Spotted Hyena Optimizer Research Articles

Dynamic Optimization and Placement of Renewable Generators and Compensators to Mitigate Electric Vehicle Charging Station Impacts Using the Spotted Hyena Optimization Algorithm

The growing adoption of electric vehicles (EVs) offers notable benefits, including reduced maintenance costs, improved performance, and environmental sustainability. However, integrating EVs into radial distribution systems (RDSs) poses challenges related to power losses and voltage stability. The model accounts for hourly variations in demand, making it crucial to determine the optimal placement of electric vehicle charging stations (EVCSs) throughout the day. This study proposes a new approach that combines EVCSs, distribution static compensators (DSTATCOMs), and renewable distributed generation (RDG) from solar and wind sources, with a focus on dynamic analysis over 24 h. The spotted hyena optimization algorithm (SHOA) is employed to determine near-global optimum locations and sizes for RDG, DSTATCOMs, and EVCSs, aiming to minimize real power loss while meeting system constraints. The SHOA outperforms traditional methods due to its unique search mechanism, which effectively balances exploration and exploitation, allowing it to find superior solutions in complex environments. Simulations on an IEEE 34-bus RDS under dynamic load conditions validate the approach, demonstrating a reduction in average power loss from 180.43 kW to 72.04 kW, a 72.6% decrease. Compared to traditional methods under constant load conditions, the SHOA achieves a 77.0% reduction in power loss, while the BESA and PSO achieve reductions of 61.1% and 44.7%, respectively. These results underscore the effectiveness of the SHOA in enhancing system performance and significantly reducing real power loss.

A Novel Spotted Hyena Optimizer for the Estimation of Equivalent Circuit Model Parameters in Li-Ion Batteries

Li-ion batteries possess significant advantages like large energy density, fast recharge, and high reliability; hence, they are widely adopted in electric vehicles, portable electronics, and military and aerospace applications. Albeit having their merits, accurate battery modeling is subjected to problems like prior information on internal chemical reactions, complexity in problem formulation, a large number of unknown parameters, and the need for extensive experimentation. Hence, this article presents a reliable Spotted Hyena Optimizer (SHO) to determine the equivalent circuit parameters of lithium-ion (Li-ion) batteries. The methodology of the SHO is derived from the living and hunting tactics of spotted hyenas, and it is efficiently applied to solve the battery parameter estimation problem. Nine unknown battery model parameters of a Samsung INR 18650-25R are determined using this method. The model parameters estimated are endorsed for five different datasets with various discharge current values. Further, the effect of parameter range and its selection is also emphasized. Secondly, for validation, various performance metrics such as Integral Squared Error, mean best, mean worst, and Standard Deviation are evaluated to authenticate the superiority of the proposed parameter extraction. From the computed results, the SHO algorithm is able to explore the search area up to 89% in the case of larger search ranges. The chosen model and range of the SHO precisely predict the behavior of the proposed Li-ion battery, and the results are in accordance with the catalog data.

Hybrid optimization‐based topology construction and DRNN‐based prediction method for data reduction in IoT

SummaryThe Internet of Things (IoT) acts as a prevalent networking setup that plays a vital role in everyday activities due to the increased services provided through uniform data collection. In this research paper, a hybrid optimization approach for the construction of heterogeneous multi‐hop IoT wireless sensor network (WSN) network topology and data aggregation and reduction is performed using a deep learning model. Initially, the IoT network is stimulated and the network topology is constructed using Namib Beetle Spotted Hyena Optimization (NBSHO) by considering different network parameters and encoding solutions. Moreover, the data aggregation and reduction in the IoT network are performed using a Deep Recurrent Neural Network (DRNN)‐based prediction model. In addition, the performance improvement of the designed NBSHO + DRNN approach is validated. Here, the designed NBSHO + DRNN method achieved a packet delivery ratio (PDR) of 0.469, energy of 0.367 J, prediction error of 0.237, and delay of 0.595 s.

