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Solar photovoltaic Maximum Power Point Tracking controller optimization using Grey Wolf Optimizer: A performance comparison between bio-inspired and traditional algorithms

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EXPERT SYSTEMS WITH APPLICATIONS 2023年 211卷

作者： Aguila-Leon, Jesus Vargas-Salgado, Carlos Chinas-Palaciosa, Cristian Diaz-Bello, Dacil Univ Politecn Valencia Inst Univ Invest Ingn Energet Valencia Spain Univ Guadalajara Dept Estudios Agua & Energia Guadalajara Mexico Univ Politecn Valencia Dept Ingn Electr Valencia Spain

Solar photovoltaic systems are widely used;however, their performance is bound to weather conditions, depending on irradiation, temperature, and the effect of shadows. Maximum Power Point Tracking techniques have been developed to solve this issue. Standard methods use mainly-two algorithms: Perturb and Observe and Incremental Conductance. However, such algorithms perform differently when the Solar photovoltaic system works under sudden solar irradiation changes, temperature, and load changes. This research proposes an opti-mized Maximum Power Point Tracking controller based on the Grey Wolf Optimization algorithm using the MATLAB/Simulink software as an alternative to the traditional techniques. Global efficiency and Root Mean Square Error evaluate the controller's performance. The response time is analyzed using the Grey Wolf Optimizer algorithm, Wolf Optimizer Algorithm, Simulated Annealing, and Particle Swarm Optimization. These four metaheuristic algorithms are compared to the Perturb and Observe, and Incremental Conductance algorithms. The models are analyzed for the transient state and full-day operation scenarios for constant and variable ir-radiations, temperatures, and loads. The comparative results show that the Maximum Power Point Tracking controller optimized by the Grey Wolf Optimizer algorithm has superior performance, giving an average 6% output power higher than the other controllers under the test scenarios evaluated. The efficiency of the proposed model was, on average, 3% higher than the Incremental Conductance and Perturb & Observe controllers. For the MPPT controller tunning stage, the Grey Wolf Optimizer Algorithm had the best performance with an RMSE of 255.3549 with a compute time of 27.3 min;the worst performing was the Particle Swarm Optimization with an RMSE of 332.4075 and 27.8 min computation time. The proposed GWO optimized MPPT controller had the faster settling time for each irradiation level compared, with an average of 0.175 s. Als

关键词： Optimization metaheuristic algorithms Grey Wolf Optimization Microgrid Photovoltaic Maximum Power Point Tracking Bio-inspired algorithm

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BIM-Based Resource Tradeoff in Project Scheduling Using Fire Hawk Optimizer (FHO)

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BUILDINGS 2022年第9期12卷

作者： Shishehgarkhaneh, Milad Baghalzadeh Azizi, Mandi Bashi, Mahla Moehler, Robert C. Islamic Azad Univ Tabriz Dept Construct Management Tabriz *** Iran Univ Tabriz Dept Civil Engn Tabriz *** Iran Monash Univ Fac Engn Dept Civil Engn 23 Coll WalkClayton Campus Melbourne Vic 3800 Australia

Project managers should balance a variety of resource elements in building projects while taking into account many major concerns, including time, cost, quality, risk, and the environment. This study presents a framework for resource trade-offs in project scheduling based on the Building Information Modeling (BIM) methodology and metaheuristic algorithms. First, a new metaheuristic algorithm called Fire Hawk Optimizer (FHO) is used. Using project management software and the BIM process, a 3D model of the construction is created. In order to maximize quality while minimizing time, cost, risk, and CO2 in the project under consideration, an optimization problem is created, and the FHO's capability for solving it is assessed. The results show that the FHO algorithm is capable of producing competitive and exceptional outcomes when it comes to the trade-off of various resource options in projects.

关键词： fire hawk optimizer optimization metaheuristic algorithms building information modelling (BIM) resource management project resource management

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Feature selection optimization with filtering and wrapper methods: two disease classification cases

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TURKISH JOURNAL OF ELECTRICAL ENGINEERING AND COMPUTER SCIENCES 2023年第7期31卷 1329-1342页

作者： Atik, Serhat Dalyan, Tugba Istanbul Bilgi Univ Fac Engn & Nat Sci Dept Comp Engn Istanbul Turkiye

Discarding the less informative and redundant features helps to reduce the time required to train a learning algorithm and the amount of storage required, improving the learning accuracy as well as the quality of results. In this study, we present different feature selection approaches to address the problem of disease classification based on the Parkinson and Cardiac Arrhythmia datasets. For this purpose, first we utilize three filtering algorithms including the Pearson correlation coefficient, Spearman correlation coefficient, and relief. Second, metaheuristic algorithms are compared to find the most informative subset of the features to obtain better classification accuracy. As a final method, a hybrid model involving filtering algorithms is applied to the datasets to eliminate half of the features, and then a metaheuristic algorithm based on a proposed genetic algorithm is applied to the rest of the datasets. With all three methods, we use three classification algorithms: support vector machine, K-nearest neighbor, and random forest. The results show that the best scores are obtained from the metaheuristic algorithm based on the proposed genetic algorithm for both datasets. This comparative study contributes to the literature by increasing the accuracy of classification for both datasets and presenting a hybrid model with filtering and a metaheuristic algorithm.

