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Dimensional synthesis of a four-bar linkage for closed-path generation performed by the differential evolution algorithm combined with the marine predators algorithm

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MECHANICS BASED DESIGN OF STRUCTURES AND MACHINES 2025年

作者： Nikitovic, Aleksandra M. Ducic, Nedeljko G. Jeli, Zorana V. Bulatovic, Radovan R. Atanasovska, Ivana D. Univ Kragujevac Fac Tech Sci Cacak Cacak Serbia Univ Belgrade Fac Mech Engn Belgrade Serbia Univ Kragujevac Fac Mech & Civil Engn Kraljevo Dositejeva 19 Kraljevo 36000 Serbia Serbian Acad Arts & Sci Math Inst Belgrade Serbia

This article proposes a new approach to the synthesis of four-bar mechanisms by means of a new modified differential evolution (DE) algorithm developed by incorporating a part of the marine predators algorithm (MPA) into the corresponding DE algorithm strategy. This improves the strategy of the DE algorithm and enables it to find a solution in the global optimum space, thus contributing to the optimal performance of the algorithm. The proposed DE algorithm combined with the MPA was tested on three examples of the dimensional synthesis of the four-bar mechanisms for closed-path generation without prescribed timing, a large number of design variables being optimized per each problem. The proposed method ensures faster convergence, with a minimal chance of premature convergence in local optimum, which is not necessarily global optimum of the desired problem. The results of this research conducted on the examples provided in the article suggest that the objective function values are significantly lower if the proposed algorithm is used than if only the standard DE algorithm or the MPA is used to perform the synthesis. The proposed algorithm can be successfully used to solve very complex problems of both four-bar mechanisms and mechanisms of different geometry.

关键词： Differential evolution marine predators algorithm dimensional synthesis four-bar linkage

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Design of cascade P-P-FOPID controller based on marine predators algorithm for load frequency control of electric power systems

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ELECTRICAL ENGINEERING 2025年第1期107卷 809-827页

作者： Hussain, Jahanzeab Zou, Runmin Akhtar, Samina Abouda, Khalid A. Cent South Univ Sch Automat Changsha 410017 Peoples R China

As power network complexity increases, efficient control of frequency deviations and tie-line power fluctuations becomes crucial. Load frequency control (LFC) addresses these issues by managing generation-consumption mismatches. This paper introduces a unique P-P-FOPID cascade controller for LFC, combining a proportional controller with a fractional-order PID. The marine predators algorithm (MPA), known for its benefits like being parameter-less, derivative-free, user-friendly, flexible, and simple, is employed to optimize the controller's parameters using the integral time absolute error criterion. Tested on three power systems, the proposed controller outperforms single-structure FOPID controller based on MPA and recent approaches in reducing ITAE, settling time, and frequency and tie-line power deviations. In comparative analysis, the proposed MPA-tuned P-P-FOPID controller achieves a 60% and 11% decrease in ITAE compared to the second-best DSA-optimized FOPI-FOPD controller for Systems 1 and 2, respectively, and a 25% decrease compared to the second-best MPA-tuned FOPID controller for System 3. Furthermore, the controller's robustness against parametric uncertainties is confirmed through variations in fundamental parameters.

关键词： Load frequency control Interconnected power systems Fractional-order PID controller Cascade controller marine predators algorithm

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Advanced Parameter Identification in Electric Vehicles Lithium-Ion Batteries With marine predators algorithm-Based Optimization

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INTERNATIONAL JOURNAL OF ENERGY RESEARCH 2025年第1期2025卷

作者： Ghadbane, Houssam Eddine Rezk, Hegazy Alhumade, Hesham Univ 8 Mai 1945 Dept Electrotech & Automatique Lab Genie Elect Guelma LGEG Guelma 24000 Algeria Prince Sattam bin Abdulaziz Univ Coll Engn Wadi Alddawasir Dept Elect Engn Al Kharj Saudi Arabia Minia Univ Fac Engn Elect Engn Dept Al Minya 61517 Egypt King Abdulaziz Univ Fac Engn Chem & Mat Engn Dept Jeddah Saudi Arabia King Abdulaziz Univ Ctr Excellence Desalinat Technol Jeddah 21589 Saudi Arabia

