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Hybrid salp swarm maximum power point tracking algorithm for photovoltaic systems in highly fluctuating environmental conditions

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SCIENTIFIC REPORTS 2025年第1期15卷 1-24页

作者： Jamaludin, Mohd Nasrul Izzani Tajuddin, Mohammad Faridun Naim Younis, Tarek Thanikanti, Sudhakar Babu Khishe, Mohammad Univ Malaysia Perlis Fac Elect Engn & Technol Arau 02600 Malaysia Aswan Univ Dept Elect Engn Aswan 81528 Egypt Chaitanya Bharathi Inst Technol Dept Elect & Elect Engn Hyderabad 500075 India Imam Khomeini Naval Sci Univ Nowshahr Dept Elect Engn Nowshahr Iran Yuan Ze Univ Innovat Ctr Artificial Intelligence Applicat Taoyuan City Taiwan Appl Sci Private Univ Appl Sci Res Ctr Amman 11937 Jordan Jadara Univ Res Ctr Irbid Jordan

The maximum power delivered by a photovoltaic system is greatly influenced by atmospheric conditions such as irradiation and temperature and by surrounding objects like trees, raindrops, tall buildings, animal droppings, and clouds. The partial shading caused by these surrounding objects and the rapidly changing atmospheric parameters make maximum power point tracking (MPPT) challenging. This paper proposes a hybrid MPPT algorithm that combines the benefits of the salp swarm algorithm (SSA) and hill climbing (HC) techniques. As long as the rate of change of irradiance does not exceed a specific limit, the HC mode is applied to track the global maximum power point (GMPP). Once a high rate of change in irradiation is detected, the SSA mode is activated. Moreover, the proposed algorithm employs the concept of boundary conditions to handle fast and slow fluctuating irradiance patterns. A comprehensive comparative evaluation of the proposed hybrid SSA-HC with state-of-the-art MPPT algorithms has been undertaken. Four distinct cases have been examined, including irradiance conditions with varying rates of change and partial shading conditions. The proposed hybrid SSA-HC algorithm has been validated and tested using a developed hardware setup, simulated in MATLAB for solar photovoltaic (PV) systems, and compared with standard SSA and HC. The performance of the tracking capability of this proposed hybrid technique at both steady-state and dynamic conditions under rapid and gradual irradiance changes demonstrates its superiority over recent state-of-the-art algorithms.

关键词： Maximum power point tracking optimization algorithms Fluctuating environments Partial shading Solar PV system

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A Novel Community Detection Algorithm Based on Node Propagation and Attraction

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IEEE Access 2025年 13卷 69161-69174页

作者： Fu, Lidong Xie, Xinxing Dou, Zengfa Bai, Yun Zhao, Xi Xi'an University of Science and Technology College of Computer Science and Technology Xi'an710600 China Qinghai Institute of Technology School of Computer and Information Science Xining810016 China Xi'an University School of Information Engineering Xi'an710065 China Xi'an Big Data Service Center Xi'an710007 China

High-quality community detection in complex networks typically relies on the similarity between nodes to select communities. However, when a node exhibits the same similarity with different communities, it becomes ambiguous to determining the optimal community assignment for that node, potentially leading to incorrect predictions and affecting the accuracy of community assignments for other nodes. To address this problem, this paper proposes a novel community detection algorithm based on node propagation and attraction. The algorithm first introduces a node propagation strategy, which identifies high-propagation nodes based on their connectivity and core attributes. These high-propagation nodes form the initial backbone and framework of communities, minimizing the interference of low-propagation nodes on community structure. Subsequently, an attraction definition is introduced, which starts from high-propagation nodes to select partitioning methods based on their relationships with neighboring nodes, using the attraction among these nodes facilitate community organization and propagation, aiming to enhance community quality. Finally, in the community optimization phase, the algorithm effectively improves the accuracy of partitions that result from similar attractions by considering the association degree between nodes and communities. Experimental results demonstrate that, on both synthetic and real networks, the proposed algorithm outperforms various existing community detection algorithms. © 2013 IEEE.

