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Parameter extraction of solar photovoltaic module by using a novel hybrid marine predators - success history based adaptive differential evolution algorithm

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ENERGY SOURCES PART A-RECOVERY UTILIZATION AND ENVIRONMENTAL EFFECTS 2024年第1期46卷 14550-14572页

作者： Naraharisetti, Jaya Naga Lakshmi Devarapalli, Ramesh Bathina, Venkateswararao SR Engn Coll Dept EEE Warangal Andhra Pradesh India Indian Inst Technol ISM Dept Elect Engn Dhanbad Jharkhand India VR Siddhartha Engn Coll Dept EEE Vijayawada India

The manufacturers of photovoltaic (PV) panel give the data of three major points on I-V characteristics. However, this information alone is not sufficient to derive the five parameter and seven parameter models, i.e., single-diode and double-diode models. Hence, several population-based metaheuristic techniques are proposed in the literature. However, there is a need of well-balanced algorithm which is used for extracting the parameters of the diode model of PV panels from the datasheet. In this paper, a novel hybrid algorithm is proposed by combing the best features of a recently developed marine predators algorithm (MPA) and Success History based Adaptive Differential Evolution (SHADE) algorithm. The principal algorithm for obtaining the optimum solution is MPA. However, during the search process to enhance the best solution region, self-adaptive DE based on the successive history of parameters is used. The derived objective function ensures the zero error at three important points of the I-V characteristics. Hence, the parameters extracted by using proposed method results in the I-V curves which are passing through the all three important points. Only three parameters out of five in single-diode model, and five parameters out of seven are optimized with the proposed algorithm and remaining are calculated analytically to reduce the burden on metaheuristic algorithm. MATLAB programming is used to test the proposed parameter extraction by using hybrid marine predators - Success History based Adaptive Differential Evolution (MP-SHADE) algorithm, and the results are compared with the other state-of-the-art metaheuristic techniques. The single-diode and double-diode models of three types of panels (monocrystalline, polycrystalline, and thin-film) are derived by using MP-SHADE algorithm.

关键词： marine predators algorithm metaheuristic techniques PV module parameter extraction shade algorithm

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A reliable approach for modeling the photovoltaic system under partial shading conditions using three diode model and hybrid marine predators-slime mould algorithm

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ENERGY CONVERSION AND MANAGEMENT 2021年 243卷 114269-114269页

作者： Yousri, Dalia Fathy, Ahmed Rezk, Hegazy Babu, Thanikanti Sudhakar Berber, Mohamed R. Fayoum Univ Fac Engn Elect Engn Dept Al Fayyum Egypt Jouf Univ Fac Engn Elect Engn Dept Al Jouf Saudi Arabia Zagazig Univ Elect Power & Machine Dept Fac Engn Zagazig Egypt Prince Sattam Bin Abdulaziz Univ Coll Engn Wadi Addawaser Al Kharj Saudi Arabia Minia Univ Fac Engn Elect Engn Dept Al Minya Egypt Chaitanya Bharathi Inst Technol Dept Elect & Elect Engn Hyderabad 500075 India Jouf Univ Coll Sci Chem Dept Al Jouf Saudi Arabia

In this article, the triple diode model (TDM) is studied for modeling the Canadian-Solar-CS6P-240P poly-crystalline PV module, Kyocera Solar KC200GT multi-crystalline PV module, Sharp NU-(Q250W2) mono-crystalline PV module, and Pythagoras Solar Large PVGU Window mono-crystalline PV module. A novel hybrid algorithm of the marine predator's algorithm (MPA) and slime mould algorithm (SMA) (HMPA) is proposed to enhance the MPA exploitation phase while identifying the TDM parameters. The HMPA results are compared to several recent algorithms that are equilibrium optimizer (EO), manta ray foraging optimization (MRFO), transient search optimization (TSO), jellyfish optimizer (JS), and forensic-based optimizer (FBI), besides the basic versions of MPA and SMA. For unbiased comparison, several statistical analyses and non-parametric tests are applied. The convergence curves are used to evaluate the convergence property of the proposed algorithm compared to their counterparts. The HMPA confirms its efficiency in handling the complex multi-modal and multi-dimensional optimization process of identifying the TDM parameters. HMPA provides the least root mean square error (RMSE) between the measured and estimated datasets with the least standard deviation (STD). For Canadian Solar (CS6P-240P) module, the proposed HMPA achieves the minimum RMSE of 0.00037313 with STD of 0.0030488;for Kyocera Solar (KC200GT) module, HMPA attains RMSE +/- STD of 0.0033042 +/- 0.0061813. For SharpNU-(Q250W2) PV module and Pythagoras Solar Large PVGU Window, HMPA outperforms the other counterparts with RMSEs +/- STDs of 0.00027661 +/- 0.0053002 and 0.00285 +/- 0.0020075, respectively. Accordingly, the HMPA provides the slightest deviation between the estimated datasets and the experimental ones with high consistency over several independent runs. The convergence curves of the proposed HMPA affirm its fast response while handling the optimization problem of TDM. The reliability of the identified paramete