Accurate parameter identification of lithium-ion (Li-ion) battery models is critical for understanding battery behavior and optimizing performance in electric vehicle (EV) applications. Traditional methods often rely on manual adjustments or trial-and-error processes, leading to inefficiencies and suboptimal outcomes. This study introduces a novel parameter identification approach using the marine predators algorithm (MPA), applied to a Shepherd model for EV applications. The proposed technique was validated under various dynamic test conditions, including the urban dynamic driving cycle (UDDC), the new European driving cycle (NEDC), and the worldwide harmonized light vehicles test procedure (WLTP). The MPA-based method systematically identifies optimal parameters, achieving a voltage error of 2.743 x 10-3, a state of charge (SOC) error of 0.7693 x 10-3, and a root mean square error (RMSE) of 8.37 x 10-3 between the model and real data. Compared to other optimization techniques, the MPA demonstrated superior performance, achieving an optimization efficiency of 97.69%. These results validate the robustness and reliability of the method for accurately capturing battery dynamics under realistic driving conditions. These results highlight the potential of the MPA-based approach in improving the accuracy of Li-ion battery parameter identification, leading to more efficient energy management in EVs and contributing to enhanced battery performance and reliability.

关键词： Li-ion battery marine predators algorithm metaheuristic optimization algorithms parameter identification

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Enhanced electricity price forecasting in smart grids using an optimized hybrid convolutional Multi-Layer Perceptron deep network with marine predators algorithm for feature selection

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ENERGY SOURCES PART B-ECONOMICS PLANNING AND POLICY 2025年第1期20卷

作者： Bakir, Abderrahim Rami, Abdelkader Univ Djillali Liabes Sidi Bel Abbes Elect Engn Dept IRECOM Lab Sidi Bel Abbes 22000 Algeria

The electricity market is rapidly evolving by integrating renewable energy and smart grids, making accurate electricity price forecasting essential. This study introduces a novel three-stage model to enhance short-term electricity price forecasting. The first stage involves extensive data preprocessing to manage data noise and volatility, including aggregation, feature engineering, and normalization. The second stage employs an advanced feature selection method combining the marine predators algorithm and Random Forest. This approach reduced dimensionality by 73% and improved model accuracy by 84% by identifying the most influential features. The final stage integrates a hybrid forecasting model that combines a Convolutional Neural Network with a Multi-Layer Perceptron, leveraging the strengths of both architectures. Bayesian Optimization is used to fine-tune hyperparameters for optimal performance. Implemented in MATLAB and tested on real-time data from Connecticut within ISO New England, the model showed superior results. For day-ahead forecasting, it achieved a Mean Absolute Percentage Error (MAPE) of 1.483%, a Daily Peak Mean Absolute Percentage Error (DP-MAPE) of 0.631%, a Mean Absolute Error (MAE) of $0.410 per megawatt-hour, a Root Mean Square Error (RMSE) of $0.440 per megawatt-hour, and a coefficient of determination (R2) of 0.992. For week-ahead forecasting, it achieved a MAPE of 1.413%, a DP-MAPE of 1.460%, an MAE of $1.256 per megawatt-hour, an RMSE of $1.879 per megawatt-hour, and an R2 of 0.999. These results demonstrate the robustness and accuracy of the model by outperforming reference models, providing precise short-term forecasts, and supporting informed decision-making.