Dual-Objective Accelerated Linear Convergence Spotted Hyena Optimization for Power Enhancement in Partially Shaded PV Arrays

Dual-Objective Accelerated Linear Convergence Spotted Hyena Optimization for Power Enhancement in Partially Shaded PV Arrays

QSAR modelling of enzyme inhibition toxicity of ionic liquid based on chaotic spotted hyena optimization algorithm

ABSTRACT Ionic liquids (ILs) have attracted considerable interest due to their unique properties and prospective uses in various industries. However, their potential toxicity, particularly regarding enzyme inhibition, has become a growing concern. In this study, a QSAR model was proposed to predict the enzyme inhibition toxicity of ILs. A dataset of diverse ILs with corresponding toxicity data against three enzymes was compiled. Molecular descriptors that capture the physicochemical, structural, and topological properties of the ILs were calculated. To optimize the selection of descriptors and develop a robust QSAR model, the chaotic spotted hyena optimization algorithm, a novel nature-inspired metaheuristic, was employed. The proposed algorithm efficiently searches for an optimal subset of descriptors and model parameters, enhancing the predictive performance and interpretability of the QSAR model. The developed model exhibits excellent predictive capability, with high classification accuracy and low computation time. Sensitivity analysis and molecular interpretation of the selected descriptors provide insights into the critical structural features influencing the toxicity of ILs. This study showcases the successful application of the chaotic spotted hyena optimization algorithm in QSAR modelling and contributes to a better understanding of the toxicity mechanisms of ILs, aiding in the design of safer alternatives for industrial applications.

Optimized ensemble deep learning for predictive analysis of student achievement.

Education is essential for individuals to lead fulfilling lives and attain greatness by enhancing their value. It improves self-assurance and enables individuals to navigate the complexities of modern society effectively. Despite the obstacles it faces, education continues to develop. The objective of numerous pedagogical approaches is to enhance academic performance. The development of technology, especially artificial intelligence, has caused a significant change in learning. This has made instructional materials available anytime and wherever easily accessible. Higher education institutions are adding technology to conventional teaching strategies to improve learning. This work presents an innovative approach to student performance prediction in educational settings. The strategy combines the DistilBERT with LSTM (DBTM) hybrid approach with the Spotted Hyena Optimizer (SHO) to change parameters. Regarding accuracy, log loss, and execution time, the model significantly improved over earlier models. The challenges presented by the increasing volume of data in graduate and postgraduate programs are effectively addressed by the proposed method. It produces exceptional performance metrics, including a 15-25% decrease in processing time through optimization, 98.7% accuracy, and 0.03% log loss. This work additionally demonstrates the effectiveness of DBTM-SHO in administering extensive datasets and makes an important improvement to educational data mining. It provides a robust foundation for organizations facing the challenges of evaluating student achievement in the era of vast data.

Drive Control using Multilevel Inverter for Electric Vehicle Application: A Hybrid SCSO-SNN Technique

This manuscript proposes a hybrid method for drive control using a multilevel inverter (MLI) for electric vehicle (EV) applications. The proposed technique is the joint execution of spiking neural network (SNN), and sand cat swarm optimization (SCSO), hence it is named as SCSO-SNN technique. The primary goal of the proposed technique is to improve the speed tracking performance and, energy efficiency and lessen the torque ripple of the EV. The set of control signals is generated by SCSO and the SNN is used to forecast the optimal control signal of the cascaded multilevel inverter (CMLI). The proposed technique is implemented in the MATLAB software and is evaluated their performance with various present methods. The results show that the proposed method can achieve better performance than current techniques based on speed tracking, energy efficiency, and torque ripple. The proposed method enhanced the total efficiency of the system (96%) and reduced the torque ripple (0.5%) when contrasted with current techniques like Genetic Algorithm (GA), Spotted Hyena Optimizer (SHO), Particle Swarm Optimization (PSO). The proposed method shows a mean of 1.269, a median of 1.212, and a standard deviation of 0.015, indicating high stability and consistency. It also demonstrates lower switching losses (0.159 W) and conduction losses (0.370 W) compared to existing methods, underscoring its superior energy efficiency and effectiveness.

Social media mining for business inequality: modified spotted hyena optimization based random forest

Businesses may advertise, communicate with customers and research industry trends on social media today. Due to the massive amount of data generated on social media platforms, organizations struggle to acquire valuable data and fix marketing inequalities. To combine Modified Spotted Hyena Optimization with Random Forest (MSHO-RF) for classification in social media mining for a business inequality study. Data-driven solutions to inequality in operations and customer interactions should increase prediction accuracy, lower feature dimensionality and extract actionable insights. We begin by collecting social media mining for business inequality data. Afterward, Adaptive mean filter (AMF) techniques were used to initialize the data preprocessing. With the use of Linear Discriminant Analysis (LDA), we examined the most suitable arrangement of feature extraction. This research offers a unique method MSHO-RF, to deal with the problem of business inequality using social media mining. The MSHO-RF algorithm had higher results than compared to a variety of performance parameters, featuring a 94% of F1-score, a precision rate of 97%, an accuracy rate of 98% and recall rates of 95%. Our findings suggest that firms might benefit from a framework for leveraging social media data to promote more equitable and sustainable practices if they combine MSHO-RF with social media mining for business inequality. This approach benefits companies and their stakeholders by fostering an open, data-driven ecosystem.

