Grey Wolf Optimization Approach Research Articles

A Robust Dual Watermarking using Grey Wolf Optimization, Selective Encryption and Fast Flexible De-Noising Convolution Neural Network

Digital data interchange in IoT systems has flourished with the advancement of industrial internet technologies. Particularly, more and more digital images created by intelligent and industrial equipment are sent there are security concerns related to the website, server, and cloud. To accomplish this issue, in this article a secure watermarking approach is suggested in this research to effectively improve security, invisibility, and resilience at the same time. The adequate coefficient for information embedding is first determined using an assortment of transform domain techniques Discrete-Wavelet-Transform (DWT), Heisenberg- decomposition (HD), and Tensor-singular-value-decomposition (T-SVD). Using the grey wolf optimization (GWO) approach, we estimated the appropriate embedding factors to provide a reasonable compromise between robustness and invisibility. To enable the suggested approach to offer an additional level of security, a selective encryption technique is used on the watermark image. Moreover, FFDNet—a quick and adaptable de-noising convolutional-neural–network is working to increase the robustness-of-the suggested algorithm. The results demonstrate that the recommended watermarking method generates exceptional imperceptibility, resilience, and security against standard attacks. Additionally, the comparison demonstrates that the suggested algorithm performs better than alternative strategies. The following metrics were reached: 51.6966 dB, 0.9944, 0.9961, and 0.2849 for the peak-signal- to-noise ratio (PSNR), Structural-Similarity-Index (SSIM), number of changing pixels per second (NPCR), and unified-averaged-changed-intensity (UACI) average scores.

Improvement of soft clay soil with a combination of pre-gelatinized corn starch and nanoparticle agro waste: A multi-objective grey wolf optimizer approach

The present study aimed to assess the potential of a bio-inspired algorithm, multi-objective grey wolf optimization algorithm (MOGWO), to optimize the strength properties (California bearing ratio (CBR) and unconfined compressive strength (UCS)) of an expansive subgrade soil. This optimization process involves the use of two additives, namely a bio-polymer, pregelatinized corn starch (PGCS), and a nanoparticle agro waste, rice husk ash (RHA), blended with the soil in different mix ratios determined by a 32 factorial experimental design. The CBR samples were cured for 7 days, while the UCS samples were cured for 1, 7, and 28 days. To optimize the expansive subgrade soil strength, regression models were developed using PGCS and RHA as predictors for CBR and UCS, serving as fitness functions in the slightly modified MOGWO optimization technique. Next, the optimization analysis produced non-dominated solutions. The results obtained from the laboratory experiments and optimization analysis revealed that there was significant improvement in the UCS and CBR of the soil. These improvements can be attributed to the pozzolanic reaction between the soil-RHA matrix, the formation of intercalated and exfoliated nanocomposites, and the hydrophilic interaction of PGCS. By applying the slightly modified MOGWO technique, the study achieved optimal enhancements in UCS (710.3 kN/m2) and CBR (24.2%) when the expansive subgrade soil was mixed with 0.2637% PGCS and 12.2413% RHA. The results demonstrate the potential of the MOGWO technique in improving the properties of expansive subgrade soil.

Lifespan Enhancement of WSN for IoT - Modified Fuzzy Grey Wolf Optimizer (MFGWO) Approach

Wireless Sensor Networks (WSNs) are gaining prominence for diverse applications, including environmental monitoring and industrial automation. Yet, their energy constraint poses a significant challenge. Clustering, a prevalent technique, optimizes energy utilization by grouping nodes into clusters and appointing a cluster head (CH) to aggregate data and communicate with the base station (BS). This paper presents a novel clustering and CH selection algorithm for a energy varied WSNs, leveraging modified fuzzy c-means (FCM) clustering and Grey Wolf Optimization (GWO). Modified FCM partitions nodes based on their similarity, while GWO identifies CHs in each cluster, considering energy levels, centrality, distance from the BS, and dynamic node distribution. Simulation results demonstrate the superior energy efficiency and network lifetime of our proposed approach compared to existing algorithms. Key Word: Wireless Sensor Networks, Modified Fuzzy C Means algorithm (MFCM), Grey Wolf Optimizer (GWO)