关键词： Electricity price forecasting smart grid feature selection hybrid deep neural network Convolutional Neural Network Multi-Layer Perceptron marine predators algorithm

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Tropical Cyclone Intensity Prediction using Bayesian Machine Learning with marine predators algorithm on Satellite Cloud Imagery

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AIN SHAMS ENGINEERING JOURNAL 2025年第3期16卷

作者： Ragab, Mahmoud King Abdulaziz Univ Fac Comp & Informat Technol Informat Technol Dept Jeddah 21589 Saudi Arabia Al Azhar Univ Fac Sci Dept Math Cairo 11884 Egypt

Due to its wide range of associated hazards, tropical cyclones (TC) become the costliest natural disaster worldwide. A correct diagnosis model for the TC intensity can save property and lives. Unfortunately, intensity forecasting of TC has been a bottleneck and has made it difficult to forecast weather. Several existing approaches and techniques make a diagnosis of TC wind speed through the satellite data at the specified time with varying success. Deep learning (DL)-based intensity forecasting has recently held great promise in surpassing conventional approaches. DL-based techniques have been developed in geosciences to replace traditional methods. However, weather forecasting is uncertain due to the Earth system's nonlinearity, complexity, and chaotic effects. Thus, this manuscript develops a new Bayesian Machine Learning with marine predators algorithm for TC Intensity Prediction (BMLMPA-TCIP) approach. The major goal of the BMLMPA-TCIP model is to estimate the level of the TCs on satellite cloud images. To accomplish this, the BMLMPA-TCIP technique utilizes the Gaussian filtering (GF) approach to eradicate the noise in the cloud images. Additionally, the extraction of useful feature vectors is performed by using the capsule network (CapsNet) technique. Moreover, the MPA method accomplishes the hyperparameter tuning of the CapsNet method. Lastly, the BMLMPA-TCIP technique utilizes the Bayesian Belief Network (BBN) method to predict TC intensity. To authorize the performance of the BMLMPATCIP approach, a wide variety of experiments are performed under the TC image dataset. The experimental validation of the BMLMPA-TCIP approach illustrates a superior RMSE value of 5.89 over existing techniques.

关键词： Tropical Cyclones Machine Learning marine predators algorithm Bayesian Belief Network CapsNet

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An Adaptive Parameter Evolutionary marine predators algorithm for Joint Resource Scheduling of Cooperative Jamming Networked Radar Systems

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REMOTE SENSING 2025年第8期17卷 1325-1325页

作者： Lu, Dejiang Cheng, Siyi Chen, You Zhang, Xi Li, Haoyang Yang, Tianjian Air Force Engn Univ Aviat Engn Sch Xian 710038 Peoples R China

This paper investigates the formation joint resource scheduling problem from the perspective of cooperative jamming against radar systems. First, the formation survivability is redefined based on the task requirements. Then, a hierarchical adaptive scheduling strategy solution framework is constructed for state prediction and detection fusion of the networked radar system. Considering the scene constraints, an Improved Adaptive Parameter Evolution marine predators algorithm is designed as an optimizer and embedded in the proposed framework to jointly optimize the platform beam allocation and jamming mode selection. Based on the original algorithm, real number random coding is used to perform dimensional conversion of decision variables, an adaptive parameter evolution mechanism is designed to reduce the dependence on algorithm parameters, and an adaptive selection mechanism for dominant strategies and a search intensity control strategy are proposed to help decision-makers explore the optimal resource scheduling strategy quickly and accurately. Finally, considering the formation maneuvering behavior and incomplete information, the proposed method is compared with existing base strategies in different typical scenarios. It is proved that the proposed strategy can fully exploit the limited jamming resources and maximize the survivability of the formation in radar system cooperative jamming scenarios, demonstrating superior jamming performance and shorter decision time.