关键词： optimization algorithms

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Improving power system performance by integrating TCSC and SVC devices in the presence of stochastic renewable energy generators

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WIND ENGINEERING 2025年

作者： Benhabsa, Imadeddine Kouadri, Ramzi Slimani, Linda Mouassa, Souhil Univ Set 1 Elect Engn Dept Bez Campus Setif 19137 Algeria Univ Bouira Elect Engn Dept Bouira Algeria

This paper investigates the application of advanced metaheuristic algorithms Blood-Sucking Leech Optimizer (BSLO), Bonobo Optimizer (BO), and Electric Eel Foraging optimization (EEFO) to solve the optimal power flow (OPF) problem with stochastic renewable energy generators (REGs), specifically photovoltaic power generators (PVGs) and wind power generators (WGs). Two scenarios are examined: Scenario 1 evaluates the proposed algorithms performance without Flexible AC Transmission Systems (FACTS), focusing on minimizing Total Generation Cost (TGC), Active Power Loss (APL), and a combined objective of TGC and Emissions (TGCE). The TGC including both thermal and REG costs, in which the cost related to stochastic PV and wind power generation encompasses direct, reserve, and penalty costs due to overestimation and underestimation of available PV and wind power. Scenario 2 introduces the Thyristor-Controlled Series Capacitor (TCSC) and the Static Var Compensator (SVC) to evaluate their impact on three objective functions. The performance of the algorithms is evaluated on a modified IEEE 30-bus system. The results show that the BSLO algorithm consistently achieves the best TGC, APL, and TGCE values at 781.1209 $/h, 1.9960 MW, and 810.7376 $/h, respectively. These outcomes highlight its effectiveness and competitive performance in the first scenario. The integration of FACTS devices in the second scenario results in a 6.73% reduction in APL with the insertion of TCSC, a 1.86% reduction with the insertion of SVC, and a 6.10% reduction with the insertion of both TCSC and SVC, compared to the APL value in the case without FACTS devices (1.9960 MW). The study comprehensively analyzes how different optimization techniques and FACTS devices enhance power system performance with stochastic REG integration.

关键词： Optimal power flow stochastic PV and wind power generation thyristor-controlled series capacitor the static var compensator blood-sucking leech optimizer optimization algorithms

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Coyote and Badger optimization (CBO): A natural inspired meta-heuristic algorithm based on cooperative hunting

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COMMUNICATIONS IN NONLINEAR SCIENCE AND NUMERICAL SIMULATION 2025年 140卷

作者： Khatab, Mahmoud El-Gamel, Mohamed Saleh, Ahmed I. El-Shenawy, Atallah Rabie, Asmaa H. Mansoura Univ Fac Engn Math & Engn Phys Dept Mansoura Egypt Mansoura Univ Fac Engn Comp Engn & Syst Dept Mansoura Egypt

optimization techniques play a pivotal role in refining problem-solving methods across various domains. These methods have demonstrated their efficacy in addressing real-world complexities. Continuous efforts are made to create and enhance techniques in the realm of research. This paper introduces a novel technique that distinguishes itself through its clarity, logical mathematical structure, and robust mathematical equations, particularly in the second phase. This study sents the development of a new metaheuristic algorithm named Coyote and Badger optimization (CBO). CBO draws inspiration from the cooperative behaviors observed in honey badgers coyotes, with a specific focus on their intriguing communication process. Utilizing the inherent traits of these animals, the proposed CBO algorithm offers an intuitive and effective solution addressing engineering optimization challenges by providing the best fitness values. To validate CBO's effectiveness in real-time applications, complex engineering problems called pressure vessel design, feature selection in medical system, and tension-compression spring design are used as case studies for testing the proposed CBO compared to other recent algorithms. Additionally, ten benchmark functions and also statistical analysis methods (mean, standard deviation, confidence intervals, t-test, and Wilcoxon test) are used. Experimental results demonstrate that CBO algorithm surpasses eleven recent algorithms when subjected to common ten benchmark functions. Additionally, CBO outperforms other recent eleven algorithms according to three different case studies. According to the ten benchmark functions (F1 to F10), CBO provides minimum fitness values which are closed to the exact (standard) values;0, 0, 0.003, 0.0002,-1.0316, 3.0058, 0.398, 0.02, 0.00076, and 0.000725 respectively. Related to statistical analysis, CBO provides the best mean, standard deviation, confidence intervals, t-test, and Wilcoxon values. According to case studies

关键词： Coyote Badger optimization algorithms Pressure vessel design Feature selection Spring design

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A hybrid learning approach for simulating settlement of shallow foundation

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MULTISCALE AND MULTIDISCIPLINARY MODELING EXPERIMENTS AND DESIGN 2025年第1期8卷 1-15页