A novel hybrid method based MPP tracking design using boost converter for solar power systems

The convergence time is one of the key parameters for evaluating the energy conversion efficiency of PV power systems in the experimental process of tracking the maximum power point (MPP) in real-time. When the PV system is partially shaded, for example, MPP tracking easily slips into the local MPP and takes a long time. To overcome this drawback, a new solution for a stand-alone PV power system has been proposed that combines the incremental conductance (In-Cond) algorithm and the improved grey wolf optimization (GWO) approach. The improved GWO technique changed the methodology is used to find the global power area in this proposed method, and it is integrated with the In-Cond algorithm to fast obtain the global MPP. To demonstrate the efficacy of the suggested strategy, MATLAB/ Simulation and experiment results of the PV system are provided. The proposed hybrid method has a quick response time of 0.18 to 0.42 seconds for good transient oscillation and global power tracking, whereas the classic GWO and particle swarm optimization (PSO) methods take 0.82 to 2.1 seconds and 0.68 to 2.2 seconds, respectively. The global MPP was obtained not only with uniform irradiance intensity, but also with partial shade.

Optimal Location of EV Charging Stations in the Distribution System Considering GWO Algorithm

Objectives: The transition from conventional vehicles to electric vehicles (EVs) has been significantly influenced by the current scarcity of fossil fuels and the environmental concerns surrounding greenhouse gas emissions. The exploration of the optimal location of electric vehicle charging stations has been prompted by the electrification of the transportation system and the increasing demand for EVs. Methods: One of the main challenges is the detrimental impact of improper positioning of charging stations on the power distribution network. The Grey Wolf Optimization (GWO) approach is used to determine the effective Charging Stations (CS) locations to minimize the losses, which is the major objective of the proposed technique. An IEEE 33 bus system is used to implement the suggested technique. Findings : The proposed method finds the suitable deployments for EVCS within the distribution system, which is of utmost importance due to the constraints imposed by limited battery capacity and prolonged charging periods. This article proposes the optimal location for EVCS to minimize losses inside the distribution network. Novelty: The effectiveness of the proposed GWO is evaluated by comparing it with the PSO optimal algorithm. Finally, a comparison of system voltage and nominal voltage is presented and the probability comparison of EV location with the base case and the proposed algorithms is presented. Keywords: Electric Vehicle Charging Station, Distribution System, GWO, System and Nominal Voltage

Optimal placement of phasor measurement units using topology transformation method based on Grey Wolf optimization approach

The optimal placement of phasor measurement units (PMUs) requires minimizing both the number of PMUs required and ensure that the whole power system is totally observable. To identify a power system as observable, it is necessary to know the voltages of all the buses in the power system. This paper suggests rules of selection for the topology transformation method, which implies a process of merging a zero injection bus and one of its neighboring buses. The selection of a bus chosen to merge with a zero injection bus will affect the result of the merging process. To determine the most appropriate bus to merge with the zero injection bus, the proposed method will use four rules designed to determine the minimum number of PMUs required to achieve full observability of the power system. The problem is formulated and solved by a grey wolf optimization (GWO) approach. The suggested GWO has been applied to the IEEE 14-bus, 24-bus, and New England 39-bus.

Using the Grey Wolf Aquila Synergistic Algorithm for Design Problems in Structural Engineering.