关键词： Triple diode model Photovoltaic Parameters estimation marine predators algorithm Slime mould algorithm

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A Novel Hybrid Quantum Particle Swarm Optimization With marine predators for Engineering Design Problems

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IEEE ACCESS 2022年 10卷 129322-129343页

作者： Qin, Chuandong Han, Baole North Minzu Univ Sch Math & Informat Sci Yinchuan 750021 Peoples R China Ningxia Key Lab Intelligent Informat & Big Data Pr Yinchuan 750021 Peoples R China

computational efficiency of the quantum particle swarm optimization (QPSO) is significantly higher than that of the traditional particle swarm optimization when solving the parameters of the optimization problem model. However, the exploration and exploitation abilities of the QPSO are weak, based on the historical best position and the global best position. Enlightened by the multi-stage search strategies of marine predators algorithm, we propose a novel hybrid quantum particle swarm optimization algorithm with marine predators (HMPQPSO) in this paper. The evolutionary process of the algorithm is divided two stages: firstly, the Brownian motion of the predator is introduced to the exploration. The randomness and uniformity of which can expand the solution space of particles;secondly, the Levy motion strategy and dynamic parameter strategy are used to update the position, which can accelerate the convergence of the algorithm and enhance the diversity of the algorithm. Meanwhile, both fish aggregation devices(FADs) and opposition-based learning strategy incorporated are used to increase the diversity of the population and prevent the phenomenon of premature particle aggregation. The algorithm is applied to distinct types of CEC2017 benchmark test functions and four multidimensional nonlinear structure design optimization problems, as compared to other recent algorithms. The results demonstrate that the convergence speed and accuracy of HMPQPSO are notably superior to that of other algorithms.

关键词： marine predators algorithm motion strategy particle swarm optimization structural design optimization

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marine predators social group optimization: a hybrid approach

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EVOLUTIONARY INTELLIGENCE 2024年第4期17卷 2355-2386页

作者： Naik, Anima Raghu Engn Coll Dept CSE Visakhapatnam 530003 Andhra Pradesh India

Numerous heuristic and metaheuristic algorithms inspired by nature have been developed so far. These algorithms have demonstrated their excellence in resolving challenging problems across a variety of fields. However, many optimization algorithms fail while solving a complex real-world problem efficiently in an independent way because these problems have a distinct search behavior as they contain numerous equality and inequality constraints of the linear, nonlinear, and non-convex types. In this study, two optimization algorithms: the marine predators algorithm (MPA) and the Social Group Optimization (SGO) algorithm are hybridized in order to increase their efficiency and problem-solving capability to solve this type of problem. This proposed hybrid optimization algorithm is named as marine predators Social Group Optimization (MPSGO) algorithm. This hybrid algorithm combines the strengths of both techniques to enhance the efficiency and effectiveness of the optimization process. It is validated through thirty benchmark functions of CEC2014 and after that 26 real-world optimization problems test suites of CEC 2020 from the chemical and mechanical engineering domain have been solved. The validation results are compared with ten state-of-art optimization algorithm and real-world optimization problem result is compared with BiPop matrix adaptation evolution strategy (BP-epsilon MAg-ES), enhanced Multi-Operator Differential Evolution (EnMODE), LSHADE for Constrained Optimization (COLSHADE), Cohort Intelligence self-adaptive penalty function (CI-SAPF), and Cohort Intelligence self-adaptive penalty function Colliding Bodies Optimization (CI-SAPF-CBO). As MPSGO is a hybrid algorithm, again we have compared its performance with the seven best modified and improved optimization algorithms. In terms of convergence quality and improving solution quality, the simulated results of MPSGO have been shown to be competitive with those of state-of-the-art, hybrid, improved, and advanc