关键词： Feature selection optimization algorithms metaheuristic algorithms genetic algorithms filtering methods

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Parameters identification of photovoltaic models using Lambert W-function and Newton-Raphson method collaborated with AI-based optimization techniques: A comparative study

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EXPERT SYSTEMS WITH APPLICATIONS 2024年 255卷

作者： Abdel-Basset, Mohamed Mohamed, Reda Hezam, Ibrahim M. Sallam, Karam M. Hameed, Ibrahim A. Zagazig Univ Zagazig 44519 Sharqiyah Egypt King Saud Univ Coll Sci Stat & Operat Res Dept Riyadh 11451 Saudi Arabia Univ Sharjah Dept Comp Sci Sharjah U Arab Emirates Norwegian Univ Sci & Technol NTNU Dept ICT & Nat Sci Alesund Norway

Accurately estimating the unknown parameters of the photovoltaic (PV) models based on the measured voltagecurrent data is a challenging optimization problem due to its high nonlinearity and multimodality. An accurate solution to this problem is essential for efficiently simulating, controlling, and evaluating PV systems. There are three different PV models, including the single-diode model, the double-diode model, and the triple-diode model, with five, seven, and nine unknown parameters, respectively, proposed to represent the electrical characteristics of PV systems with varying levels of complexity and accuracy. In the literature, several deterministic and metaheuristic algorithms have been used to accurately solve this hard problem. However, due to the high nonlinearity of this problem, the deterministic methods could not achieve accurate solutions. On the other side, the metaheuristic algorithms, also known as gradient-free methods, could achieve somewhat good solutions for this problem, but they still need further improvements to strengthen their performance against stuck-in local optima and slow convergence speed problems. Over the last two years, several recent metaheuristic algorithms with better characteristics to improve convergence speed and avoid local optima have been proposed to tackle continuous optimization problems. However, the performance of the majority of those algorithms for estimating the parameters of PV models has not been investigated. Therefore, in this paper, the performance of nineteen recently published metaheuristic algorithms, such as the Mantis search algorithm (MSA), spider wasp optimizer (SWO), light spectrum optimizer (LSO), growth optimizer (GO), walrus optimization algorithm (WAOA), hippopotamus optimization algorithm (HOA), black-winged kite algorithm (BKA), quadratic interpolation optimization (QIO), sinh cosh optimizer (SCHA), exponential distribution optimizer (EDO), optical microscope algorithm (OMA), secretary bird optimiz

关键词： metaheuristic algorithms Newton-Raphson method Lambert W function Photovoltaic models Solar energy

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NoiseAugmentNet-HHO: Enhancing Histopathological Image Classification Through Noise Augmentation

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IEEE ACCESS 2024年 12卷 194203-194227页

作者： Jena, Prem Purusottam Mishra, Debahuti Das, Kaberi Mishra, Sashikala Siksha O Anusandhan Deemed Univ Dept Comp Sci & Engn Bhubaneswar 751030 India Siksha O Anusandhan Deemed Univ Dept Comp Applicat Bhubaneswar 751030 India Symbiosis Int Deemed Univ Symbiosis Inst Technol Pune 412115 Maharashtra India

Histopathological image analysis is critical in contemporary medical diagnostics, enabling precise examination of tissue samples at microscopic levels. Despite advances in digital pathology and imaging technologies, developing robust deep learning classifiers for histopathological images remains challenging due to inherent variability and complexity. This study investigates the efficacy of noise augmentation and denoising techniques to enhance classifier performance. We systematically evaluate various noise augmentation methods across datasets including BACH, Camelyon17, and BreakHis, aiming to improve robustness under diverse noise conditions such as Salt and Pepper, Speckle, and Poisson. Key innovations include NoiseAugmentNet-HHO, which integrates Harris Hawks optimization (HHO) with VGG16, ResNet50, and deep CNN (DCNN). This model is designed to effectively manage noise types and achieve superior denoising performance, with metrics such as PSNR, entropy, SSIM, MSE, and MAE being used for evaluation. Pixel-wise regression is emphasized for accurate tissue structure assessment and anomaly detection. NoiseAugmentNet-HHO demonstrates significant improvements in classifier performance compared to traditional methods. It achieves high accuracy rates of 94.22% to 98.26% across the BACH, Camelyon17, and BreakHis datasets. Superior sensitivity (up to 98.36%) and specificity (up to 98.10%) further highlight its robust capability in accurately identifying image features. The encoder-decoder architecture of NoiseAugmentNet-HHO ensures robust feature extraction and reconstruction, preserving spatial information essential for accurate classification. Statistical analyses confirm its superiority in maintaining image fidelity and classification accuracy. NoiseAugmentNet-HHO outperforms traditional denoising methods across Salt and Pepper, Speckle, and Poisson noise types, effectively enhancing diagnostic precision. It demonstrates high effectiveness in noise reduction while pre