关键词： dynamic resource scheduling radar countermeasures networked radar systems marine predators algorithm

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marine predators algorithm and multi-verse optimisation algorithm for optimal battery case design of electric vehicles

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INTERNATIONAL JOURNAL OF VEHICLE DESIGN 2022年第1期88卷 1-11页

作者： Yildiz, Betul Sultan Bursa Uludag Univ Dept Elect & Energy TR-16059 Gorukle Turkey

This paper focuses on the optimum design of a battery case of an electric racing car. Two recently developed metaheuristics, which are marine predators algorithm (MPA) and the multi-verse optimisation (MVO) algorithm, are used to create an optimal design where the mass is considered as an objective function, and the geometric dimensions of the component is considered to be design variables. The kriging surrogate modelling is used to obtain the proxy model to increase the efficiency of the optimisation. The results show the robustness of the MPA in the optimum design of the electric car components. The MPA can be used in other product development processes.

关键词： MPA marine predators algorithm electric vehicles battery case shape design optimisation multi-verse optimisation algorithm topology optimisation vehicle design structural optimisation weight reduction metaheuristics

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marine predators algorithm Optimized Reduced Sensor Fuzzy-Logic Based Maximum Power Point Tracking of Fuel Cell-Battery Standalone Applications

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IEEE ACCESS 2021年 9卷 27987-28000页

作者： Aly, Mokhtar Ahmed, Emad M. Rezk, Hegazy Mohamed, Emad A. Aswan Univ Dept Elect Engn Fac Engn Aswan 81542 Egypt Univ Tecn Federico Santa Maria Elect Engn Dept Valparaiso 2390123 Chile Jouf Univ Dept Elect Engn Coll Engn Sakaka 72388 Saudi Arabia Aswan Univ Fac Engn AWCRC Aswan 81542 Egypt Prince Sattam Bin Abdulaziz Univ Coll Engn Wadi Addawaser Wadi Ad Dawasir 11991 Saudi Arabia Minia Univ Fac Engn Elect Engn Dept Al Minya 61517 Egypt

Fuel cell (FC) represents one of the promising efficient solutions for future energy supply. Improving performance and integration methods of FCs via maximum power point tracking (MPPT) and high boosting factor inverters are key requirements for research in renewable energy fields. Recently, hybrid FC-battery structures have shown wide applications in several areas. Accordingly, marine predators algorithm (MPA) is proposed in this article for optimizing the design of reduced sensor fuzzy-logic based MPPT scheme. The proposed scheme inherits the following benefits: reduced sensors and hence reduced costs, more flexibility and smooth performance due to fuzzy-logic based MPPT, and optimized design method of fuzzy-logic based MPPT through MPA method. Moreover, a high boosting ratio inverter is introduced in this article based on using the switched capacitor multilevel inverter (SCMLI). The proposed system achieves self capacitor voltage control without complex control or extra sensors. The proposed hybrid FC-battery system has been validated at various operating points. In addition, comprehensive comparisons with existing schemes in the literature are provided in the paper. The superiority of the proposed scheme has been verified with robust, fast and accurate tracking, reduced cost, flexible, simple, and smooth output waveforms. The proposed method achieves the lowest output power fluctuations with fast tracking speed compared to the studied classical methods.

关键词： Maximum power point trackers Boosting Inverters Batteries Voltage control Optimization Fuel cells Battery fuel cells (FCs) fuzzy logic control marine predators algorithm maximum power point tracking stand alone applications

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marine predators algorithm with Deep Learning-Based Leukemia Cancer Classification on Medical Images

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Computer Modeling in Engineering & Sciences 2024年第10期141卷 893-916页