关键词： marine predators algorithm Social group optimization Metaheuristic algorithms Hybridization Real-world optimization problems

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New binary marine predators optimization algorithms for 0-1 knapsack problems

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COMPUTERS & INDUSTRIAL ENGINEERING 2021年 151卷 106949-106949页

作者： Abdel-Basset, Mohamed Mohamed, Reda Chakrabortty, Ripon K. Ryan, Michael Mirjalili, Seyedali Zagazig Univ Fac Comp & Informat Sharqiyah 44519 Egypt UNSW Canberra Sch Engn & IT Capabil Syst Ctr Canberra ACT Australia Torrens Univ Australia Ctr Artificial Intelligence Res & Optimisat Brisbane Qld 4006 Australia Yonsei Univ YFL Yonsei Frontier Lab Seoul South Korea King Abdulaziz Univ Jeddah Saudi Arabia

Recently, a novel meta-heuristic algorithm known as the marine predators algorithm (MPA) has been proposed for solving continuous optimization problems. Despite the significant superiority of MPA in solving continuous problems, the algorithm is not applicable to binary problems. This work proposes a binary version of MPA for solving the 0-1 knapsack (KP01) problem. To develop the binary variant of MPA (BMPA), a wide range of V-Shaped and S-shaped transfer functions is investigated for mapping continuous values to binary. The performance of a binary algorithm is first shown to heavily rely on the selection of an appropriate transfer function on a specific dataset. The performance of the proposed BMPA algorithm is then tested on a set of KP01 problems and compared to a number of existing algorithms. The results demonstate the merits of the BMPAs proposed in this work.

关键词： 0-1 knapsack problem marine predators algorithm algorithm Artificial Intelligence Repair infeasible solutions Binary optimization Benchmark Combinatorial optimization

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Towards Optimized IoT Sensor Networks for Smart Cities: Centrality-Aware Position-Based Occlusion-Driven and Role Dynamics Solutions for Clustering and Routing

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IEEE Internet of Things Journal 2025年

作者： Darabkh, Khalid A. Al-Akhras, Muna The University of Jordan Computer Engineering Department School of Engineering Jordan

This paper introduces a novel clustering and routing protocol for Internet of Things (IoT) sensor networks aimed at reducing energy consumption and extending the lifespan, a critical requirement for smart cities. The proposed protocol demonstrates significant potential in diverse IoT applications, including smart logistics parks, smart medical centers, smart factory zones, smart universities, and smart cities. By ensuring energy-efficient, long-lasting sensor networks, the proposed protocol supports continuous real-time data collection and monitoring, enabling cities to achieve sustainability goals, enhance urban resilience, and improve operational efficiency. The protocol dynamically divides the sensing area into equal-sized Hexagonal Clusters (HCs), adapting to variations in network size, Sensor Node (SN) count, and their distribution. Each HC is managed by a Cluster Head (CH), selected using the marine predators algorithm (MPA), which excels in optimization tasks. The MPA algorithm determines the optimal CH by evaluating four key cluster fitness function parameters: the SNs’ distance to the sink, the Average Scale of Building Occlusions (ASBO), the SN centrality, and the CH role count. Furthermore, our proposed protocol incorporates an innovative relay cost function for the purpose of data forwarding, where CHs transmit data hop-by-hop to the sink, selecting relay nodes based on their role count, distance from the sink, ASBO, and distance from other CHs. Simulation results validate the protocol’s effectiveness, demonstrating its superior performance in extending network lifespan, increasing throughput, reducing average delay, and conserving energy. The proposed protocol outperforms existing protocols, establishing a new benchmark for energy-efficient clustering and routing in IoT applications. © 2014 IEEE.

