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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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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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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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A Discrete Multi-Objective squirrel search algorithm for Energy-Efficient Distributed Heterogeneous Permutation Flowshop with Variable Processing Speed

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Computers, Materials & Continua 2024年第10期81卷 1757-1787页

作者： Liang Zeng Ziyang Ding Junyang Shi Shanshan Wang School of Electrical and Electronic Engineering Hubei University of TechnologyWuhan430068China Hubei Key Laboratory for High-Efficiency Utilization of Solar Energy and Operation Control of Energy Storage System Hubei University of TechnologyWuhan430068China Xiangyang Industrial Institute of Hubei University of Technology Xiangyang441100China

In the manufacturing industry,reasonable scheduling can greatly improve production efficiency,while excessive resource consumption highlights the growing significance of energy conservation in *** paper studies the problem of energy-efficient distributed heterogeneous permutation flowshop problem with variable processing speed(DHPFSP-VPS),considering both the minimum makespan and total energy consumption(TEC)as objectives.A discrete multi-objective squirrel search algorithm(DMSSA)is proposed to solve the *** makes four improvements based on the squirrel search ***,in terms of the population initialization strategy,four hybrid initialization methods targeting different objectives are proposed to enhance the quality of initial ***,enhancements are made to the population hierarchy system and position updating methods of the squirrel search algorithm,making it more suitable for discrete scheduling ***,regarding the search strategy,six local searches are designed based on problem characteristics to enhance search ***,a dynamic predator strategy based on Q-learning is devised to effectively balance DMSSA’s capability for global exploration and local ***,two speed control energy-efficient strategies are designed to reduce *** comparative experiments are conducted in this paper to validate the effectiveness of the proposed *** results of comparing DMSSA with other algorithms demonstrate its superior performance and its potential for efficient solving of the DHPFSP-VPS problem.

关键词： Distributed heterogeneous permutation flowshop problem squirrel search algorithm muli-objective optimization energy-efficient variable processing speed

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Efficient Task Scheduling in Cloud Computing: A Multiobjective Strategy Using Horse Herd-squirrel search algorithm

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INTERNATIONAL TRANSACTIONS ON ELECTRICAL ENERGY SYSTEMS 2024年第1期2024卷

作者： Parthasaradi, V. Karunamurthy, A. Hussaian Basha, C. H. Senthilkumar, S. EGS Pillay Engn Coll Dept Elect & Commun Engn Nagapattinam 611002 Tamil Nadu India Sri Manakula Vinayagar Engn Coll Dept MCA Pondicherry 605107 India SR Univ Dept Elect & Elect Engn Hanumakonda 506371 Telangana India

Cloud computing (CC) is a technology that enables the delivery of IT services outside of the workplace. CC, on the other hand, has had several drawbacks. The task scheduling issue is taken as one of the important difficulties because a solid mapping between available resources and users' activities is essential to reduce the execution time of users' jobs (i.e., minimize makespan) and maximize resource utilization. Because the service provider must offer several customers' benefits at distinct times and from distinct locations, task scheduling is indeed a serious challenge in CC. As a result, in the CC environment, these operations must be scheduled in a more dynamic and timely manner. The objective is to provide an enhanced task scheduling algorithm for allocating the task of the user to different computing resources. The major aim of the research work is to reduce the cost and the execution time as well as to improve the resource utilization of the task scheduling problem using the improved support vector machine (ISVM) and the optimization concept. The novel algorithm used here merges two familiar algorithms as squirrel search algorithm (SSA) and the horse herd optimization algorithm (HOA) leading to a new hybrid metaheuristic algorithm called the horse herd-squirrel search algorithm (HO-SSA). The developed HO-SSA assists in introducing a multiobjective optimization for efficiently handling task scheduling issues in the cloud sector. The proposed HO-SSA method for the task scheduling in CC model in terms of cost is 22.22%, 15.73%, and 38.74% better than SSA, HOA, and TSA, respectively. Similarly, the proposed HO-SSA method for the task scheduling in CC model with respect to energy is 9.68%, 5.35%, and 22.50% superior to SSA, HOA, and TSA, respectively. The proposed method outperformed the existing methods like SSA, HOA, and TSA in terms of cost, energy, degree of imbalance, makespan, speedup, and efficiency.

