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squirrel search algorithm based intelligent controller for interconnected power system

INTERNATIONAL JOURNAL OF MODELLING AND SIMULATION 2025年第2期45卷 420-440页

作者： Kalyan, CH. Naga Sai Goud, B. Srikanth Kumar, M. Kiran Thakur, Padmanabh Bajaj, Mohit Bansal, Ramesh C. Vasireddy Venkatadri Inst Technol Elect & Elect Engn Guntur Andhra Pradesh India Anurag Univ Dept Elect & Elect Engn Ghatkesar Telangana India Koneru Lakshmaiah Educ Fdn Dept Elect & Elect Engn Guntur India Graph Era Deemed be Univ Dept Elect Engn Dehra Dun Uttarakhand India Appl Sci Private Univ Appl Sci Res Ctr Amman Jordan Univ Sharjah Dept Elect Engn Sharjah U Arab Emirates Univ Pretoria Dept Elect Elect & Comp Engn Pretoria South Africa

In this paper, a novel controller, namely fractional order fuzzy proportional-integral-derivative (FOFPID) has been proposed for the dual area power system of hydro-thermal-gas units (DAHTG). The squirrel search algorithm (SSA) is implemented to achieve the optimal values of the controller tuning parameters. Further, various non-linearity constraints, such as governor dead band (GDB), generation rate constraint (GRC) and communication time delays (CTDs) have also been considered to test the effectiveness of the proposed controller in a real-time environment. The step load of 10% is provided as the disturbance under the regulation. In addition, the coordinated strategy of a static synchronous series compensator (SSSC) device and superconducting magnetic energy storage (SMES) is employed in the proposed system to enhance the system's performance. Further, the accuracy, effectiveness and robustness of the proposed controller are revealed through the comparative analysis with classical PID and traditional fuzzy PID controllers. Furthermore, presented controller efficacy is revealed with the other recently reported control techniques by implementing it on the two-area thermal system with GDB. The comparative analysis shows the better performance of the proposed controller than these two. Finally, the sensitivity analysis is performed to check the controller robustness.

关键词： Load-frequency control squirrel search algorithm FOFPID controller Coordinated SMES-SSSC strategy

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Enhancing wireless sensor network lifespan and efficiency through improved cluster head selection using improved squirrel search algorithm

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ARTIFICIAL INTELLIGENCE REVIEW 2025年第3期58卷 1-32页

作者： Alshammri, Ghalib H. King Saud Univ Community Coll Dept Comp Sci Riyadh 11437 Saudi Arabia

A Wireless Sensor Network (WSN) is a significant technological advancement that might contribute to the industrial revolution. The sensor nodes that are part of WSNs are battery-powered. Energy is the most crucial resource for WSNs since batteries cannot be changed or refilled. Since WSNs are a finite resource, several techniques have been devised and used throughout time to preserve them. To extend the lifespan of WSNs, this study will provide an effective method for Cluster Head (CH) selections. Many researches are employing the Swarm-based optimization algorithm to Select the optimal CH. In this study, the squirrel search algorithm (SSA) is utilized to select the optimal CH Selection in WSN. The general SSA has been modified in this study to address the exact need for CH choice in WSNs. The Improved squirrel search algorithm (I-SSA) integrates a series of enhancements aimed at accelerating convergence and elevating solution quality. Notably, we've implemented Adaptive Population Initialization, Dynamic Step Size Control, and a Local search algorithm to augment the exploration and exploitation capabilities of the SSA. These enhancements collectively refine the algorithm's ability to navigate the search space effectively, resulting in more efficient convergence towards optimal solutions. The suggested formulation's goal function takes into account the CH balance average, factor, sink distance residual energy and intra-cluster distance. The simulations are run under a variety of circumstances. The MATLAB 2021a working setting is utilised for simulation. The proposed code of conduct SSA-C is compared with the existing protocols Grey Wolf Optimization (GWO), SSA, Chernobyl Disaster Optimizer (CDO), Sperm Swarm Optimization (SSO), A Metaheuristic Optimized Cluster head selection-based Routing algorithm for WSNs (MOCRAW), Energy-Efficient Weighted Clustering (EEWC), and Multi-agent pathfinding using Ant Colony Optimization (MAP-ACO). The ISSA-C method achieved a Packet

关键词： Routing protocol Wireless Sensor Networks squirrel search algorithm Clustering

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Voice feature-based diagnosis of Parkinson’s disease using nature inspired squirrel search algorithm with ensemble learning classifiers

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Iran Journal of Computer Science 2025年 1-25页

作者： Shibina, V. Thasleema, T.M. Department of Computer Science Central University of Kerala Kasargod India