作者： Sonali Das Saroja Kumar Rout Sujit Kumar Panda Pradyumna Kumar Mohapatra Abdulaziz S.Almazyad Muhammed Basheer Jasser Guojiang Xiong Ali Wagdy Mohamed Department of Computer Science&Engineering Gandhi Institute for TechnologyBPUTBhubaneswar752054India Department of Information Technology Vardhaman College of Engineering(Autonomous)Hyderabad501218TelanganaIndia Department of Electronic&Communication Engineering Vedang Institute of TechnologyBhubaneswar752010India Department of Computer Engineering College of Computer and Information SciencesKing Saud UniversityRiyadh11543Saudi Arabia Department of Computing and Information Systems School of Engineering and TechnologySunway UniversityJalan UniversitiBandar SunwaySelangor Darul Ehsan47500Malaysia Research Centre for Human-Machine Collaboration(HUMAC) School of Engineering and TechnologySunway UniversityJalan UniversitiBandar SunwaySelangor Darul Ehsan47500Malaysia Guizhou Key Laboratory of Intelligent Technology in Power System College of Electrical EngineeringGuizhou UniversityGuiyang550025China Operations Research Department Faculty of Graduate Studies for Statistical ResearchCairo UniversityGiza12613Egypt Applied Science Research Center Applied Science Private UniversityAmman11931Jordan

In blood or bone marrow,leukemia is a form of cancer.A person with leukemia has an expansion of white blood cells(WBCs).It primarily affects children and rarely affects *** depends on the type of leukemia and the extent to which cancer has established throughout the *** leukemia in the initial stage is vital to providing timely patient *** image-analysis-related approaches grant safer,quicker,and less costly solutions while ignoring the difficulties of these invasive *** can be simple to generalize Computer vision(CV)-based and image-processing techniques and eradicate human *** researchers have implemented computer-aided diagnosticmethods andmachine learning(ML)for laboratory image analysis,hopefully overcoming the limitations of late leukemia detection and determining its *** study establishes a marine predators algorithm with Deep Learning Leukemia Cancer Classification(MPADL-LCC)algorithm onMedical *** projectedMPADL-LCC system uses a bilateral filtering(BF)technique to pre-process medical *** MPADL-LCC system uses Faster SqueezeNet withmarine predators algorithm(MPA)as a hyperparameter optimizer for feature ***,the denoising autoencoder(DAE)methodology can be executed to accurately detect and classify leukemia *** hyperparameter tuning process using MPA helps enhance leukemia cancer classification *** results are compared with other recent approaches concerning various measurements and the MPADL-LCC algorithm exhibits the best results over other recent approaches.

关键词： Leukemia cancer medical imaging image classification deep learning marine predators algorithm

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marine predators algorithm for performance optimization of nanoscale FinFET

Marine predators algorithm for performance optimization of n...

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3rd International Conference on Advancement in Nanoelectronics and Communication Technologies (ICANCT)

作者： Kaur, Navneet Rattan, Munish Gill, Sandeep Singh Kaur, Gurpurneet Walia, Gurjot Kaur Kaur, Rajvir Guru Nanak Dev Engn Coll Ludhiana 141006 Punjab India NITTTR Chandigarh 160019 India

Fin-shaped Field Effect Transistors (FinFETs) have proved to be an effective solution when transistors are downscaled and conventional MOSFETs do not perform appreciably well in nanoscale regime. The current work emphasizes on the optimization of Rectzoidal FinFET design at shorter gate length and evaluates short channel effects for 20 nm gate length. Various device dimensions like fin width, fin height, equivalent gate oxide thickness, gate material work-function decide the device performance which is, in fact, measured by leakage current, on-current and short channel effects such as Subthreshold swing. Design and simulations for device have been executed in TCAD tool. After simulations, the performance of designed FinFET structure is optimized with the use of marine predators algorithm (MPA), which is an effective optimization technique inspired from foraging strategy opted by ocean predators. A dataset of fin height and fin width acts as input to artificial neural network, which outputs a trained network. Trained network along with the fitness function is inputted to MPA, and optimized output when verified with TCAD results, produces acceptable results. Fitness function tends to reduce the short channel effects and enhance on-current of device. Approximate optimized fin width and fin height values of designed FinFET are 5 nm and 24 nm respectively. This optimized miniaturized device design is appropriate for transistors which are further employed in numerous nanoscale applications. (C) 2022 Elsevier Ltd. All rights reserved.

关键词： FinFET Short Channel Effects marine predators algorithm Optimization Subthreshold Swing MATLAB

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