The Aquila Optimizer (AO) is a metaheuristic algorithm that is inspired by the hunting behavior of the Aquila bird. The AO approach has been proven to perform effectively on a range of benchmark optimization issues. However, the AO algorithm may suffer from limited exploration ability in specific situations. To increase the exploration ability of the AO algorithm, this work offers a hybrid approach that employs the alpha position of the Grey Wolf Optimizer (GWO) to drive the search process of the AO algorithm. At the same time, we applied the quasi-opposition-based learning (QOBL) strategy in each phase of the Aquila Optimizer algorithm. This strategy develops quasi-oppositional solutions to current solutions. The quasi-oppositional solutions are then utilized to direct the search phase of the AO algorithm. The GWO method is also notable for its resistance to noise. This means that it can perform effectively even when the objective function is noisy. The AO algorithm, on the other hand, may be sensitive to noise. By integrating the GWO approach into the AO algorithm, we can strengthen its robustness to noise, and hence, improve its performance in real-world issues. In order to evaluate the effectiveness of the technique, the algorithm was benchmarked on 23 well-known test functions and CEC2017 test functions and compared with other popular metaheuristic algorithms. The findings demonstrate that our proposed method has excellent efficacy. Finally, it was applied to five practical engineering issues, and the results showed that the technique is suitable for tough problems with uncertain search spaces.

Optimizing the Capacitated Vehicle Routing Problem at PQR Company: A Genetic Algorithm and Grey Wolf Optimizer Approach

Product distribution for companies in the industrial sector is an important issue as it is necessary to increase customer satisfaction and reduce costs. In this study, Nondominated-Sorting Genetic Algorithm II is used to solve Capacitated Vehicle Routing Problem in a case study dataset from the biggest fertilizer company in Indonesia. NSGA-II algorithm reduced the need for vehicles in product distribution with an advantage of 44.7%, while GWO algorithm yielded better results with an increase of time of 16.9%.

Cost‐Efficient Network Design in Multichannel WSNs With Power Control: A Grey Wolf Optimization Approach to Routing and Clustering

Wireless sensor networks (WSNs) require optimized energy consumption and an improved packet delivery ratio (PDR) for optimal performance. Clustering and routing strategies are frequently used to reduce energy usage, while multiradio (MR) multichannel (MC) systems improve PDR levels. Transmission power control (TPC) features in both energy consumption reduction and PDR improvement. While the mentioned techniques are used in previous studies, they were investigated separately and without a holistic approach. This study discusses the challenge of obtaining high‐PDR, energy‐efficient, and cost‐effective clustering and routing in WSNs. As a means of reducing deployment costs, a heterogeneous setting with energy‐constraint normal sensors for environmental monitoring as well as some high‐energy, TPC‐enabled, MR super nodes is assumed. The super nodes are considered as CHs and are responsible for collecting the sensed data from the normal sensors. Installing additional radios on super nodes enables static channel assignment (CA), which yields high PDR with low imposed overhead on the network. The mentioned TPC‐based MC heterogeneous setting, which yields cost‐effectiveness, high PDR, and energy efficiency, was not investigated in previous studies. The considered problem is decoupled into two phases of configuring the super nodes and normal sensors, which are solved using the grey wolf optimization (GWO) algorithm. The performed simulations show an average improvement of our proposed algorithm in PDR, energy consumption, consumed energy per delivered bit, and network lifetime by 9%, 30%, 65%, and 44%, respectively.

Enhanced Brain Tumor Segmentation and Size Estimation in MRI Samples using Hybrid Optimization

The area of medical imaging specialization, specifically in the context of brain tumor segmentation, has long been challenged by the inherent complexity and variability of brain structures. Traditional segmentation methods often struggle to accurately differentiate between the diverse types of tissues within the brain, such as white matter, grey matter, and cerebrospinal fluid, leading to suboptimal results in tumor identification and delineation. These limitations necessitate the development of more advanced and precise segmentation techniques to enhance diagnostic accuracy and treatment planning. In response to these challenges, the proposed study introduces a novel segmentation approach that combines the Grey Wolf Optimization approach and the Cuckoo Search approach within a Fuzzy C-Means (FCM) framework. The integration of GWO and CS is designed to leverage their respective strengths in optimizing the segmentation of brain tissues. This hybrid approach was rigorously tested across multiple Magnetic Resonance Imaging (MRI) datasets, demonstrating significant enhancements over existing segmentation methods. The study observed a 4,9 % improvement in accuracy, 3,5 % increase in precision, 4,5 % higher recall, 3,2 % less delay, and 2,5 % better specificity in tumor segmentation. The implications of these advancements are profound. By achieving higher precision and accuracy in brain tumor segmentation, the proposed method can substantially aid in early diagnosis and accurate staging of brain tumors, eventually leading to more effective treatment planning and improved patient outcomes. Furthermore, the integration of GWO and CS within the FCM process sets a new benchmark in medical imaging, paving the way for future investigation in the field of study