Parkinson’s disease is a neurological condition primarily affecting individuals aged 55–65, leading to impairments in movement and speech due to the degeneration of specific brain regions. Recent advancements in machine learning have demonstrated the potential of using acoustic signals for the early detection of Parkinson’s illness. This study proposes a robust classification framework for PD diagnosis by leveraging machine learning techniques with advanced feature selection methods. The proposed approach employs the squirrel search algorithm (SSA) for feature selection, combined with principal component analysis (PCA) and ensemble learning, to enhance the efficiency of conventional machine learning algorithms. To ensure robustness and reliability, hyperparameter tuning and tenfold cross-validation were applied during the evaluation process. The proposed framework utilizes different classification models, including decision trees (DTs), support vector machines (SVM) with linear and radial basis function (RBF) kernels, and random forest (RF). In addition, ensemble classifiers such as gradient boost, AdaBoost, stacking, extra tree, light gradient boosting machine (LGBM), CatBoost, and voting classifiers were implemented and compared. The experiments were conducted using a data set from the University of California, Irvine (UCI) repository, comprising 64 healthy controls and 188 PD patients. Among the evaluated methods, the extra tree classifier combined with PCA and SSA achieved the highest performance, with an accuracy of 97.19%, precision of 97.32%, recall of 97.19%, F1-score of 97.17%, and an AUC score of 0.98. This work highlights the effectiveness of the proposed framework in accurately diagnosing Parkinson’s disease, offering a promising tool for early detection and intervention. © The Author(s), under exclusive licence to Springer Nature Switzerland AG 2025.

关键词： Machine learning classifiers Parkinson’s disease Principal component analysis squirrel search algorithm Variance threshold

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Data aggregation by enhanced squirrel search optimization algorithm for in wireless sensor networks

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WIRELESS NETWORKS 2025年第3期31卷 2181-2201页

作者： Kathiroli, Panimalar Kanmani, S. SRM Inst Sci & Technol Fac Engn & Technol Dept Data Sci & Business Syst Kattankulathur 603203 Tamilnadu India Puducherry Technol Univ Dept Informat Technol Pondicherry India

Wireless Sensor Network's are inherently power-constrained, with data transmission being a major source of energy depletion. Efficient data aggregation is therefore essential to minimize energy consumption and extend the network's operational lifetime. This paper introduces a novel hybrid meta-heuristic optimization algorithm that integrates the squirrel search algorithm (SSA) with the monarch butterfly optimization algorithm (MBOA) to optimize the clustering process and the selection of aggregation nodes. The hybrid algorithm leverages SSA's strengths in local search and MBOA's robust global exploration capabilities to overcome the limitations of traditional methods, such as premature convergence to local optima. By dynamically balancing exploitation and exploration, the proposed model ensures more effective cluster head selection, significantly reduces communication overhead, and enhances overall network stability. Simulation results demonstrate that the hybrid algorithm outperforms existing state of the art models in performance metrics including energy efficiency, aggregation delay, and network lifetime. The algorithm's adaptability to varying network conditions, coupled with its ability to maintain population diversity, positions it as a highly effective solution for improving the performance and reliability of WSNs.

关键词： Data aggregation squirrel search algorithm Monarch butterfly algorithm Wireless sensor network

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PARKINSON'S DISEASE CLASSIFICATION USING HYBRID NAMIB squirrel search WATER algorithm-BASED DEEP LEARNING APPROACH

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INTERNATIONAL JOURNAL FOR MULTISCALE COMPUTATIONAL ENGINEERING 2025年第3期23卷 37-61页

作者： Sharanyaa, S. Mathew, Jimsha K. Nair, Kavitha R. Arumugasamy, Muthulakshmi Panimalar Engn Coll Dept Informat Technol Chennai 600056 Tamil Nadu India New Horizon Coll Engn Dept Artificial Intelligence & Machine Learning Bengaluru 560103 Karnataka India Acharya Inst Technol Dept Artificial Intelligence & Machine Learning Bengaluru 560107 Karnataka India Sathyabama Inst Sci & Technol Dept Comp Sci & Engn Chennai 600119 Tamil Nadu India

*Address all correspondence to: Department of Artificial Intelligence and Machine Learning, New Horizon Parkinson's disease (PD) is a neurological disease throughout the globe, and it is the second leading reason for death and impairment. The overall cases of PD have nearly doubled in the past 15 years. It has been defined by the absence of dopamine cells in the brain. PD affects about 1% of individuals over the age of 65, while 90% of them are affected by speech disorders like articulation, phonation, fluency, and prosody. Hence, the earlier prediction is significant in providing a good treatment for PD. In this research, the Namib squirrel search water algorithm (NSSWA) is proposed for PD classification. The voice sample is used as input and it is preprocessed using a Gaussian filter. Furthermore, feature extraction is applicable for the extraction of significant features, and the feature selection is done using the NSSWA. Moreover, the NSSWA-trained convolutional neural network (CNN) fused long short-term memory (LSTM) (CNN-LSTM), called NSSWA_CNN-LSTM, is used in PD classification. In addition, the efficacy of the model is validated via accuracy, sensitivity, specificity, loss function, mean-square error, and root-mean-square error with optimal values of 0.931, 0.934, 0.929, 0.068, 0.097, and 0.312 obtained.