Efficient resource allocation in cloud environment using SHO-ANN-based hybrid approach

The cloud computing paradigm provides services to users in an on-demand fashion using high-speed Internet. This Internet-based computing paradigm provides resources on a rent basis without any fault. Virtual machine resource allocation is one of the challenging concerns in a cloud computing environment. The existing static, dynamic, and Meta-Heuristic approaches provide the solution to the virtual machine allocation problem. These techniques stuck with the local optimal solution. The slow convergence rate leads to the optimal solution locally and fails to provide the optimal solution Globally. This manuscript proposes a hybrid Spotted Hyena optimizer and artificial neural network, named the SHO-ANN technique, to provide a solution to the virtual machine assignment problem. The presented hybrid technique is evaluated and analyzed using performance metrics “Energy Consumption (Kwh) (8.54%), Host Utilization (24.8%), Average Execution Time(ms) (26.33%), SLA Violations (1.33%), and Number of Migrations (Counts) (19.73%)”. The spotted hyena optimizer is used to provide the vast data set to the ANN model for better accuracy. The hybrid approach provides an optimal solution globally with high convergence. The experimental results exhibit that the SHO-ANN outperforms the IqMc, SHO, and Genetic approaches using real workload scenarios and fabricated scenarios.

Illumination correction for dyed products with spotted hyena optimizer and regularized extreme learning machine

Dyed products exhibit varying colors when illuminated by different light sources. Human eyes possess color constancy, enabling them to automatically adjust and overlook color discrepancies, effectively self-correcting visually. However, these variations are perceived as distinct colors by computers. To eliminate the effects of lighting on the dyed products, illumination correction is needed to make the dyed products show their original color. To achieve this goal, this article presents a novel algorithm, WOA-SHO-RELW (Whale Optimization Algorithm-Spotted Hyena Optimizer-Regularized Extreme Learning Machine), designed to emulate color constancy and applies it to the illumination correction of dyed products. We employ the WOA to optimize the initial population of SHO, carefully selecting a group of suitable initial parameters. Subsequently, SHO is utilized to optimize the hidden biases and input weights of RELM, with the overarching aim of enhancing the model’s effectiveness and performance, thereby ensuring more precise illumination correction for images of dyed products. The comparison results with other algorithms show that our WOA-SHO-RELM has better comprehensive performances.

Drill tools sticking prediction based on adaptive long short-term memory

As one of the most severe disasters in deep coal mining, rockburst can be prevented through drill-hole pressure relief. However, the coal mine is characterized by high crustal stress and changeable mechanical properties of surrounding rock, which will cause drill rod deflection phenomenon, then lead to rod-deflection sticking accidents. This paper proposes a prediction method based on adaptive long short-term memory (ALSTM) for rod-deflection sticking accidents to improve drilling efficiency and reduce sticking accidents. Firstly, the sticking data is collected through the intelligent drilling condition simulation experimental platform, and then the sticking features are extracted based on the sticking data. Secondly, the sticking factor is constructed, and the sticking critical line is set. Thirdly, the good-point set and the proposed random perturbation algorithm are employed to improve the spotted hyena optimizer (SHO) to obtain the improved SHO (ISHO). Finally, we use the ISHO to optimize the hyperparameters of the long short-term memory and then establish the sticking prediction model based on ALSTM. The experimental results show that the proposed prediction model meets the demands for sticking prediction very well.