作者： Li, Jiaman Wu, Jundong Hu, Wei Shaanxi Coll Commun Technol Xian 710000 Shaanxi Peoples R China China Airport Planning & Design Inst Co Ltd Northwest Branch Xian 710000 Shaanxi Peoples R China Shaanxi Prov Transport Planning Design & Res Inst Xian 710065 Shaanxi Peoples R China

Settlement modeling is essential because of the cohesive soil texture's intricacy. This study seeks to identify settlement (Sm) of shallow foundations using newly discovered ML approaches named the hybridized random forests analysis (RF) with grasshoppers optimization algorithm (GOA), the bat-inspired approach (BAT), beluga whale optimization (BWH). RF serves as the primary predictive model due to its robust ensemble learning approach. optimization algorithms enhance its predictive accuracy. Hybrid models (GOARF, BATRF, BWHRF) leverage the strengths of both RF and optimization algorithms to provide superior settlement predictions. By combining RF with these optimization techniques, the study aims to achieve highly accurate predictions of settlement for shallow foundations, demonstrating the effectiveness of these advanced machine-learning approaches. All the BATRF, GOARF, and BWHRF models accurately emulated the Sm, with R2 values of at least 0.985 for the training and 0.978 for the testing collection, respectively. Comparing the BWHRF to other models and literature, it is believed to be the appropriate system with the highest categorization. R2, RMSE, and MAE values during learning are 0.9913, 2.341, and 1.239 mm, respectively, which are superior by 0.9025, 8.09, and 4.92 mm over ANFIS-PSO. The values of the A_(15-index) index depict that the BWHRF can be outperformed by other models. Finally, after examining the validity and considering the assumptions, it is clear that the RF paired with BWH can perform more effectively than the BAT and GOA, and even ANFIS-PSO (literature), in the Sm simulation.

关键词： Settlement Shallow foundation Estimation Random forests optimization algorithms

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A Non-Parametric Translational Motion Compensation Algorithm for ISAR Imaging by using the Alternating Iteration and LBFGS Algorithm

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IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 2025年 18卷 11623-11633页

作者： Gao, Yuexin Xue, Min Yu, Hanwen Chen, Jianlai Xidian University School of Aerospace Science and Technology Xi'an710071 China Chang'an University School of Data Science and Artificial Intelligence Xi'an710064 China University of Electronic Science and Technology of China School of Resources and Environment Chengdu611731 China Central South University School of Automation Changsha410083 China

A translational motion compensation method for a target with non-parametric translation in dechirping system is proposed in this paper. We establish the non-parametric translational motion compensation scheme based on the optimization of an image's sharpness. Since the impacts of the range shifts and phase errors on an image's quality are different, we convert the optimization into an alternating iteration of estimating two vectors. These two kinds of iterations are searching for range shifts and phase errors for each range profile, respectively. The Limited-Memory BFGS algorithm is used to solve the optimization problems with high efficiency. According to the application of the proposed algorithm to the real data, it is valid and could be faster and be of higher performance in comparison with some existing methods. © 2008-2012 IEEE.

关键词： optimization algorithms

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A smart multiplexed microRNA biosensor based on FRET for the prediction of mechanical damage and storage period of strawberry fruits

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PLANT MOLECULAR BIOLOGY 2025年第2期115卷 1-10页

作者： Vakilian, Keyvan Asefpour Gorgan Univ Agr Sci & Nat Resources Dept Biosyst Engn Gorgan Iran

Today, measuring the concentration of various microRNAs in fruits has been introduced to model the storage conditions of agricultural products. However, there is a limiting factor in the extensive utilization of such techniques: the existing methods for measuring microRNA sequences, including PCR and microarrays, are time-consuming and expensive and do not allow for simultaneous measurement of several microRNAs. In this study, a biosensor based on the F & ouml;rster resonance energy transfer (FRET) of fluorescence dyes that can lead to the hybridization of oligonucleotide probes labeled with such dyes by using an excitation wavelength has been used to simultaneously measure microRNAs. Three microRNA compounds, i.e., miRNA-164, miRNA-167, and miRNA-399a, which play significant roles in the postharvest characteristics of strawberry fruits were measured. The simultaneous measurement was performed using three fluorescence dyes which exert various emission wavelengths at 570, 596, and 670 nm. In the following, machine learning methods including artificial neural networks (ANNs) and support vector machines (SVMs), with hyperparameter values optimized with the help of metaheuristic optimization algorithms, were used to predict the amount of mechanical loading on strawberry fruits and their storage period having the microRNA concentrations. The results showed that the SVM with Gaussian kernel, which was optimized by the Harris hawks optimization, is capable of predicting the mechanical stress and storage period of strawberry fruits with a coefficient of determination (R2) of 0.89 and 0.92, respectively. The findings of this study reveal the application of combining FRET-based biosensors and machine learning methods in fruit storage quality assessment.