关键词： KEY WORDS: convolutional neural network Parkinson's disease Namib beetle optimization algorithm long short term memory squirrel search algorithm

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SQUMUTH squirrel search based algorithm for high order mutant generation in mutation testing

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Discover Computing 2025年第1期28卷 1-17页

作者： Mohanty, Subhasish Mishra, Jyotirmaya Mohapatra, Sudhir Kumar Bejo, Seifu Detso Deferisha, Aliazar Deneke Computer Science and Engineering GIET University Odisha Gunupur India Faculty of Engineering and Technology Sri Sri University Cuttack India Department of Software Engineering CCI Wolkite University Wolkite Ethiopia Faculty of Computing and Software Engineering Arba Minch University Arba Minch Ethiopia

In today's software testing community, quality assessment remains critical, with mutation testing standing as a cornerstone technique for evaluating the effectiveness of test cases. This method involves introducing faulty code, or mutants, into the program to assess the quality of the test suite and other testing methods. However, mutation testing faces challenges such as the generation of numerous mutants, the presence of equivalent mutants that are difficult to detect through testing, and the lack of realistic mutant creation. Literature reviews indicate significant efforts to address these issues through formal solutions and heuristic methods. Recent optimization-based methods are now recognized as cost-effective result in an optimized solution. Hence, to overcome these limitations, this study introduces SQUMUTH, a novel approach for high-order mutant generation based on the squirrel search algorithm (SSA). Inspired by the foraging behavior of squirrels, SSA offers a promising solution for enhancing the efficiency and effectiveness of mutation testing. Experimental evaluations on eight well-known Java benchmark programs demonstrate that SQUMUTH outperforms existing methods. Comparative analyses of mutation scores and the rates of realistic mutants consistently show its better performance across all subject programs compared to other state-of-the-art methods such as Social Group Optimization, Binary Genetic algorithm, and random testing. The experimental results underscore its effectiveness in generating more realistic mutants. The experimental results indicated that the proposed approach has the potential to advance software testing by improving the cost-effectiveness of mutation analysis and the quality of software systems. © The Author(s) 2025.

关键词： First-order mutants Higher-order mutants Mutation score Mutation testing Software testing squirrel search algorithm

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squirrel search algorithm for multi-region combined heat and power economic dispatch incorporating renewable energy sources

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ENERGY 2019年 182卷 296-305页

作者： Basu, M. Jadavpur Univ Dept Power Engn Kolkata 700098 India

This paper suggests squirrel search algorithm (SSA) for solving intricate multi-region combined heat and power economic dispatch problem with integration of renewable energy sources. The valve point effect and proscribed workable area of thermal generators and solar and wind power uncertainty have been pondered. SSA is a newly developed swarm intelligence algorithm which emulates the dynamic scavenging activities of squirrels. The efficiency of the suggested method is revealed on a three region test system. Simulation outcomes of the suggested method have been evaluated with those attained by grey wolf optimization (GWO), particle swarm optimization (PSO), differential evolution (DE) and evolutionary programming (EP). It has been examined from the assessment that the suggested SSA has the capability to bestow with better-quality solution. (C) 2019 Elsevier Ltd. All rights reserved.

关键词： squirrel search algorithm Multi-region Co-generation units Wind power uncertainty Solar power uncertainty Tie line constraints Proscribed workable area

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squirrel search algorithm applied to effective estimation of solar PV model parameters: a real-world practice

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NEURAL COMPUTING & APPLICATIONS 2023年第18期35卷 13529-13546页

作者： Maden, Dincer Celik, Emre Houssein, Essam H. Sharma, Gulshan Duzce Univ Engn Fac Dept Elect & Elect Engn Duzce Turkiye Minia Univ Fac Comp & Informat Al Minya Egypt Univ Johannesburg Dept Elect Engn Technol Johannesburg South Africa

Model parameters estimation of solar photovoltaic (PV) cells/modules using real current-voltage (I-V) data is a critical task for the performance of PV systems. Therefore, there is a necessity to procure optimal parameters of PV models using proper optimization techniques. For this aim, squirrel search algorithm (SSA) as the recent and powerful tool is employed to accomplish the mentioned task in the single-diode model (SDM) and double-diode model (DDM) of a PV unit. Of course, better parameter values can be obtained by reducing the error between the experimental and model-based estimated data. Analyses are performed under two case studies. The former considers a standard dataset of R.T.C. France silicon solar cell, whereas the latter uses an experimental dataset of a polycrystalline CS6P-220P solar module. The I-V data of this PV module were acquired when it worked under 30 degrees C and solar radiance of 1000W/m(2) at the Engineering Faculty Campus of Duzce University, Turkey. The results of the first case study are compared with those of other prevalent approaches, which demonstrate the superiority of SSA over its competing peers. Moreover, SSA is found to handle the model parameters definition of an industrial PV module located at the university campus. Thus, the new method offers a practical tool beneficial to boost the effectiveness of PV systems.