PSO-PSP-Net + InceptionV3: An optimized hyper-parameter tuned Computer-Aided Diagnostic model for liver tumor detection using CT scan slices

An automated diagnostic system leads to a supreme requirement in medical image analysis, greatly impacting the death rate due to the high spreading rate of liver tumors. However, the traditional histopathological diagnostic processes cause more disruption in the medical detection framework because of the procedure of manual estimation of CT scans and medical labor-intensive, which lead to the time consumption process. It is a highly challenging task for the segmentation of liver tumor because of the irregular appearance, distinct densities, patchy edges, and tumor sizes. To overcome this issue, an automated computer-aided diagnosis framework has been utilized to diagnose the liver cancer quickly and accurately with the less workload of expert radiologists.Presently, Deep learning-based techniques have been applied to identify the tumor regions and assist radiologists in the initial diagnosis and scrutinizing the severity level. The proposed work presents a modified pyramid scene parsing network (PSP-Net) model based on InceptionV3 deep network architecture with a particle swarm optimization algorithm, which increases the efficiency and effectiveness of the liver diagnostic model. Three publicly accessible benchmark datasets have been engaged with the pre-processing task accomplished by image normalization and data augmentation techniques. Moreover, a hyper-parameter tuned improved PSO-PSP-Net algorithm has analyzed the optimal set of gathered features. In the experimental evaluation, the proposed model outperformed than grey wolf optimization (GWO-PSP-Net), PSO-U-Net, GWO-U-Net, opposition based spotted hyena optimization (O-SHO-CRNN), and grey wolf-class topper optimization (GW-CTO) + U-Net by 8.42 %, 4.9 %, 9.54 %, 5.12 %, and 1.75 %, respectively in terms of accuracy for LiTS dataset. The developed PSO-PSP-Net model attained better accuracy than GWO-PSP-Net, PSO-U-Net, GWO-U-Net, and Residual Atrous U-Net (RA-U-Net) for the segmentation of Type-I and Type-II liver tumors by 6.88 %, 8.44 %, 13.75 %, 5.99 % and 6.12 %, 9.74 %, 17.3 %, and 5.11 %, respectively for 3DIRCAD-b1 dataset. The obtained accuracy of the developed model is 14.09 %, 7.54 %, 16.36 %, 2.64 %, and 14.9 %, 8.86 %, 17.76 %, and 4.87 % maximized than GWO-PSP-Net, PSO-U-Net, GWO-U-Net, and RA-U-Net, respectively, for the segmentation of the infected arteries and bones in the 3DIRCAD-b1 dataset. For the CHAOS dataset, the improved PSO-PSP-Net model outperformed GWO-PSP-Net, PSO-U-Net, and GWO-U-Net, respectively, in terms of overall accuracy by 7.44 %, 8.85 %, and 17.2 %, respectively. The investigational analysis validates the proposed PSO-PSP-Net model's effectiveness and performance in obtaining high segmentation accuracy in comparison with other state-of-the-art approaches.

Minimizing the impact of electric vehicle charging station with distributed generation and distribution static synchronous compensator using PSR index and spotted hyena optimizer algorithm on the radial distribution system

This research investigates the impact of electric vehicle charging station on power loss, voltage stability, and reliability within radial distribution systems. With the escalating use of electric vehicles, understanding these effects is crucial for ensuring the stability and efficiency of radial distribution systems infrastructure. The study formulates the electric vehicle charging station allocation problem as a multi-objective function, integrating power loss, voltage stability, and reliability metrics expressed as the PSR index. Using the IEEE 69-bus system as a test system, the research explores the ramifications of electric vehicle charging station integration and proposes strategies to mitigate their effects. An innovative combination of distributed generation and distribution static compensator are employed to alleviate the impact of electric vehicle charging station on radial distribution systems. A recent optimization methodology called spotted hyena optimizer algorithm is introduced to determine optimal electric vehicle charging station locations, with results compared to other existing algorithms. The study assesses potential power losses induced by additional electric vehicle loads, explores strategies to optimize charging rates for minimizing resistive losses, and evaluates voltage stability and reliability implications. The important results include objective function values obtained using various algorithms: Spotted hyena optimizer algorithm (0.7683), cuckoo search algorithm (0.7709), bat algorithm (0.8035), and African vultures optimization algorithm (0.7856). The findings demonstrate that the proposed algorithm outperforms other algorithms in minimizing the objective function value, indicating its efficacy in optimizing electric vehicle charging station placement within radial distribution systems. In conclusion, this research contributes valuable insights into the challenges associated with integrating electric vehicle charging station into radial distribution systems and provides innovative solutions for optimizing electric vehicle charging station placement, thereby advancing the understanding and management of distribution systems in the context of electric vehicle adoption.