Unleashing wastewater heat Recovery's potential in smart building systems: Grey wolf-assisted optimization aided by artificial neural networks

This article presents an innovative and efficient way to address residential dwellings' substantial heating needs. The primary objective is to utilize the heat from wastewater to enhance energy efficiency through a control framework based on predetermined rules. This framework aims to increase the incoming air temperature at the air handling unit. A thorough evaluation is carried out to analyze all aspects of the proposed system compared to an identical system that does not incorporate the wastewater heat recovery process. The practicality of the concept is assessed for a residential building located in Beijing, China, employing the TRNSYS software. The most optimal operating condition is achieved via the grey wolf optimizer and TOPSIS decision-making approach equipped with the artificial neural network using MATLAB. Then, the proposed system's performance under optimal conditions is compared with similar works in the literature. According to the results, compared to the conventional system, a higher performance efficiency of 6 % and lower levelized cost of heating of 15.6 $/MWh is obtained by implementing the wastewater heat recovery process. The parametric study results also demonstrate a conflicting change in techno-economic and environmental indicators when altering the primary decision variables, highlighting the necessity for multi-criteria optimization. What stands out from the optimization outcomes is that the grey wolf method increases the efficiency and CO2 saving by around 5.2 and 531.2 kg/year while reducing the levelized cost of heating by about 13.6 $/MWh, respectively. The optimization results reveal that this condition is attained by raising the heat exchanger's effectiveness and the number of residences and decreasing the wastewater temperature. According to the scatter distribution of key parameters, the energy wheel effectiveness has low sensitivity, and the optimal points of tank volume are distributed within the range of 3 m3 and 4 m3.

Performance enhancement of large-scale linear dynamic MIMO systems using GWO-PID controller

The multi-input multi-output (MIMO) technique is becoming grown and integrated into wireless wideband communication. MIMO techniques suffer from a large-scale linear dynamic problem, it will be easy to adjust the proportional-integral-derivative (PID) of a continuous system, unlike the nonlinear model. This work displays the tuning of the PID controller for MIMO systems utilizing a statistical grey wolf optimization (GWO) and evaluated by objective function as integral time absolute error (ITAE). The instantaneous adjusting characteristic GWO approach is the criterion that distinguishes such a combination-proposed strategy from that existing in the traditional PID approach. The GWO algorithm searching-based methodology is used to determine the adequate gain factors of the PID controller. The suggested approach guarantees stability as the initial scheme for a steady state condition. A combination of ITAE combined with the GWO reduction method is adopted to reduce the steady-state transient time responses between the higher-order initial scheme and the unit amplitude response. Simulation outcomes are illustrated using MATLAB software to show the capability of adopting the GWO scheme for PID controlling.

QoS-aware UAV mounted base station deployment in a disaster scenario with user position estimation

The base station installed on the unmanned aerial vehicle, referred to as the UAV-mounted base station (UBS), is considered an innovative paradigm for restoring cellular connectivity in areas where a terrestrial network does not exist, has been demolished, or is overcrowded. The challenging issue in this context is how to acquire the position information of user equipment (UE), characterize the air-to-ground (A2G) channel, and identify the optimal locations for quality-of-service (QoS)-aware 3D UBS deployment. To tackle these issues, we proposed a framework for 3D UBS deployment with the goal of maximizing the number of UEs covered. First, unlike the existing UBS deployment schemes, which ignore the UEs' localization, we propose a received signal strength indicator (RSSI)-based localization (RBL) approach to localize the randomly distributed UEs. Then, the 3D UBS deployment problem is divided into horizontal position optimization to meet the QoS requirements of individual UEs’ and vertical position optimization for path loss minimization. We propose a K-means initialized grey wolf optimization (KIGWO) approach in which UBSs are deployed to maximize the covered UEs percentage while taking the QoS requirements of individual UEs’ and UBS’ total capacity into account. Finally, the effectiveness of the proposed approach is demonstrated through Monte Carlo simulations conducted in four different environmental scenarios.

