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Blade shape optimization of Savonius wind turbine using radial based function model and marine predator algorithm

ENERGY REPORTS 2022年 8卷 12366-12378页

作者： Xia, Huaijie Zhang, Song Jia, Rongyuan Qiu, Huadong Xu, Shuhui Qilu Univ Technol Shandong Acad Sci Sch Mech Engn Jinan 250353 Peoples R China Shandong Inst Mech Design & Res Jinan 250353 Peoples R China Shandong Shanke Shixin Technol Co Ltd Jining 273299 Peoples R China

In order to overcome the problems of low efficiency and poor results of classical optimization methods, an efficient, intelligent, and reliable method for optimizing the shape of the blades of the Savonius wind turbine is proposed in this paper. Firstly, the shape of the blade is parameterized by the third-order Bezier curve, and then a certain number of design schemes in the design space are obtained by the Latin hypercube sampling method and the torque coefficient of each design scheme is evaluated based on CFD simulation, and next a radial basis function surrogate model is constructed based on these design schemes and their torque coefficients, and finally the optimal design scheme is obtained by solving the surrogate model using the marine predator algorithm, and is verified using the CFD simulation. The results show that when the tip speed ratio is 1, the average torque coefficient of the optimized blade obtained by the above method is about 7% higher than that of the classical semicircular blade, and under other tip speed ratios, the average torque coefficients of the optimized blade is also significantly higher than that of the classical blade. Furthermore, compared with the classical semicircular blade, about 11% materials can be saved in the manufacturing of the optimized blade.(c) 2022 The Authors. Published by Elsevier Ltd. This is an open access article under the CC BY-NC-ND license (http://***/licenses/by-nc-nd/4.0/).

关键词： Blade optimization Savonius turbine CFD marine predator algorithm Radial basis function model Latin hypercube sampling Bezier curve

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China's carbon dioxide emission forecast based on improved marine predator algorithm and multi-kernel support vector regression

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ENVIRONMENTAL SCIENCE AND POLLUTION RESEARCH 2023年第3期30卷 5730-5748页

作者： Qin, Xiwen Zhang, Siqi Dong, Xiaogang Zhan, Yichang Wang, Rui Xu, Dingxin Changchun Univ Technol Sch Math & Stat 2055 Yanan St Changchun 130012 Peoples R China Changchun Univ Technol Grad Sch 2055 Yanan St Changchun 130012 Peoples R China

Global warming has constituted a major global problem. Carbon dioxide emissions from the burning of fossil fuels are the main cause of global warming. Therefore, carbon dioxide emission forecasting has attracted widespread attention. Aiming at the problem of carbon dioxide emissions forecasting, this paper proposes a new hybrid forecasting model of carbon dioxide emissions, which combines the marine predator algorithm (MPA) and multi-kernel support vector regression. For further strengthening the prediction accuracy, a novel variant of MPA is proposed, called EGMPA, which introduces the elite opposition-based learning strategy and the golden sine algorithm into MPA. algorithm test results show that EGMPA can effectively improve the convergence speed and optimization accuracy. The carbon dioxide emission data of China from 1965 to 2020 are taken as the research objects. Root-mean-square error (RMSE), mean absolute error (MAE), and mean absolute percentage error (MAPE) are used to evaluate the performance of the proposed model. The proposed multi-kernel support vector regression model is used to forecast China's carbon dioxide emissions during the "14th Five-Year Plan" period. The results show that the proposed model has RMSE of 37.43 Mt, MAE of 30.63 Mt, and MAPE of 0.32%, which significantly improves the prediction accuracy and can accurately and effectively predict China's carbon dioxide emissions. During the "14th Five-Year Plan" period, China's carbon dioxide emissions will continue to show an increasing trend, but the growth rate will slow down significantly.

关键词： Carbon dioxide emissions Multi-kernel support vector regression marine predator algorithm Elite opposition-based learning strategy Golden sine algorithm

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Optimal Fuzzy PIDF Load Frequency Controller for Hybrid Microgrid System Using marine predator algorithm

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IEEE ACCESS 2021年 9卷 54220-54232页

作者： Yakout, Ahmed H. Kotb, Hossam Hasanien, Hany M. Aboras, Kareem M. Ain Shams Univ Fac Engn Dept Elect Engn Cairo 11517 Egypt Alexandria Univ Fac Engn Dept Elect Engn Alexandria 21544 Egypt Ain Shams Univ Fac Engn Elect Power & Machines Dept Cairo 11517 Egypt

