GWO Algorithm Research Articles

Feature selection in wind speed forecasting systems based on meta-heuristic optimization.

Technology for anticipating wind speed can improve the safety and stability of power networks with heavy wind penetration. Due to the unpredictability and instability of the wind, it is challenging to accurately forecast wind power and speed. Several approaches have been developed to improve this accuracy based on processing time series data. This work proposes a method for predicting wind speed with high accuracy based on a novel weighted ensemble model. The weight values in the proposed model are optimized using an adaptive dynamic grey wolf-dipper throated optimization (ADGWDTO) algorithm. The original GWO algorithm is redesigned to emulate the dynamic group-based cooperative to address the difficulty of establishing the balance between exploration and exploitation. Quick bowing movements and a white breast, which distinguish the dipper throated birds hunting method, are employed to improve the proposed algorithm exploration capability. The proposed ADGWDTO algorithm optimizes the hyperparameters of the multi-layer perceptron (MLP), K-nearest regressor (KNR), and Long Short-Term Memory (LSTM) regression models. A dataset from Kaggle entitled Global Energy Forecasting Competition 2012 is employed to assess the proposed algorithm. The findings confirm that the proposed ADGWDTO algorithm outperforms the literature's state-of-the-art wind speed forecasting algorithms. The proposed binary ADGWDTO algorithm achieved average fitness of 0.9209 with a standard deviation fitness of 0.7432 for feature selection, and the proposed weighted optimized ensemble model (Ensemble using ADGWDTO) achieved a root mean square error of 0.0035 compared to state-of-the-art algorithms. The proposed algorithm's stability and robustness are confirmed by statistical analysis of several tests, such as one-way analysis of variance (ANOVA) and Wilcoxon's rank-sum.

Research on transformer fault diagnosis based on ISOMAP and IChOA‐LSSVM

AbstractOil‐immersed transformers play an important role in the stable operation of power systems. In order to improve the accuracy of transformer fault diagnosis, a transformer fault diagnosis method based on Isomap and IChOA‐LSSVM is proposed. Firstly, Isomap is used to reduce the dimensionality of the 14‐dimensional transformer fault characteristics data to eliminate redundant data between variables. In addition, IChOA is proposed to optimise the Least Squares Support Vector Machine (LSSVM) parameters and establish an optimal diagnosis model based on LSSVM. For ChOA, three improvement methods are proposed. Circle mapping is used instead of the original population initialisation to improve population diversity. In addition, the location weighting strategy with proportional weights and the Gaussian Corsi variation strategy is proposed to improve the optimization accuracy and efficiency of ChOA. The improved ChOA is compared with the original ChOA, PSO and GWO algorithms by five benchmark test functions. Finally, the improved Chimpanzee Optimisation algorithm was used to find the parameters of the LSSVM to obtain the fault diagnosis model combining Isomap and IChOA‐LSSVM. The model is compared with PSO‐LSSVM, ChOA‐LSSVM and GWO‐LSSVM. The diagnostic accuracy is 90.83%, 81.67%, 83.33% and 80%, respectively. The results demonstrate that the proposed method can effectively improve the performance of transformer fault diagnosis.

A Joint Allocation Method of Multi-Jammer Cooperative Jamming Resources Based on Suppression Effectiveness

This paper studies the resource allocation problem when multiple jammers follow the aircraft formation to support ground penetration. A joint optimization allocation method of multi-jammer beam-power based on the improved artificial bee colony (IABC) algorithm is proposed. The air-to-ground “many-to-many” assault of the multi-jammer cooperative suppression jamming model is given. The constant false alarm probability detection model of the networked radar is used to evaluate the suppression effect, and a coordinated control model of multi-jammer jamming beams and emitting power is established. The optimal allocation scheme under different combat scenarios is solved by using the IABC algorithm. The search efficiency of the ABC algorithm is improved by cross mutation operation and the replacement of the worst nectar source, and the search performance of the algorithm is enhanced by the random key encoding. Due to the infeasible solution generated by the special random key encoding method, the feasible adjustment strategy is adopted. By changing the jamming parameters, the effect on the detection probability of the radar network is analyzed. Compared to the GWO, SCA, BBO and ABC algorithms, the jamming resource allocation scheme obtained by the proposed IABC algorithm makes the radar detection probability lower. The IABC algorithm has better global search capability and robustness.