关键词： Parameter estimation squirrel search algorithm Solar PV unit I-V characteristic Single-diode model Double-diode model

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Compressive strength prediction and optimization design of sustainable concrete based on squirrel search algorithm-extreme gradient boosting technique

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Frontiers of Structural and Civil Engineering 2023年第9期17卷 1310-1325页

作者： Enming LI Ning ZHANG Bin XI Jian ZHOU Xiaofeng GAO ETSI Minas y Energía Universidad Politécnica de MadridMadrid 28003Spain Leibniz Institute of Ecological Urban and Regional Development(IOER) Dresden 01217Germany Department of Civil and Environmental Engineering Politecnico Di MilanoMilano 20133Italy School of Resources and Safety Engineering Central South UniversityChangsha 410083China Key Laboratory of the Three Gorges Reservoir Region’s Eco-Environment(Ministry of Education) College of Environment and EcologyChongqing UniversityChongqing 400045China

Concrete is the most commonly used construction ***,its production leads to high carbon dioxide(CO_(2))emissions and energy ***,developing waste-substitutable concrete components is *** the sustainability and greenness of concrete is the focus of this *** this regard,899 data points were collected from existing studies where cement,slag,fly ash,superplasticizer,coarse aggregate,and fine aggregate were considered potential influential *** complex relationship between influential factors and concrete compressive strength makes the prediction and estimation of compressive strength *** of the traditional compressive strength test,this study combines five novel metaheuristic algorithms with extreme gradient boosting(XGB)to predict the compressive strength of green concrete based on fly ash and blast furnace *** intelligent prediction models were assessed using the root mean square error(RMSE),coefficient of determination(R^(2)),mean absolute error(MAE),and variance accounted for(VAF).The results indicated that the squirrel search algorithm-extreme gradient boosting(SSA-XGB)yielded the best overall prediction performance with R^(2) values of 0.9930 and 0.9576,VAF values of 99.30 and 95.79,MAE values of 0.52 and 2.50,RMSE of 1.34 and 3.31 for the training and testing sets,*** remaining five prediction methods yield promising ***,the developed hybrid XGB model can be introduced as an accurate and fast technique for the performance prediction of green ***,the developed SSA-XGB considered the effects of all the input factors on the compressive *** ability of the model to predict the performance of concrete with unknown proportions can play a significant role in accelerating the development and application of sustainable concrete and furthering a sustainable economy.

关键词： sustainable concrete fly ash slay extreme gradient boosting technique squirrel search algorithm parametric analysis

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Improved DV-Hop based on squirrel search algorithm for localization in wireless sensor networks

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WIRELESS NETWORKS 2021年第4期27卷 2743-2759页

作者： Abd El Ghafour, Mohamed G. Kamel, Sara H. Abouelseoud, Yasmine Alexandria Univ Fac Engn Dept Engn Math & Phys Alexandria 21544 Egypt

The ability to obtain the accurate location of nodes in wireless sensor networks is crucial for practical applications. The sensed data is meaningless if it is not accompanied by its location. Range-free localization techniques are favored to overcome the hardware limitations of sensor nodes and to avoid the costly range-based techniques. DV-Hop is a range-free localization algorithm that is well-known for its simplicity. However, it suffers from low accuracy and poor stability. In this paper, an enhanced variant of the DV-Hop algorithm is used to estimate the distance between the unknown nodes and anchor nodes, then the position estimation phase is formulated as a minimization problem solved by means of the recently developed squirrel search algorithm (SSA). The SSA is utilized to find the locations of the unknown sensor nodes. Our proposed algorithm is thus called SSIDV-Hop algorithm. The performance of our proposed algorithm is compared to that of existing localization algorithms including the DV-Hop, PSODV-Hop, GADV-Hop, and DEIDV-Hop algorithms. Extensive simulations showed that our proposed algorithm is superior to other existing algorithms as it achieved higher localization accuracy, better stability and faster convergence rate.

关键词： DV-Hop algorithm Meta-heuristics Range-free localization squirrel search algorithm Wireless sensor networks

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