关键词： cloud computing horse herd optimization multiobjective optimization squirrel search algorithm task scheduling

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Sine Cosine Embedded squirrel search algorithm for Global Optimization and Engineering Design

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CLUSTER COMPUTING-THE JOURNAL OF NETWORKS SOFTWARE TOOLS AND APPLICATIONS 2024年第4期27卷 4415-4448页

作者： Zeng, Liang Shi, Junyang Li, Ming Wang, Shanshan HuBei Univ Technol Sch Elect & Elect Engn Wuhan 430068 Hubei Peoples R China Hubei Univ Technol Hubei Key Lab High Efficiency Utilizat Solar Energ Wuhan 430068 Hubei Peoples R China Hubei Univ Technol Xiangyang Ind Inst Xiangyang 441100 Hubei Peoples R China

The squirrel search algorithm (SSA) is an innovative optimization method that takes inspiration from the foraging and gliding behavior of squirrels. Despite its simple structure and stable performance, it is prone to the same issues as other algorithms, such as falling into local optima and experiencing premature convergence. To address this problem, this paper proposes an improved squirrel search algorithm embedded with the Sine Cosine algorithm (SCSSA). Firstly, the Sine Cosine algorithm is introduced into the SSA to enhance its local exploitation ability. Secondly, the Sobol sequence is utilized to generate the initial population, resulting in higher quality initial solutions. Thirdly, dimensional learning is applied to squirrels on both hickory and oak trees, promoting population diversity and preventing local optima. Finally, the glide constant Gc in SSA is adjusted to decay nonlinearly with iteration count, starting with a large value that gradually decreases in the early stage to facilitate global exploration, and then rapidly decreasing in the later stage to promote local exploitation. Extensive experiments are conducted on 23 classic benchmark functions, the CEC2017 test set, and three engineering problems. The experimental results show that SCSSA can effectively maintain population diversity and can achieve a balance between exploration and exploitation. It consistently outperforms the comparison algorithms in terms of numerical optimization and convergence rate.

关键词： squirrel search algorithm Sine cosine algorithm Dimensional learning Sobol Optimization

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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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Research on short-term photovoltaic power generation forecasting model based on multi-strategy improved squirrel search algorithm and support vector machine

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SCIENTIFIC REPORTS 2024年第1期14卷 1-17页

作者： Zhu, Ruijin Li, Tingyu Tang, Bo Tibet Agr & Husb Coll Elect Engn Coll Nyingchi 860000 Peoples R China Daqing Power Supply Co State Grid Heilongjiang Ele Daqing 163000 Peoples R China

Solar photovoltaic (PV) power generation is susceptible to environmental factors, and redundant features can disrupt prediction accuracy. To achieve rapid and accurate online prediction, we propose a method that combines Principal Component Analysis (PCA) with a multi-strategy improved squirrel search algorithm (SSA) to optimize Support Vector Machine (MISSA-SVM) for prediction. Initially, to mitigate the impact of redundant features on prediction accuracy, KPCA is employed for feature dimensionality reduction. Subsequently, SVM is suggested as the foundational algorithm for constructing the prediction model. Furthermore, to address the influence of hyperparameter selection on model performance, SSA is introduced for optimizing SVM hyperparameters, with the aim of establishing the optimal prediction model. Moreover, to enhance solution efficiency and accuracy, a multi-strategy approach termed MISSA is proposed, which integrates Population Initialization based on the Tent map, Nonlinear Predator Presence Probability, Chaotic-based Dynamic Opposition-based Learning, and Selection Strategy, to refine SSA. Finally, through case studies, the performance of MISSA optimization is assessed using challenging CEC2021 test functions, demonstrating its high optimization performance, stability, and significance. Subsequently, the performance of the prediction model is validated using two datasets, showcasing that the proposed prediction method achieves high accuracy and robust prediction stability.

关键词： PV power generation forecasting Support vector machine regression squirrel search algorithm Multi strategy improvement method Principal component analysis

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The optimal multi-degree reduction of Ball Bezier curves using an improved squirrel search algorithm

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ENGINEERING WITH COMPUTERS 2023年第2期39卷 1143-1166页

作者： Cao, Huanxin Zheng, Hongchan Hu, Gang Northwestern Polytech Univ Sch Math & Stat Xian 710072 Shaanxi Peoples R China Xian Univ Technol Dept Appl Math Xian 710054 Shaanxi Peoples R China

As a new nature-inspired swarm intelligence optimizer, squirrel search algorithm (SSA) has shown potential to solve several real-world problems, but for some complex problems, it still suffers from degraded performance. In this paper, a hybrid squirrel search algorithm (NOSSA) combined with optimal neighborhood update and quasi-opposition learning strategies is proposed to overcome the drawback of population update guided only by leading individuals in SSA. NOSSA adopts a stochastic optimal neighborhood update strategy to improve convergence speed and accuracy, and incorporates a Quasi-opposition learning strategy to enhance exploration. To verify its efficiency, NOSSA has been tested on 23 classic benchmark functions. Experimental results show that NOSSA has better performance on search-efficiency, convergence rate and solution accuracy compared with the representative stochastic optimizers. Furthermore, intelligent algorithms are introduced into the optimal multi-degree reduction of Ball Bezier curves and two new methods are proposed for the multi-degree reduction of center curve and radius function of Ball Bezier curve respectively. Experimental results demonstrate the effectiveness of the methods and show that NOSSA performs best among the representative stochastic optimizers in the degree reduction. The methods achieve the automatic and intelligent degree reduction of Ball Bezier curves.

关键词： squirrel search algorithm Optimal neighborhood Quasi-opposition learning Ball Bezier curve Multi-degree reduction

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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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