This paper presents a fully optimized fuzzy proportional-integral-derivative with filter (FPIDF) load frequency controller (LFC) for enhancing the performance of a hybrid microgrid system. The marine predator algorithm (MPA), a recent optimization algorithm, is used to optimize the gains as well as the input scaling factors and membership functions of the proposed fuzzy PIDF controller. The controller performance is tested on a two-area hybrid microgrid system containing various renewable energy sources and energy storage devices. The effectiveness of the MPA based FPIDF controller is compared with conventional PIDF and FPIDF controllers based on other heuristic techniques presented in literatures. Moreover, different scenarios are implemented in this study to verify the robustness and sensitivity of the proposed controller to different step load perturbations, system parameters variations, wind speed fluctuation and solar irradiance variation. The dynamic response of the system is compared using different controllers in terms of settling time, maximum overshoot and undershoot values. The results are presented in the form of time domain simulations conducted via MATLAB/SIMULINK.

关键词： Fuzzy control load frequency control marine predator algorithm microgrid system PIDF controller renewable energy storage systems

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Evolving deep gated recurrent unit using improved marine predator algorithm for profit prediction based on financial accounting information system

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COMPLEX & INTELLIGENT SYSTEMS 2024年第1期10卷 595-611页

作者： Li, Xue Khishe, Mohammad Qian, Leren North China Univ Water Resources & Elect Power Sch Management & Econ Zhengzhou 450045 Henan Peoples R China Imam Khomeini Marine Sci Univ Dept Elect Engn Nowshahr Iran Arizona State Univ Sch Comp & Augmented Intelligence Tempe AZ 85281 USA

This research proposes a hybrid improved marine predator algorithm (IMPA) and deep gated recurrent unit (DGRU) model for profit prediction in financial accounting information systems (FAIS). The study addresses the challenge of real-time processing performance caused by the increasing complexity of hybrid networks due to the growing size of datasets. To enable effective comparison, a new dataset is created using 15 input parameters from the original Chinese stock market Kaggle dataset. Additionally, five DGRU-based models are developed, including chaotic MPA (CMPA) and the nonlinear MPA (NMPA), as well as the best Levy-based variants, such as the dynamic Levy flight chimp optimization algorithm (DLFCHOA) and the Levy-base gray wolf optimization algorithm (LGWO). The results indicate that the most accurate model for profit forecasting among the tested algorithms is DGRU-IMPA, followed by DGRU-NMPA, DGRU-LGWO, DGRU-DLFCHOA, DGRU-CMPA, and traditional DGRU. The findings highlight the potential of the proposed hybrid model to improve profit prediction accuracy in FAIS, leading to enhanced decision-making and financial management.

关键词： Deep gated recurrent unit Financial accounting information Profit prediction marine predator algorithm

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Dynamic Lévy-Brownian marine predator algorithm for photovoltaic model parameters optimization

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SCIENTIFIC REPORTS 2024年第1期14卷 1-26页

作者： Bouteraa, Yassine Khishe, Mohammad Prince Sattam Bin Abdulaziz Univ Coll Comp Engn & Sci Dept Comp Engn Al Kharj 11942 Saudi Arabia Yuan Ze Univ Innovat Ctr Artificial Intelligence Applicat Taoyuan City Taiwan Appl Sci Private Univ Appl Sci Res Ctr Amman Jordan

The dynamic and multimodal nature of photovoltaic (PV) systems makes it challenging to examine all solar photovoltaic characteristics. Consequently, this study recommends a recently developed optimization method called the marine predator algorithm (MPA) for developing reliable PV models. In the traditional MPA, the two main search processes are L & eacute;vy flight (LF) and Brownian walk (BW), and the switch across them is unpredictable. This is while the transition between these two mechanisms is naturally continuous and dynamic. To rectify the limitation mentioned above, this research paper presents an innovative, dynamic shift function that effectively modulates the interplay that exists between the BW and LF procedures. By enhancing the changeover pattern between the primary phases of MPA, the suggested dynamic walk substantially boosts the performance of MPA. The dynamic L & eacute;vy-Brownian MPA (DLBMPA) is also made to be resilient in dealing with the parameterization limitations of PV Modeling approaches by using a constraint handling technique. The performance of DLBMPA is tested using ten popular optimization methods. Employing the DLBMPA achieved an average RMSE of 9.7 x 10(- 4) in the parameter estimation across a number of multiple PV models, including the SDM, DDM, and TDM, where out of the ten optimization algorithms experimented, this was statistically significant (p < 0.05) better. In terms of averaged computation time, DLBMPA was 13 ms and still showed high accuracy in dealing with different irradiance and temperature levels. These improvements allow for MBPA to be credited as having a high efficiency when estimating the PV parameters since its speed of convergence and accuracy level surpass the previous techniques used.