关键词： Cluster Fitness Function Dynamic Clustering Management Energy Efficiency Internet of Things marine predators algorithm Network Lifespan Routing

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Improved multi-core arithmetic optimization algorithm-based ensemble mutation for multidisciplinary applications

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JOURNAL OF INTELLIGENT MANUFACTURING 2023年第4期34卷 1833-1874页

作者： Abualigah, Laith Diabat, Ali Amman Arab Univ Fac Comp Sci & Informat Amman 11953 Jordan Univ Sains Malaysia Sch Comp Sci George Town 11800 Malaysia New York Univ Abu Dhabi Div Engn Abu Dhabi 129188 U Arab Emirates NYU Tandon Sch Engn Dept Civil & Urban Engn Brooklyn NY 11201 USA

This paper proposes a new search method based on an augmented version of the Arithmetic Optimization algorithm to solve various benchmark functions, engineering design cases, and feature selection problems. The proposed method is called MCAOA, combined with the marine predators algorithm and a new proposed Ensemble Mutation Strategy. The Arithmetic Optimization algorithm is a new meta-heuristic technique used to solve optimization problems. Sometimes, Arithmetic Optimization algorithm faces convergence problems and falls into local optima for specific optimization problems, especially large-scale and multimodal problems. The marine predators algorithm and Ensemble Mutation Strategy improve the Arithmetic Optimization algorithm's convergence rate and equilibrium in the exploration and exploitation search methods. The proposed method is tested on 23 different benchmark functions, seven common engineering design cases, and sixteen feature selection problems. The obtained results are compared with other well-known and state-of-the-art methods. The experimental results indicated that the proposed method found new best solutions for different complicated problems;the general performance is promising compared to other comparative methods.

关键词： Arithmetic optimization algorithm (AOA) Ensemble mutation marine predators algorithm Engineering design problems Feature selection Global optimization

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Improved machine learning technique for feature reduction and its application in spam email detection

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JOURNAL OF INTELLIGENT INFORMATION SYSTEMS 2024年第6期62卷 1749-1771页

作者： Ewees, Ahmed A. Gaheen, Marwa A. Alshahrani, Mohammed M. Anter, Ahmed M. Ismail, Fatma H. Univ Bisha Coll Comp & Informat Technol Dept Informat Syst & Cybersecur POB 551 Bisha 61922 Saudi Arabia Damietta Univ Dept Comp Dumyat Egypt Beni Suef Univ Fac Comp & Artificial Intelligence Benisuef 62511 Egypt Misr Int Univ Fac Comp Sci Cairo 11341 Egypt

This paper introduces MPAG, a new feature selection method aimed at overcoming the limitations of the conventional marine predators algorithm (MPA). The MPA may experience stagnation and become trapped in local optima during optimization. To address this challenge, we propose a refined version of the MPA, termed MPAG, which incorporates the Local Escape Operator (LEO) from the gradient-based optimizer (GBO). By leveraging the LEO operator, MPAG enhances the exploration ability of the MPA, particularly during the initial one-third of iterations. This enhancement injects more diversity into populations, thereby improving the process of search space discovery and mitigating the risk of premature convergence. The performance of MPAG is evaluated on 14 feature selection benchmark datasets, employing seven performance measures including fitness value, classification accuracy, and selected features. Our findings indicate that MPAG outperforms other algorithms in 86% of the datasets, underscoring its capability to select the most relevant features across various datasets while maintaining stability. Additionally, MPAG is evaluated using two cybersecurity applications, specifically spam detection datasets, where it demonstrates superior performance across most performance measures compared to other methods.