关键词： Noise Noise reduction Feature extraction Accuracy Imaging Biomedical imaging Deep learning Medical diagnostic imaging Residual neural networks Robustness metaheuristic algorithms medical image analysis noise augmentation

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Development and application of Quantum Entanglement inspired Particle Swarm Optimization

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KNOWLEDGE-BASED SYSTEMS 2021年 219卷

作者： Vaze, Rujuta Deshmukh, Nagraj Kumar, Rajesh Saxena, Akash Malaviya Natl Inst Technol Jaipur Dept Elect Engn Jaipur Rajasthan India Swami Keshvanand Inst Technol Management & Gramot Dept Elect Engn Jaipur Rajasthan India

Particle Swarm Optimization has been extensively researched and applied to tackle optimization problems due to the ease in implementation and less number of parameters to be tuned. But particle swarm optimization (PSO) algorithm gets trapped into local optimum in high-dimensional space and it is inefficient in solving optimization problems which show high dependency. To overcome the above problems without compromising the advantages of PSO, this paper proposes Quantum Entanglement inspired Particle Swarm Optimization (QEPSO). QEPSO incorporates entangled states in its Q-bits to efficiently solve high-dependency problems and uses quantum local search to accelerate the optimization process. The proposed algorithm is tested on several standard benchmark functions and is also further benchmarked on IEEE Congress of Evolutionary computing (CEC 2017) benchmark set. The performance of QEPSO is compared with existing variants of PSO and some other popular algorithms. The results show that QEPSO outperforms other algorithms and is especially useful in high dimensional problems. Finally it is used for a real-life application of Multi-level Image Segmentation where eight gray-scale standard test images were used. The performance of QEPSO was superior to the other algorithms as it gave better results with high stability and quick convergence. (C) 2021 Elsevier B.V. All rights reserved.

关键词： metaheuristic algorithms Particle Swarm Optimization Quantum Entanglement High-dependency problems

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A state-of-the-art differential evolution algorithm for parameter estimation of solar photovoltaic models

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ENERGY CONVERSION AND MANAGEMENT 2021年 230卷

作者： Gao, Shangce Wang, Kaiyu Tao, Sichen Jin, Ting Dai, Hongwei Cheng, Jiujun Univ Toyama Fac Engn Toyama 9308555 Japan Nanjing Forestry Univ Sch Sci Nanjing Peoples R China Jiangsu Ocean Univ Sch Comp Engn Lianyugang 222005 Peoples R China Tongji Univ Key Lab Embedded Syst & Serv Comp Minist Educ Shanghai 200092 Peoples R China

Photovoltaic (PV) generation systems are vital to the utilization of the sustainable and pollution-free solar energy. However, the parameter estimation of PV systems remains very challenging due to its inherent nonlinear, multi-variable, and multi-modal characteristics. In this paper, we propose a state-of-the-art optimization method, namely, directional permutation differential evolution algorithm (DPDE), to tackle the parameter estimation of several kinds of solar PV models. By fully utilizing the information arisen from the search population and the direction of differential vectors, DPDE can possess a strong global exploration ability of jumping out of the local optima. To verify the performance of DPDE, six groups of experiments based on single, double, triple diode models and PV module models are conducted. Extensive comparative results between DPDE and other fifteen representative algorithms show that DPDE outperforms its peers in terms of the solution accuracy. Additionally, statistical results based on Wilcoxon rank-sum and Friedman tests indicate that DPDE is the most robust and best performing algorithm for the parameter estimation of PV systems.