Grey wolf optimization approach for enhancing the transient stability of microgrid using fractional-order PID-based inertia injection controller

Grey wolf optimization approach for enhancing the transient stability of microgrid using fractional-order PID-based inertia injection controller

A Grey Wolf Optimization Algorithm-Based Optimal Reactive Power Dispatch with Wind-Integrated Power Systems

Keeping the bus voltage within acceptable limits depends on dispatching reactive power. Power quality improves as a result of creating an effective power flow system, which also helps to reduce power loss. Therefore, optimal reactive power dispatch (ORPD) studies aim at designing appropriate system configurations to enable a reliable operation of power systems. Establishment of such a configuration is handled through control variables in power systems. Various control variables, such as adjusting generator bus voltages, transformer tap locations, and switchable shunt capacitor sizes, are utilized to achieve this objective. Additionally, the integration of wind power can greatly impact power quality and mitigate power loss. In this study, the Grey Wolf Optimization (GWO) approach was applied to the ORPD issue for the first time to discover the best placement of newly installed wind power in the power system while taking into account tap changer settings, shunt capacitor sizes, and generated power levels. The main objective was to determine optimal wind placement to minimize power loss and voltage deviation, while maintaining control variables within specified limits. On the basis of IEEE 30-bus and IEEE 118-bus systems, the performance of the proposed method was investigated. The results demonstrated the superiority of GWO in multiple scenarios. In IEEE-30, GWO outperformed the PSO, GA, ABC, OGSA, HBMO, and HFA methods, reducing total loss by 10.36%, 18.03%, 9.19%, 7.13%, 5.23%, and 7.73%, respectively, and voltage deviation by 68.00%, 1.59%, 36.34%, 41.97%, 46.29%, and 71.08%, respectively. In wind integration scenarios, GWO achieved the simultaneous reduction of power loss and voltage deviation. In IEEE-118, GWO outperformed the ABC, PSO, GSA, and CFA methods, reducing power loss by approximately 19.91%, 16.83%, 14.09%, and 4.36%, respectively, and voltage deviation by 8.50%, 14.15%, 16.19%, and 7.17%, respectively. These promising results highlighted the potential of the GWO algorithm to facilitate the integration of renewable energy sources, and its role in promoting sustainable energy solutions. In addition, this study conducted an analysis to investigate site-specific wind placement by using the Weibull distribution function and commercial wind turbines.

Fractional-Order Model-Free Predictive Control for Voltage Source Inverters

Currently, a two-level voltage source inverter (2L-VSI) is regarded as the cornerstone of modern industrial applications. However, the control of VSIs is a challenging task due to their nonlinear and time-varying nature. This paper proposes employing the fractional-order controller (FOC) to improve the performance of model-free predictive control (MFPC) of the 2L-VSI voltage control in uninterruptible power supply (UPS) applications. In the conventional MFPC based on the ultra-local model (ULM), the unknown variable that includes all the system disturbances is estimated using algebraic identification, which is insufficient to improve the prediction accuracy in the predictive control. The proposed FO-MFPC uses fractional-order proportional-integral control (FOPI) to estimate the unknown function associated with the MFPC. To get the best performance from the FOPI, its parameters are optimally designed using the grey wolf optimization (GWO) approach. The number of iterations of the GWO is 100, while the grey wolf’s number is 20. The proposed GWO algorithm achieves a small fitness function value of approximately 0.156. In addition, the GWO algorithm nearly finds the optimal parameters after 80 iterations for the defined objective function. The performance of the proposed FO-MFPC controller is compared to that of conventional MFPC for the three loading cases and conditions. Using MATLAB simulations, the simulation results indicated the superiority of the proposed FO-MFPC controller over the conventional MFPC in steady state and transient responses. Moreover, the total harmonic distortion (THD) of the output voltage at different sampling times proves the excellent quality of the output voltage with the proposed FO-MFPC controller over the conventional MFPC controller. The results confirm the robustness of the two control systems against parameter mismatches. Additionally, using the TMS320F28379D kit, the experimental verification of the proposed FO-MFPC control strategy is implemented for 2L-VSI on the basis of the Hardware-in-the-Loop (HIL) simulator, demonstrating the applicability and effective performance of our proposed control strategy under realistic circumstances.