Enhancing Spotted Hyena optimization with fuzzy logic for complex engineering optimization

Enhancing Spotted Hyena optimization with fuzzy logic for complex engineering optimization

Spotted hyena optimizer with ensemble deep learning based intrusion detection in internet of things environment

The Internet of Things (IoT) has many potential uses in the day-to-day operations of individuals, companies, and governments. It makes linking all devices to the internet a realistic possibility. Convincing IoT devices to work together to implement several real-world applications is a challenging feat. Security issues impact innovative platform applications due to the current security state in IoT-based operations. As a result, intrusion detection systems (IDSs) tailored to IoT platforms are essential for protecting against security breaches caused by the Internet of Things (IoT) that exploit its vulnerabilities. Issues with data loss, dangers, service interruption, and external hostile assaults are all part of the IoT security landscape. Designing and implementing appropriate security solutions for IoT environments is the main emphasis of this research. Within the Internet of Things (IoT) context, this research creates a Spotted Hyena Optimizer (SHO-EDLID) method for intrusion detection using ensemble deep learning. The main goal of the demonstrated SHO-EDLID method was to detect and categorize intrusions in an Internet of Things setting. It comprises many subprocesses, including feature selection, categorization, and pre-processing. The SHO-EDLID method uses a SHO-based feature selection strategy to identify the best feature subsets. It then used an ensemble of three DL models— a deep belief network (DBN), a stacked autoencoder (SAE), and a bidirectional recurrent neural network (BiRNN)— to detect and name cyberattacks. Finally, the DL models’ parameters are tuned using the Adabelief optimizer. A comprehensive simulation was run to illustrate that the offered model performed better. According to a thorough comparative analysis, the suggested method outperformed other recent approaches. Purpose of the Manuscript: To identify the best feature subsets, the SHO-EDLID method used the SHO-based feature selection method... Afterward, cyberattack identification and tracking were carried out using an ensemble of three DL models: DBN, SAE, and BiRNN. The final step in optimizing the DL models’ parameters is the Adabelief optimizer. The main comparative results: The proposed model present the Comparative analysis of SHO-EDLID algorithm with other existing systems and its outperform the performance in precision 97.50, accuracy 99.56, Recall 98.42, F-Measure.97.95.

Safeguarding media integrity: A hybrid optimized deep feature fusion based deepfake detection in videos

The surge in deepfake techniques allows even non-experts to create deceptive videos using only source and target images, posing risks such as impersonation, character defamation, and spreading misinformation on various platforms. Existing methods often prove insufficient, particularly when faced with videos generated through diverse deepfake techniques that feature variations in lighting conditions and ethnicities. This paper introduces a novel approach called Hybrid Optimized Deep Feature Fusion-based Deepfake Detection (HODFF-DD) in videos, employing a spotted hyena optimizer. The HODFF-DD framework is designed to be resilient across ethnicities and varying lighting conditions, capable of detecting deepfake videos created by different techniques. The architecture comprises two integral components: a customized model incorporating InceptionResNetV1 and InceptionResNetV2, and a Bidirectional Long-Short Term Memory (BiLSTM). In this framework, faces extracted from videos undergo feature extraction at the frame level using our custom model comprising InceptionResNetV1 and InceptionResNetV2. The resulting sequences of features are then utilized to train a temporally aware BiLSTM for binary classification between real and fake videos. To optimize network weights, a bio-inspired spotted hyena optimizer is applied during the training process. Evaluation on diverse datasets, including the Kaggle dataset, FaceForensics++ (FF++) encompassing videos manipulated using various techniques (DeepFakes, FaceSwap, Face2Face, FaceShifter, and NeuralTextures), and the FakeAVCeleb dataset, demonstrates the effectiveness of our presented approach. Our method achieves an accuracy exceeding 90 % on subsets like DeepFakes, FaceSwap, and Face2Face.

Taylor-Spotted Cat Optimization (Taylor-SCO): An Energy-Efficient Cluster Head Selection Algorithm with Improved Trust Factor for Data Routing in WSN

Wireless Sensor Network (WSN) has inexpensive, small, and less energy sensor nodes, which are allocated in random ways in particular areas for measuring the phenomenon or events in that field. In recent days, WSN has played a vital role in various applications, like industrial monitoring, medical treatments, agricultural monitoring, and military operations. However, the security challenges and network lifetime are the main issues in the existing methods. In order to overcome these issues, the Taylor-Spotted Cat Optimization (Taylor-SCO) approach is devised in this paper. Here, the Cluster Heads (CHs) are selected based on the developed optimization method, named Taylor CSO. Moreover, the delay, distance, and energy parameters are considered for effective Cluster Head Selection (CHS). Here, route maintenance is also done for increasing network lifetime and reducing complexities. In addition, the Modified K-Vertex Disjoint Paths Routing (KVDPR) model is established for routing. The modification of KVDPR is carried out using several factors, such as link reliability, throughput, and various trust factors. Moreover, the developed Taylor-SCO algorithm is developed by combining the Spotted Hyena Optimizer (SHO), Cat Swarm Optimization (CSO) algorithm, and Taylor series. The Taylor-SCO achieved better performance with energy consumption, trust, and throughput of 0.00037 J, 0.51, and 793160 kbps.

