various adaptations like hybridized,improved,and other MRFO variants,alongside optimization *** trends indicate that 12%,31%,8%,and 49%of MRFO studies are distributed across these four categories respectively.

关键词： manta ray foraging optimization Metaheuristic algorithms Hybridization Improved optimization

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manta ray foraging optimization with Machine Learning Based Biomedical Data Classification

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Computers, Materials & Continua 2022年第11期73卷 3275-3290页

作者： Amal Al-Rasheed Jaber S.Alzahrani Majdy M.Eltahir Abdullah Mohamed Anwer Mustafa Hilal Abdelwahed Motwakel Abu Sarwar Zamani Mohamed I.Eldesouki Department of Information Systems College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityRiyadh11671Saudi Arabia Department of Industrial Engineering College of Engineering at AlqunfudahUmm Al-Qura UniversityMecca24382Saudi Arabia Department of Information Systems College of Science&Art at MahayilKing Khalid UniversityAbha62529Saudi Arabia Research Centre Future University in EgyptNew Cairo11845Egypt Department of Computer and Self Development Preparatory Year DeanshipPrince Sattam bin Abdulaziz UniversityAlKharjSaudi Arabia Department of Information System College of Computer Engineering and SciencesPrince Sattam bin Abdulaziz UniversityAlKharjSaudi Arabia

The biomedical data classification process has received significant attention in recent times due to a massive increase in the generation of healthcare data from various *** developments of artificial intelligence(AI)and machine learning(ML)models assist in the effectual design of medical data classification ***,this article concentrates on the development of optimal Stacked Long Short Term Memory Sequence-toSequence Autoencoder(OSAE-LSTM)model for biomedical data *** presented OSAE-LSTM model intends to classify the biomedical data for the existence of ***,the OSAE-LSTM model involves min-max normalization based pre-processing to scale the data into uniform *** by,the SAE-LSTM model is utilized for the detection and classification of diseases in biomedical *** last,manta ray foraging optimization(MRFO)algorithm has been employed for hyperparameter optimization *** utilization of MRFO algorithm assists in optimal selection of hypermeters involved in the SAE-LSTM *** simulation analysis of the OSAE-LSTM model has been tested using a set of benchmark medical datasets and the results reported the improvements of the OSAELSTM model over the other approaches under several dimensions.

关键词： Biomedical data classification deep learning manta ray foraging optimization healthcare machine learning artificial intelligence

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Fractional-Order Control of a Wind Turbine Using manta ray foraging optimization

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Computers, Materials & Continua 2021年第7期68卷 185-199页

作者： Hegazy Rezk Mohammed Mazen Alhato Mohemmed Alhaider Soufiene Bouallègue College of Engineering at Wadi Addawaser Prince Sattam Bin Abdulaziz UniversityAl-Kharj11911Saudi Arabia Department of Electrical Engineering Faculty of EngineeringMinia University61517MiniaEgypt Research Laboratory in Automatic Control(LARA) National Engineering School of Tunis(ENIT)University of TunisTunis1002Tunisia High Institute of Industrial Systems of Gabès University of GabèsGabès6011Tunisia

In this research paper,an improved strategy to enhance the performance of the DC-link voltage loop regulation in a Doubly Fed Induction Generator(DFIG)based wind energy system has been *** proposed strategy used the robust Fractional-Order(FO)Proportional-Integral(PI)control *** FOPI control contains a non-integer order which is preferred over the integer-order control owing to its *** offers extra flexibility in design and demonstrates superior outcomes such as high robustness and *** optimal gains of the FOPI controller have been determined using a recent manta ray foraging optimization(MRFO)*** the optimization process,the FOPI controller’s parameters are assigned to be the decision variables whereas the objective function is the error racking that to be *** prove the superiority of the MRFO algorithm,an empirical comparison study with the homologous particle swarm optimization and genetic algorithm is *** obtained results proved the superiority of the introduced strategy in tracking and control performances against various conditions such as voltage dips and wind speed variation.

关键词： Renewable energy modeling wind turbine doubly fed induction generator fractional order control manta ray foraging optimization

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