Weighted mean of vectors optimization algorithm and its application in designing the power system stabilizer

Accurate design of the power system stabilizer (PSS) models is a crucial issue due to their significant impact on the stability of power system operation. However, identifying the parameters of a PSS model is a challenging task owing to its nonlinearity and multi-modality characteristics. Due to such characteristics, handling algorithms may be prone to stagnation in local optima. Therefore, this paper proposes a potent integrated optimization algorithm by comprising the weIghted meaN oF vectOrs (INFO) optimizer with chaotic-orthogonal based learning (COBL) and Gaussian bare-bones (GBB) strategies, named INFO-GBB, for achieving the optimal parameters of a PSS model used in a single-machine infinite-bus (SMIB) system. In the INFO-GBB, the COBL aims to enhance the searching capability to explore new regions using the orthogonal design aspect and thus improving the diversity of solutions. Also, the GBB is adopted to assist the algorithm to perform an immediate vicinity of the best solution and thus enhances the exploitation capabilities. The effectiveness and efficacy of the INFO-GBB algorithm is validated on CEC 2020 benchmark suits and the designing task of the PSS model. The achieved results by the INFO-GBB are compared with eighteen well-known algorithms. The statistical verifications along with the Friedman test have ascertained that the INFO-GBB is capable of achieving promising performances compared to the other counterparts. The results obtained based on the Friedman test illustrate that the INFO-GBB offers superior performance over the state-of-the-art algorithms as it outperforms fifteen out of eighteen algorithms by an average rank greater than 61% for benchmark problems while outperforming O-LSHADE, LSHADE, and TSA algorithms by 25%,33%, and 58%, respectively. Furthermore, the applicability of the INFO-GBB is realized through designing the PSS model used in a SMIB system. The obtained results indicate that the INFO-GBB algorithm exhibits accurate and superior performance compared to other peers as it provides the lowest value for the integral of time multiplied absolute error (ITAE) performance index which is used as an objective function. For example, the achieved results of the mean ITAE found by INFO-GBB is 1.36E−03 with improvement percentages of 24.93%, 19.78%, 13.04%, 26.64%, and 24.86%, over the LSHADE, GWO, EO, RSA, and original INFO algorithms, respectively. Therefore, the INFO-GBB can efficiently affirm its superiority and stability to deal with the function optimization task and parameters’ estimation of the PSS model.

Fusion Swarm-Intelligence-Based Decision Optimization for Energy-Efficient Train-Stopping Schemes

To solve the decision problem of train stopping schemes, this paper introduces the static game into the optimal configuration of stopping time to realize the rational decision of train operation. First, a train energy consumption model is constructed with the lowest energy consumption of train operation as the optimization objective. In addition, a Mustang optimization algorithm based on cubic chaos mapping, the population hierarchy mechanism, the golden sine strategy, and the Levy flight strategy was designed for solving the problem of it being easy for the traditional population intelligence algorithm to fall into a local optimum when solving complex problems. Lastly, simulation experiments were conducted to compare the designed algorithm with PSO, GA, WOA, GWO, and other cutting-edge optimization algorithms in cross-sectional simulations, and the results show that the algorithm had excellent global optimization finding and convergence capabilities. The simulation results show that the research in this paper can provide effective decisions for the dwell time of trains at multiple stations, and promote the intelligent operation of the train system.

A Robust Controller Design based on Kharitonov’s Theorem for Frequency Control in an Interconnected Power System

The load frequency control of power systems is often carried out using methods that are dependent on the system load and parameters. Therefore, the controller design is not robust in unforeseen cases such an attack on the power system, variations in system parameters, or changes in load. In such methods, there is a need for an attack detection tool, and moreover, the controller parameters need to be adjusted as the load and power system parameters change. In this paper, Kharitonov's theorem was applied to design a robust decentralized load frequency control for a two-area power system in the presence of electric vehicle fleets as a power source that were targeted by a cyberattack. Furthermore, the robustness of the system against system nonlinearities was demonstrated by testing the efficacy of the controller on both linear and nonlinear systems. The controller design was robust such that there was no need to change the gains of the controller even during an attack. This was compared with the performance of controllers designed using GWO algorithm and fuzzy logic that needed retuning for different case studies with different variations in system parameters, load, or inclusion of a cyberattack to the electric vehicle fleets.