Deep learning hybridization for improved malware detection in smart Internet of Things

The rapid expansion of AI-enabled Internet of Things (IoT) devices presents significant security challenges, impacting both privacy and organizational resources. The dynamic increase in big data generated by IoT devices poses a persistent problem, particularly in making decisions based on the continuously growing data. To address this challenge in a dynamic environment, this study introduces a specialized BERT-based Feed Forward Neural Network Framework (BEFNet) designed for IoT scenarios. In this evaluation, a novel framework with distinct modules is employed for a thorough analysis of 8 datasets, each representing a different type of malware. BEFSONet is optimized using the Spotted Hyena Optimizer (SO), highlighting its adaptability to diverse shapes of malware data. Thorough exploratory analyses and comparative evaluations underscore BEFSONet’s exceptional performance metrics, achieving 97.99% accuracy, 97.96 Matthews Correlation Coefficient, 97% F1-Score, 98.37% Area under the ROC Curve(AUC-ROC), and 95.89 Cohen’s Kappa. This research positions BEFSONet as a robust defense mechanism in the era of IoT security, offering an effective solution to evolving challenges in dynamic decision-making environments.

Dynamic task scheduling in edge cloud systems using deep recurrent neural networks and environment learning approaches

The rapid growth of the cloud computing landscape has created significant challenges in managing the escalating volume of data and diverse resources within the cloud environment, catering to a broad spectrum of users ranging from individuals to large corporations. Ineffectual resource allocation in cloud systems poses a threat to overall performance, necessitating the equitable distribution of resources among stakeholders to ensure profitability and customer satisfaction. This paper addresses the critical issue of resource management in cloud computing through the introduction of a Dynamic Task Scheduling with Virtual Machine allocation (DTS-VM) strategy, incorporating Edge-Cloud computing for the Internet of Things (IoT). The proposed approach begins by employing a Recurrent Neural Network (RNN) algorithm to classify user tasks into Low Priority, Mid Priority, and High Priority categories. Tasks are then assigned to Edge nodes based on their priority, optimizing efficiency through the application of the Spotted Hyena Optimization (SHO) algorithm for selecting the most suitable edge node. To address potential overloads on the edge, a Fuzzy approach evaluates offloading decisions using multiple metrics. Finally, optimal Virtual Machine allocation is achieved through the application of the Stable Matching algorithm. The seamless integration of these components ensures a dynamic and efficient allocation of resources, preventing the prolonged withholding of customer requests due to the absence of essential resources. The proposed system aims to enhance overall cloud system performance and user satisfaction while maintaining organizational profitability. The effectiveness of the DTS-VM strategy is validated through comprehensive testing and evaluation, showcasing its potential to address the challenges posed by the diverse and expanding cloud computing landscape.

PCSSHO: An implementation of Percentage of Circle Search and Spotted Hyena Optimizer for multi‐constraint load balancing and routing framework in FANET

SummaryFlying ad hoc network (FANET) is a technology that has seen tremendous growth in the past years due to its application in various military and civil developments. Conventional topology‐aided routing schemes are not suitable for large‐scale FANETs. This is because of the higher mobility rate in UAVs with the architecture differences. Hence, several path entries in routing become invalid and the neighboring nodes can be occupied before the interruption. Hence, it is very important to solve the afore‐explained issues in FANET. Initially, the task is assigned to the UAVs. These tasks are performed by satisfying the demands such as timing, congestion, and energy to attain the effective load‐balancing performance. Then, the optimal clustering and cluster head (CH) selection are performed using the developed Percentage of Circle Search and Spotted Hyena Optimizer (PCSSHO) in the communication path. Here, the feature selection is done based on constraints like position, speed, moving direction, height variation, link quality, and inter‐ and intra‐cluster distance. Then, the developed PCSSHO model performs routing by considering different constraints such as “end‐to‐end delay, delivery ratio, power consumption, and link quality.” Thus, the recommenced load balancing model in FANET secures an enhanced performance rate than the conventional load balancing frameworks.