关键词： Dynamic L & eacute vy-Brownian marine predator algorithm Photovoltaic models Solar cell

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A New Improved Model of marine predator algorithm for Optimization Problems

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ARABIAN JOURNAL FOR SCIENCE AND ENGINEERING 2021年第9期46卷 8803-8826页

作者： Ramezani, Mehdi Bahmanyar, Danial Razmjooy, Navid Tafresh Univ Dept Elect & Control Engn Tafresh *** Iran Rooigemlaan 154-0201 Ghent Belgium

The marine predator algorithm is a new nature-inspired metaheuristic algorithm that mimics biological interaction between marine predators and prey. It has been also stated from the literature that this algorithm can solve many real-world optimization problems which made it a new popular optimization technique for the researchers. However, there is still a deficiency in the marine predator algorithm such as the inability to produce a diverse initial population with high productivity, lack of quick escaping of the local optimization, and lack of widely and broadly exploration of the search space. In the present study, a developed version of this algorithm is proposed based on the opposition-based learning method, chaos map, self-adaptive of population, and switching between exploration and exploitation phases. The simulations are performed using MATLAB environment on standard test functions including CEC-06 2019 tests and a real-world optimization problem based on PID control applied to a DC motor to evaluate the performance of the suggested algorithm. The simulation results are compared with the original marine predator algorithm and five state-of-the-art optimization algorithms namely Particle Swarm Optimization, Grasshopper Optimization algorithm, JAYA algorithm, Equilibrium optimizer algorithm, Whale Optimization algorithm, Differential Search algorithm, and League Championship algorithm. Eventually, the simulation results proved that the suggested algorithm has better results compared with other algorithms for the studied case studies.

关键词： Optimization marine predator algorithm PID control DC motor CEC-06 2019 tests

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Multi-objective optimal allocation of water resources based on improved marine predator algorithm and entropy weighting method

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EARTH SCIENCE INFORMATICS 2024年第2期17卷 1483-1499页

作者： Wang, Zhaocai Zhao, Haifeng Bao, Xiaoguang Wu, Tunhua Shanghai Ocean Univ Coll Informat Shanghai 201306 Peoples R China Wenzhou Business Coll Sch Informat Engn 588 Meiquan St Wenzhou 325035 Peoples R China

Water resources play an essential role in achieving a multifaceted development society, and their superiority allocation affects the development rate of cities. The model of this research for allocating optimal water resources is constructed with objectives including social, economic, and ecological objectives, and the constraints including water supply, water demand, water transmission, and non-negativity, based on which the objectives are integrated using the Pareto front, and the dimensionless processing and entropy weighting method. Next, the improved marine predator algorithm (IMPA), which uses chaos initialization in the initial population, incorporates the golden sine algorithm in the process seeking and enhances the search capability using the quadratic interpolation method in the result comparison, is contrasted with several algorithms based on different functions for optimal values, standard deviation, and mean values. Then, using Huaying City as the research area, the water distribution scheme for the region in 2021 is obtained. The allocation schemes of local confirm the superiority of IMPA in terms of accuracy and stability, which provides a new idea for water allocation in Huaying City. The results of the experiment show that IMPA is an effective and available choice for solving water resources optimization researches.

关键词： Water resources allocation Multi-objective optimization marine predator algorithm Entropy weighting method Quadratic interpolation Pareto frontier

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Intelligent optimization of steam gasification catalysts for palm oil waste using support vector machine and adaptive transition marine predator algorithm

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COMPLEX & INTELLIGENT SYSTEMS 2024年第5期10卷 6283-6303页

作者： Guo, Xin Bouteraa, Yassine Khishe, Mohammad Li, Cen Martin, Diego Coll Yiyang Normal Coll Modern Educ Technol Yiyang 413000 Hunan Peoples R China Prince Sattam Bin Abdulaziz Univ Coll Comp Engn & Sci Dept Comp Engn Al Kharj 11942 Saudi Arabia Appl Sci Private Univ Appl Sci Res Ctr Amman Jordan Yuan Ze Univ Innovat Ctr Artificial Intelligence Applicat Taoyuan Taiwan Zhangjiajie Coll Student Affairs Off Zhangjiajie 427000 Hunan Peoples R China Univ Politecn Madrid ETSI Telecomunicac Ave Complutense 30 Madrid 28040 Spain