Task Scheduling with Altered Grey Wolf Optimization (AGWO) in Mobile Cloud Computing using Cloudlet

Mobile devices can improve their battery life by offloading their tasks to a nearby cloudlet instead of executing tasks on the mobile device. Because mobile devices have low-speed processors, small-size memory, and limited battery. As the mobile devices are moving, they are connected and disconnected from the cloudlets. So, their tasks are offloaded to the new cloudlets and also migrated from one cloudlet to another until the tasks finish their execution. Scheduling these tasks in the cloudlet will reduce the tasks' execution time and the mobile device's power consumption using this proposed new method (AGWO). The GWO algorithm is modified to accept the inputs from a two-dimensional array instead of sequence inputs and search for the prey within the two-dimensional array instead of an unknown circle area. This method deals with the arrival time of the task, task size, and big task. The migration of the partially executed task dynamically to other VMs is also examined. This proposed method also reduces the average scheduling delay and increases the percentage of requests executed by the cloudlet than other variations of GWO and other research algorithms.

Prediction Dynamics in Cotton Aphid Using Unmanned Aerial Vehicle Multispectral Images and Vegetation Indices

Cotton harvest can be increased by having real-time information on the state of cotton aphid populations. However, traditional cotton aphid monitoring relies on ground sample methods supported by models such as linear regression, resulting in low forecast accuracy. Therefore, this paper purposes to enhance the precision of the remote sensing prediction model by investigating the cotton aphid prediction model construction approach. We explored the effectiveness of the XGBoost algorithm combined with the GWO algorithm and SVR method for cotton aphid prediction relying on vegetation indices derived from UAV multispectral photography. Originally, 12 indices related to cotton aphids were calculated by UAV multispectral reflectance. Additionally, the optimal index combination for pest prediction was determined utilizing analysis of correction and two-way ANOVA, combined with the XGBoost algorithm. Furthermore, a pest prevalence prediction model for cotton aphids was constructed via the SVR methodology associated with the optimal catalog combination, and the model was optimized using the GWO algorithm. Compared with the seven algorithms, experimental results demonstrate that the MSE and MAE of the XGBoost-GWO-SVR model are reduced by 90.20% and 70.36% (SVR), 90.14% and 70.26% (XGBoost-SVR), 7.47% and 0.14% (XGBoost-GA-SVR), 5.80% and 0.11% (XGBoost-PSO-SVR), 12.06% and 58.95% (LR), and 84.77% and 89.22% (BPNN), whereas the <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$R^{2}$ </tex-math></inline-formula> is increased by 22.5% (SVR and XGBoost-SVR), 0.3% (LR), and 12.51% (BPNN). The <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$R^{2}$ </tex-math></inline-formula> of the prediction model of XGBoost-SVR combined with GWO, PSO, and GA is not significantly different. Among these models, the XGBoost-GWO-SVR obtained the highest <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$R^{2}$ </tex-math></inline-formula> of 0.980 and the lowest MAE of 2.838.

Optimal Placement Identification of Multiple DG Types Using Optimization Technique

In this paper, a combination algorithm called GAIPSO, which combines GA and a better version of the classic particle swarm optimization process, is used. In order to calculate the data enhancement in voltage profile, this study uses the GWO algorithm. The ideal position for the proposed charging points inside the distribution system is the goal. The received comment thread solution (site &amp; station size) is further re-optimized by PSO, improving both the functionality and outcome overall. Studies based on simulations show that the above mentioned technique outperforms GA, GWO, and PSO in respect of an improved voltage profiles as well as the quality of the solution found for the objective function. Optimum planning for the charging station’s location and size. the IEEE 33 bus system. The suggested approach takes into consideration the IEEE 33 bus service. The received thread solutions (site and station size) is further re-optimized by PSO, improving both the performance and outcome overall.