关键词： Support vector machine optimization algorithms microRNA biosensor FRET

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Text clustering with a hybrid multi-objective optimization approach: The multi-objective firefly differential Jaya Algorithm

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SWARM AND EVOLUTIONARY COMPUTATION 2025年 93卷

作者： Naderi, Muhammad Amiri, Maryam Arak Univ Fac Engn Dept Comp Engn Arak *** Iran

The exponential growth of unstructured text data generated by internet users has created an urgent need for efficient organization methods to uncover valuable insights. Text clustering, a widely used data mining approach, often relies on single-objective optimization, which can struggle to deliver optimal results for datasets with diverse clustering criteria. To address these challenges, we propose the Multi-objective Firefly Differential Jaya (MFDJ) algorithm, a novel nature-inspired optimization method designed to enhance text clustering. MFDJ integrates the strengths of NSGA-II, a well-established multi-objective optimization framework, with three complementary algorithms: the Firefly algorithm for swarm intelligence-based optimization, Differential Evolution for robust exploration through mutation, and the Jaya algorithm for parameter-free improvement leveraging both the best and worst solutions. This synergy significantly enhances the algorithm's ability to balance exploration and exploitation, yielding superior clustering performance. We evaluated MFDJ on eight benchmark text datasets, where it demonstrated consistent superiority over state-of-the-art methods, including NSGA-II and MOMDE. On average, MFDJ achieved a 67.89% improvement in F-measure over NSGA-II and a 5.87% improvement over MOMDE, while also exhibiting better convergence properties for the majority of datasets. These results underscore the capability of MFDJ to generate high-quality clusters, making it a versatile tool for tackling complex text clustering and broader optimization challenges.

关键词： Evolutionary computation Text clustering Multi-objective optimization optimization algorithms

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Optimising the parameters in AWJT process using a modified multi-objective DE algorithm

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International Journal of Machining and Machinability of Materials 2025年第1期27卷 19-39页

作者： Gawai, I.R. Lalwani, D.I. Department of Mechanical Engineering Sardar Vallabhbhai National Institute of Technology Ichchhanath Circle Athawa Lines Gujarat Surat395007 India

This paper presents a novel multi-objective optimisation algorithm for predicting the optimal control parameters of a radial abrasive water jet turning (AWJT) method. The objective is to maximise the material removal rate (MRR) and minimise the surface roughness (Ra) of the turned surface. The control parameters include water pressure, jet feed speed, abrasive flow rate, surface speed, and nozzle tilted angle. The proposed algorithm, called multi-objective amended differential evolution algorithm (MADEA), is a rank-based differential evolution (DE) algorithm that uses non-dominated sorting and crowding distance to select and update the solutions. The performance of MADEA is compared with six state-of-the-art multi-objective evolutionary algorithms on a set of benchmark test problems and the AWJT problem. The results show that MADEA can find better Pareto optimal solutions than the other algorithms. Copyright © 2025 Inderscience Enterprises Ltd.

关键词： optimization algorithms

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Symmetry-informed transferability of optimal parameters in the quantum approximate optimization algorithm

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Physical Review A 2025年第2期111卷 022418-022418页

作者： Isak Lyngfelt Laura García-Álvarez Department of Microtechnology and Nanoscience (MC2) Chalmers University of Technology SE-412 96 Göteborg Sweden

One of the main limitations of variational quantum algorithms is the classical optimization of the highly dimensional nonconvex variational parameter landscape. To simplify this optimization, we can reduce the search space using problem symmetries and typical optimal parameters as initial points if they concentrate. In this article, we consider typical values of optimal parameters of the quantum approximate optimization algorithm for the MAXCUT problem with d-regular tree subgraphs and reuse them in different graph instances. We prove symmetries in the optimization landscape of several kinds of weighted and unweighted graphs, which explains the existence of multiple sets of optimal parameters. However, we observe that not all optimal sets can be successfully transferred between problem instances. We find specific transferable domains in the search space and show how to translate an arbitrary set of optimal parameters into the adequate domain using the studied symmetries. Finally, we extend these results to general classical optimization problems described by Ising Hamiltonians, the Hamiltonian variational ansatz for relevant physical models, and the recursive and multiangle quantum approximate optimization algorithms.

关键词： optimization algorithms

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