关键词： marine predators algorithm Gradient based optimizer Feature selection Spam detection Cyber security

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Data centre day-ahead energy demand prediction and energy dispatch with solar PV integration

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ENERGY REPORTS 2021年 7卷 3760-3774页

作者： Ajayi, Oluwafemi Heymann, Reolyn Univ Johannesburg Dept Elect & Elect Engn ZA-2092 Johannesburg South Africa Univ Johannesburg Ctr Collaborat Digital Networks Johannesburg South Africa

This paper presents a novel marine predators algorithm for both training an Artificial Neural Network model used for predicting the energy demand and for solving a dynamic combined economic and emission dispatch of a data centre. The MPA is proposed for first finding the optimal weights and biases of the neural network based on a Mean Squared Error and Mean Absolute Error minimization objective function. Real life dataset obtained from an anonymous data centre operator in Cape Town, South Africa was used for the model implementation. The dataset was made up of a total of 564 samples and was split into training and testing set using an 80:20 ratio. The input variables contained in the dataset are the data centre's ambient temperature, ambient relative humidity, chiller output temperature and Computer Room Air Conditioning supply temperature while the energy demand is the target variable. The optimal weights of the neural network model were analysed using a weights based approach to determine the level of influence each input parameter of the model has on the data centre's energy demand. Then based on the predicted energy demand of the data centre, a dynamic economic and emission dispatch problem is solved for the building while considering thermal and solar photovoltaic generations. A spinning reserve is also incorporated in the energy dispatch model to cater for any shortfall that may exist between the predicted and actual energy demand of the data centre due to possible inaccuracies in the energy demand prediction model. Results for the energy demand prediction task showed that the proposed method outperformed the state-of-the-art by producing the least Mean Squared Error, Root Mean Squared Error, Mean Absolute Error, Mean Absolute Percentage Error and highest prediction accuracy for the training and testing sets. Further analyses also highlighted that the data centre's ambient temperature has the highest influence of about 37.63% on its energy demand pattern. For

关键词： Artificial Neural Networks Data Centre Economic and Emission Dispatch Energy Demand Forecasting marine predators algorithm

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Inverse problem for dynamic structural health monitoring based on slime mould algorithm

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ENGINEERING WITH COMPUTERS 2022年第SUPPL 3期38卷 2205-2228页

作者： Tiachacht, Samir Khatir, Samir Cuong Le Thanh Rao, Ravipudi Venkata Mirjalili, Seyedali Wahab, Magd Abdel Mouloud Mammeri Univ Tizi Ouzou Lab Mech Struct & Energet LMSE BP 17 RP Tizi Ouzou 15000 Algeria Univ Ghent Fac Engn & Architecture Soete Lab Technol Pk Zwijnaarde 903 B-9052 Zwijnaarde Belgium Ho Chi Minh City Open Univ Fac Civil Engn Ho Chi Minh City Vietnam Sardar Vallabhbhai Natl Inst Technol Dept Mech Engn Surat Gujarat India Torrens Univ Australia Ctr Artificial Intelligence Res & Optimisat Brisbane Qld 4006 Australia Yonsei Univ Yonsei Frontier Lab Seoul South Korea Ton Duc Thang Univ Div Computat Mech Ho Chi Minh City Vietnam Ton Duc Thang Univ Fac Civil Engn Ho Chi Minh City Vietnam

In this paper, damage detection, localization and quantification are performed using modal strain energy change ratio (MSEcr) as damage indicator combined with a new optimization technique, namely slime mould algorithm (SMA) developed in 2020. The SMA algorithm is employed to assess structural damage and monitor structural health. Two structures, including a laboratory beam and a bar planar truss are considered to study the effectiveness of the proposed approach. Another recent algorithm called marine predators algorithm (MPA) is also used for comparison purposes with SMA. The MSEcr is utilized in the first stage to predict the location of the damaged elements. Single and multiple damages cases are analysed based on different number of modes to study the sensitivity of the proposed indicator to the total number of modes considered in the analysis. Next, this indicator is used as an objective function in a second stage to solve the inverse problem using SMA and MPA for damage quantification of the elements identified in the first stage. Experimental validation is conducted using a 3D frame structure with four stories that have damaged components. It is demonstrated that the proposed approach, using MSEcr and SMA, provides superior results for the considered structures. The effectiveness of this technique is tested by introducing a white Gaussian noise with different levels, namely 2% and 4%. The results show that the provided approach can predict the location and level of damage with high accuracy after introducing the noise.

关键词： Modal strain energy Damage indicator Slime mould algorithm marine predators algorithm Inverse problem Modal analysis Damage assessment

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