Novel Optimized Feature Selection Using Metaheuristics Applied to Physical Benchmark Datasets

In data mining and machine learning, feature selection is a critical part of the process of selecting the optimal subset of features based on the target data. There are 2n potential feature subsets for every n features in a dataset, making it difficult to pick the best set of features using standard approaches. Consequently, in this research, a new metaheuristics-based feature selection technique based on an adaptive squirrel search optimization algorithm (ASSOA) has been proposed. When using metaheuristics to pick features, it is common for the selection of features to vary across runs, which can lead to instability. Because of this, we used the adaptive squirrel search to balance exploration and exploitation duties more evenly in the optimization process. For the selection of the best subset of features, we recommend using the binary ASSOA search strategy we developed before. According to the suggested approach, the number of features picked is reduced while maximizing classification accuracy. A ten-feature dataset from the University of California, Irvine (UCI) repository was used to test the proposed method’s performance vs. eleven other state-of-the-art approaches, including binary grey wolf optimization (bGWO), binary hybrid grey wolf and particle swarm optimization (bGWO-PSO), bPSO, binary stochastic fractal search (bSFS), binary whale optimization algorithm (bWOA), binary modified grey wolf optimization (bMGWO), binary multiverse optimization (bMVO), binary bowerbird optimization (bSBO), binary hybrid GWO and genetic algorithm (bGWO-GA), binary firefly algorithm (bFA), and bGA methods. Experimental results confirm the superiority and effectiveness of the proposed algorithm for solving the problem of feature selection.

Workflow Scheduling in Cloud Computing Environment by Combining Particle Swarm Optimization and Grey Wolf Optimization

Scheduling workflows is a vital challenge in cloud computing due to its NP-complete nature and if an efficient workflow task scheduling algorithm is not used then it affects the system’s overall performance. Therefore, there is a need for an efficient workflow task scheduling algorithm that can distribute dependent tasks to virtual machines efficiently. In this paper, a hybrid workflow task scheduling algorithm based on a combination of Particle Swarm Optimization and Grey Wolf Optimization (PSO GWO) algorithms, is proposed. PSO GWO overcomes the disadvantages of both PSO and GWO algorithms by improving the exploitation (local search) of PSO algorithm and exploration (global search) of GWO algorithm. This leads to better balance between exploration and exploitation, consequently it minimizes the makespan with 5.52% compared to GWO and 3.68% compared to PSO. The degree of imbalance reduced upto 33.22% compared to GWO and 17.61% compared to PSO, improves the convergence rate as well depending on number tasks and iterations. CloudSim tool is used to evaluate the proposed algorithm. The simulation results confirmed that the proposed method performs better than both of the standard PSO and GWO in terms of makespan, degree of imbalance and convergence rate

A GWO-P&amp;O Algorithm MPPT for PV Systems Under UIC and PSC

The operation of PV systems can experience uniform (UIC) and partial insolation (PSC) that depends on its environment. Many MPPT algorithm has been proposed in literature such as P&amp;O, and many metaheuristics algorithm such as PSO and GWO. Those algorithm only work at a certain environmental condition. The P&amp;O algorithm only work at UIC but fail to track maximum power at PSC hence reducing efficiency of MPPT system when it is experiencing UIC and PSC. The GWO algorithm can track maximum power at PSC but when the change of insolation to UIC can shift power output below maximum power hence reducing efficiency of MPPT system. In this paper another method is proposed by implementing the result of GWO to the input of the P&amp;O algorithm subsequently the GWO is reset periodically to search a new maximum power point to anticipate any environmental changes. This new method is called a GWO-P&amp;O algorithm. Simulation results show that the GWO-P&amp;O algorithm yields better efficiency compared to the GWO or the P&amp;O algorithm in case the modules of PV array experiencing UIC and PSCs. Simulation is done using MATLAB/SIMULINK software.

A hybrid algorithm based on grey wolf optimizer and differential evolution for UAV path planning

Autonomous navigation is significant to UAVs, especially in disaster scenarios. A Hybrid GWO and Differential Evolution (HGWODE) algorithm is developed to solve UAV path planning. In HGWODE, GWO and DE algorithms cooperate well to balance exploitation and exploration. The position-updated equation of GWO is improved, which makes alpha, beta, and delta wolves search around the alpha wolf, and omega wolves search around the top three wolves to boost the exploitation. A rank-based mutation strategy is implemented in DE algorithm to promote exploitation while maintaining the exploration capacity. We test HGWODE on CEC 2014 and UAV path planning. The proposed HGWODE is superior to GWO and several GWO variants when solving test functions and UAV path planning models. UAV path from HGWODE is smoother and shorter than its rivals.