关键词： Photovoltaic cell Parameter estimation Optimization methods Differential evolution metaheuristic algorithms Computational intelligence

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CCSA: Conscious Neighborhood-based Crow Search Algorithm for Solving Global Optimization Problems

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APPLIED SOFT COMPUTING 2019年 85卷

作者： Zamani, Hoda Nadimi-Shahraki, Mohammad H. Gandomi, Amir H. Islamic Azad Univ Fac Comp Engn Najafabad Branch Najafabad Iran Islamic Azad Univ Big Data Res Ctr Najafabad Branch Najafabad Iran Univ Technol Sydney Fac Engn & Informat Technol Ultimo NSW 2007 Australia Stevens Inst Technol Sch Business Hoboken NJ 07030 USA

In this paper, a conscious neighborhood-based crow search algorithm (CCSA) is proposed for solving global optimization and engineering design problems. It is a successful improvement to tackle the imbalance search strategy and premature convergence problems of the crow search algorithm. CCSA introduces three new search strategies called neighborhood-based local search (NLS), non-neighborhood based global search (NGS) and wandering around based search (WAS) in order to improve the movement of crows in different search spaces. Moreover, a neighborhood concept is defined to select the movement strategy between NLS and NGS consciously, which enhances the balance between local and global search. The proposed CCSA is evaluated on several benchmark functions and four applied problems of engineering design. In all experiments, CCSA is compared by other state-of-the-art swarm intelligence algorithms: CSA, BA, CLPSO, GWO, EEGWO, WOA, KH, ABC, GABC, and Best-so-far ABC. The experimental and statistical results show that CCSA is very competitive especially for large-scale optimization problems, and it is significantly superior to the compared algorithms. Furthermore, the proposed algorithm also finds the best optimal solution for the applied problems of engineering design. (C) 2019 Elsevier B.V. All rights reserved.

关键词： Optimization metaheuristic algorithms Swarm intelligence algorithms Engineering problems Conscious neighborhood

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Machine learning-based multiscale framework for mechanical behavior of nano-crystalline structures

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INTERNATIONAL JOURNAL OF MECHANICAL SCIENCES 2024年 265卷

作者： Khoei, A. R. Seddighian, M. R. Sameti, A. Rezaei Sharif Univ Technol Ctr Excellence Struct & Earthquake Engn Dept Civil Engn POB 113659313 Tehran Iran Bu Ali Sina Univ Fac Engn Dept Civil Engn Hamadan Iran

In this paper, a computational atomistic-continuum multiscale framework is developed based on the machine learning (ML) architecture to capture the nonlinear behavior of nano-crystalline structures. The dataset of the ML process is derived from analyzing the atomistic representative volume element (RVE) through the molecular dynamics method under various deformation paths. A comprehensive investigation is performed on the influential parameters of the atomistic RVE that leads to the determination of an appropriate atomistic RVE for reproducing the mechanical characteristics from fine-scale to coarse-scale level. The multilayer perceptron neural network approach (MLP-ANN) is employed in the optimization algorithm of the ML process to train the atomistic dataset. The performance of the ML model is validated by predicting the response of atomistic RVE under various deformation gradients. Finally, the proposed computational algorithm is employed for the simulation of several mechanical benchmark problems, including the uniaxial tensile test, pure shear test, pure torsion test, and nano-indentation problem. Numerical simulations are validated by comparing the results of machine learning-based multiscale analysis with the fully atomistic model and experimental data. It is shown that the proposed ML-based method enhances the multiscale analysis to capture the nonlinear behavior of nanocrystalline structures without computational complexities associated with conventional multiscale methods. The results highlight that the proposed multiscale framework can be utilized as a constitutive model for the largescale analysis with a great accuracy and unique efficiency.

关键词： Multiscale analysis Atomistic-continuum model Molecular dynamics Machine learning metaheuristic algorithms

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A novel harmony search based optimization of reinforced concrete biaxially loaded columns

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STRUCTURAL ENGINEERING AND MECHANICS 2015年第6期54卷 1097-1109页

作者： Nigdeli, Sinan Melih Bekdas, Gebrail Kim, Sanghun Geem, Zong Woo Istanbul Univ Dept Civil Engn TR-34320 Istanbul Turkey Temple Univ Dept Civil & Environm Engn Philadelphia PA 19122 USA Gachon Univ Dept Energy IT Songnam 461701 South Korea

A novel optimization approach for reinforced concrete (RC) biaxially loaded columns is proposed. Since there are several design constraints and influences, a new computation methodology using iterative analyses for several stages is proposed. In the proposed methodology random iterations are combined with music inspired metaheuristic algorithm called harmony search by modifying the classical rules of the employed algorithm for the problem. Differently from previous approaches, a detailed and practical optimum reinforcement design is done in addition to optimization of dimensions. The main objective of the optimization is the total material cost and the optimization is important for RC members since steel and concrete are very different materials in cost and properties. The methodology was applied for 12 cases of flexural moment combinations. Also, the optimum results are found by using 3 different axial forces for all cases. According to the results, the proposed method is effective to find a detailed optimum result with different number of bars and various sizes which can be only found by 2000 trial of an engineer. Thus, the cost economy is provided by using optimum bars with different sizes.

关键词： reinforced concrete biaxially loaded columns optimization metaheuristic algorithms harmony search algorithm

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