The present study investigates the optimization of the steam gasification process for the conversion of palm oil waste into environmentally friendly energy, utilizing the catalytic properties of calcium oxide and coal bottom ash. The objective of our research is to investigate the enhancement of the conversion process by employing a machine-learning approach. Specifically, we utilize a support vector machine (SVM) to model and evaluate the impact of different operational parameters on the resulting gas mixture. One notable feature of this study involves the incorporation of an adaptive marine predator algorithm (AMPA) into the SVM framework, aiming to enhance the predicted precision and efficiency of the model. The primary focus of this study revolves around the development of an intelligent optimization framework that surpasses conventional machine learning techniques, hence providing a more dynamic and efficient strategy for process improvement. The SVM model's performance, as assessed against experimental benchmarks, exhibits a notable degree of predictive accuracy and substantial concurrence with observed data. This increase in performance indicates that our methodology has the potential to make a significant contribution to the enhancement of renewable catalysts in gasification processes. The findings of this study could potentially have significant ramifications for the advancement of renewable energy production and the creation of intelligent systems in complicated industrial applications.

关键词： marine predator algorithm Waste materials Steam gasification Support vector machine

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marine predator algorithm with elite strategies for engineering design problems

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CONCURRENCY AND COMPUTATION-PRACTICE & EXPERIENCE 2023年第7期35卷 e7612-e7612页

作者： Aydemir, Salih Berkan Onay, Funda Kutlu Amasya Univ Dept Comp Engn Amasya Turkiye

marine predator algorithm (MPA) is a powerful metaheuristic optimization algorithm that shows effective convergence ability on complex benchmark functions. The combination of Brownian and Levy flight distributions directly affects the convergence strategy of MPA. Although MPA has good convergence performance, it is open to improvement as it falls to a local optimum and cannot comprehensively scan the search area during the exploration phase. In this study, MPA has been improved by integrating elite natural evolution and elite random mutation strategies. In addition, these two strategies are combined with Gaussian mutation. The proposed method in this study which is named as elite evolution strategy MPA (EEMPA) has achieved comprehensive scanning of the solution space and considerably reduced the risk of falling into the local optimum trap, with elite strategies. The effect of EEMPA has been tested with the CEC2017 and CEC2019 benchmark functions. EEMPA has been compared with some metaheuristic algorithms frequently used in the literature and gives promising results among the considered optimization methods. Furthermore, EEMPA has been examined for seven well-known real world engineering problems. When the results are compared with both classical MPA and enhanced MPA methods, EEMPA converges to better than the other methods.

关键词： benchmark function elite evolution strategy engineering problems marine predator algorithm metaheuristic algorithm

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Real-Time Implementation of an Enhanced PID CONTROLLER based on marine predator algorithm (MPA) for Micro-robotics System 3

Real-Time Implementation of an Enhanced PID CONTROLLER based...

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3rd International Conference on Artificial Intelligence, Robotics and Control (AIRC)

作者： Ghith, Ehab Seif Tolba, Farid Abdel Aziz Ain Shams Univ Fac Engn Dept Mechatron Cairo Egypt

ISBN: (数字)9781665459471

ISBN: (纸本)9781665459471

This paper presents a new approach to control the position of the micro-robotics system with a proportional-integral-derivative (PID) controller. By using marine predator algorithm (MPA), the optimal PID controller indicators were obtained by applying a new objective function namely, integral square time multiplied square error (ISTES). The efficiency of the proposed MPA-based controller was verified by comparisons made with Grey Wolf Optimization (GWO) algorithm-based controllers in terms of time. Each control technique will be applied to the identified model using MATLAB Simulink and the experimental test facility was conducted using LABVIEW software. The simulation and experimental results show that the performance of MPA-PID controller based on ISTES cost function achieves the best performance among various techniques. Moreover, the MPA technique had the highest performance compared to GWO technique based on rising time, settling time and settling error. Thus, it is recommended to apply MPA for tuning the parameters of PID as it can enhance its performance in micro-robotic systems. It was found that the amount of error is reduced by 33.75% using MPA than other former experiments

关键词： PID Controller Micro-particles Robotics Grey Wolf Optimization marine predator algorithm Minimally invasive surgery

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