Performance Prediction of Building Integrated Photovoltaic System Using Hybrid Deep Learning Algorithm

In a grid-connected photovoltaic system, forecasting is a necessary and critical step. Solar Power is very nonlinear; this article develops and analyses building integrated photovoltaic (BIPV) forecasting algorithms for different timeframes, such as an hour, a day, and a week ahead, to manage grid operation effectively. However, a model built for a certain time scale may improve performance at that time scale but cannot be utilized to make predictions at other time scales. Here, we demonstrate how to use the multitask learning algorithm to create a multitime scale model for solar BIPV forecasting. Effective resource distribution across several tasks is shown. The suggested multitask learning approach is implemented using LSTM neural networks and evaluated over a range of horizons. We employed a modified version of the Chicken Swarm Optimizer (CSO) that takes the best features of the CSO and the GWO algorithms and merges them into one efficient approach to estimate the hyperparameters of the proposed LSTM model. The proposed approach consistently outperformed state-of-the-art single-timescale forecasting algorithms across all time scales.

Prediction of coal mine gas emission based on hybrid machine learning model

Coal mine gas accident is one of the most serious threats in the process of safe coal mine mining, making it important to accurately predict coal mine gas emission. To improve the accuracy of coal mine gas emission prediction, a hybrid machine learning prediction model combining random forest (RF) algorithm, improved gray wolf optimizer (IGWO) algorithm and support vector regression (SVR) algorithm is proposed, the model prediction effect is validated by using actual measured gas emission data from a coal mine. Firstly, the RF algorithm is used to screen 13 influencing factors of coal mine gas emission, and finally 6 influencing factors are selected as the input variables of the prediction model; Secondly, the GWO algorithm is improved using the nonlinear convergence factor and DLH search strategy to obtain the IGWO algorithm; Finally, the IGWO algorithm is used to optimize the parameters of the SVR algorithm, and the RF-IGWO-SVR model is established. The results show that the mean absolute percentage error, mean absolute error and root mean square error of the RF-GWO-SVR model are 1.55%, 0.0759, and 0.1103, respectively, and this result is better than the other comparative models, which indicates that the model can effectively improve the prediction accuracy of coal mine gas emission and provide a new model for coal mine gas emission prediction.

Parameter Estimation of AI/p-Si Schottky Barrier Diode Using Different Meta-Heuristic Optimization Techniques

Schottky barrier diodes (SBD) are crucial in the electronics sector. The electronic properties of SBD are characterized by three basic electrical parameters as the ideality factor (n), barrier height (ΦSB) and series resistance (RS). These parameters are significant in designing and producing SBD. This paper presents a comprehensive review of metaheuristic optimization techniques used to determine the fundamental electrical parameters of SBD using experimental forward current–voltage (I-V) characteristics. In the study, popular meta-heuristic optimization techniques, such as GA, PSO, ALO, EO, DA, HHO, GWO, WOA, MFO, MVO, and SCA algorithms, are employed for the parameter estimation of SBD. Among these chosen algorithms, meta-heuristic optimization techniques, such as GWO, WOA, HHO and AHA, have been used for the first time in the literature for parameter estimation of SBD. Firstly, parameter values have been calculated using experimental (I-V) characteristics. Following that, the findings were compared to the values that had been estimated utilizing optimization techniques. Moreover, the performance of meta-heuristic optimization algorithms in determining the basic parameters of SBD was evaluated statistically. Results show that AHA has higher and symmetrical estimation performance than other presented algorithms in determining the basic parameters of SBD with R2 = 0.999925806, MAE = 2.79065 × 10−7, RMSE = 7.49521 × 10−7, RE = 0.422088668, and STD = 7.68031 × 10−7 statistical values.

Design of Task Scheduling and Fault Tolerance Mechanism Based on GWO Algorithm for Attaining Better QoS in Cloud System

Design of Task Scheduling and Fault Tolerance Mechanism Based on GWO Algorithm for Attaining Better QoS in Cloud System

The Intelligent Selection Method of Distribution Sites Driven by the Intelligent Optimization Algorithm

For the internal enterprise, the intelligent selection of logistics distribution sites can optimize the distribution route, which is conducive to reducing the total distribution cost and improving the enterprise revenue. This paper takes urban logistics distribution point selection optimization as the research content and compares and analyses the applicability of different intelligent algorithms to logistics data. The AFSA is selected as the optimization algorithm, and knowledge learning is introduced to optimize the algorithm. The optimized AFSA model is applied to the mathematical model of distribution point selection. An intelligent algorithm-driven logistics distribution point selection model is established, and the optimized AFSA is used to solve the problem. Based on the actual case of CSC Logistics Company, the route of the distribution point in a region of CSC Logistics Company is optimized and the model is validated and solved. The results show that GWO and AFSA search capabilities are significantly better than of other intelligent algorithms, but there is some instability in the GWO algorithm. The AFSA is most suitable for solving logistics-related problems. The optimized AFSA considering knowledge learning has high efficiency and good optimization results. CSC Company uses this intelligent algorithm to select distribution sites intelligently, which shortens the total logistics path and improves the distribution efficiency. The total mileage of the initial route is 115 km. After intelligent algorithm optimization, the total mileage of the distribution changes to 83 km, which reduces by 32 km and 28% compared with the original route. The whole distribution process saved about 1.5 hours, which fully optimized the efficiency of distribution.

Performance improvement and Lyapunov stability analysis of nonlinear systems using hybrid optimization techniques

AbstractUsing Hybrid optimization algorithms for nonlinear systems analysis is a novel approach. It is a powerful technique that uses the exploitation ability of one algorithm and the exploration ability of another algorithm, to find the best solution. Literature survey reveals that hybrid algorithms not only show quality response but also give faster convergence of error for nonlinear systems. In this paper, hybrid optimization techniques based proportional integral derivative (PID) controller is used for benchmark problems: Continuous stirred tank reactor (CSTR), Inverted pendulum and blood glucose system. Two recent hybrid algorithms: Particle swarm optimization‐Gravitational search algorithm (PSO‐GSA) and Particle swarm optimization‐Grey wolf algorithm (PSO‐GWO) are implemented to control the temperature and concentration of CSTR, pendulum angle of inverted pendulum, glucose concentration and insulin level of blood glucose system. In PID and PSOGWO algorithms, the exploration abilities of GSA and GWO combined with the exploitation ability of PSO have been used. The performance of these algorithms is then compared with individual PSO, GSA, and GWO algorithms proving their superiority. Stability is ensured using the Lyapunov approach while the robustness of the systems is checked using the parameter perturbation technique. Simulation results show substantial improvement in the performance of these systems by using these meta‐heuristic hybrid optimization techniques. A comparative analysis of these algorithms has also been done.

PSGWO: An Energy-efficient Framework in IoT Based on Swarm Intelligence

Background: Internet-of-things (IoT) has been developed for use in a variety of fields in recent years. The IoT network is embedded with numerous sensors that can sense data directly from the environment. The network's sensing components function as sources, observing environmental occurrences and sending important data to the appropriate data centers. When the sensors detect the stated development, they send the data to a central station. On the other hand, sensors have limited processing, energy, transmission, and memory capacities, which might have a detrimental influence on the system. Objectives: We have suggested an energy-efficient framework based on Swarm Intelligence in IoT. The idea behind using Swarm Intelligence is the probabilistic-based global search phenomena that suit well for IoT networks because of the randomization of nodes. Our framework considers the prominent metaheuristic concepts responsible for the overall performance of the IoT network. Our current research is based on lowering sensor energy consumption in IoT networks, resulting in a longer network lifetime. Methods: This study selects the most appropriate potential node in the IoT network to make it energy-efficient. It suggests a technique combining PSO's exploitation capabilities with the GWO's exploration capabilities to avoid local minima problems and convergence issues. The proposed method PSGWO is compared with the traditional PSO, GWO, Hybrid WSO-SA, and HABC-MBOA algorithms based on several performance metrics in our research study. Results: The results of our tests reveal that this hybrid strategy beats all other ways tested, and the energy consumption rate of the proposed framework is decreased by 23.8% in the case of PSO, 20.2% in the case of GWO, 31.5% in the case of hybrid WSO-SA, and 29.6% in the case of HABC-MBOA, respectively. Conclusion: In this study, several performance parameters, including energy consumption, network lifetime, live nodes, temperature, and throughput, are taken into account to choose the best potential node for the IoT network. Using various simulations, the performance of the proposed algorithm was evaluated and compared to the metaheuristic techniques. Moreover, PSGWO is found to be improved, and the energy consumption rate is decreased.