Hybrid GWO-PSO based optimal placement and sizing of multiple PV-DG units for power loss reduction and voltage profile improvement

Distributed generation (DG) is integrated in a passive distribution system to reduce power loss, improve voltage profile, and increase power output. To reap the most benefits of the distribution system, the best location and appropriate DG size must be determined. This paper presents a hybrid Grey wolf Optimizer (GWO) and Particle swarm optimization (PSO) approach for determining the best placement and DG size while considering a multi-objective function that includes active and reactive power loss minimization as well as voltage profile enhancement. Dilla distribution system was used as a case study and the weighted technique was used to convert to a single objective function while taking into account multiple constraints such as bus voltage limit, DG output limit, and branch current limit. DG penetration is limited to up 60% of the total active load on the feeder and a forward–backward sweep load flow algorithm was used to generate the load flow solutions. The findings of the study show that combining three PV-DGs (Case 3) is the best way to improve voltage profile and minimize losses. In addition, the proposed hybrid GWO-PSO algorithm performed better compared to the other four algorithms (Grey Wolf Optimization (GWO), Whale Optimization Algorithm (WOA), Particle swarm optimization (PSO), and sine cosine algorithm (SCA)) in terms of achieving the best multi-objective function (MOF) outcome.

Optimization of the Wire Electric Discharge Machining Process of Nitinol-60 Shape Memory Alloy Using Taguchi-Pareto Design of Experiments, Grey-Wolf Analysis, and Desirability Function Analysis

The nitinol-60 shape memory alloy has been rated as the most widely utilized material in real-life industrial applications, including biomedical appliances, coupling and sealing elements, and activators, among others. However, less is known about its optimization characteristics while taking advantage to choose the best parameter in a surface integrity analysis using the wire EDM process. In this research, the authors proposed a robust Taguchi-Pareto (TP)-grey wolf optimization (GWO)-desirability function analysis (DFA) scheme that hybridizes the TP method, GWO approach, and DFA method. The point of coupling of the TP method to the GWO is the introduction of the discriminated signal-to-noise ratios contained in the selected 80-20 Pareto rule of the TP method into the objective function of the GWO, which was converted from multiple responses to a single response accommodated by the GWO. The comparative results of five outputs of the wire EDM process before and after optimization reveals the following understanding. For the CR, a gain of 398% was observed whereas for the outputs named Rz, Rt, SCD, and RLT, losses of 0.0996, 0.0875, 0.0821, and 0.0332 were recorded. This discrimination of signal-to-noise ratio based on the 80-20 rule makes the research different from previous studies, restricting the data fed into the GWO scheme to the most essential to accomplishing the TP-GWO-DFA scheme proposed. The use of the TP-GWO-DFA method is efficient given the limited volume of data required to optimize the wire EDM process parameters of nitinol.

Detection and Classification of Power Quality Disturbances Using Stock Well Transform and Improved Grey Wolf Optimization-Based Kernel Extreme Learning Machine

Power quality disturbances (PQDs) in modern electrical power systems, caused by the integration of nonlinear power electronic devices and erratic distributed generation (DG), lead to interruptions and significant energy losses for end users. However, conventional methods for PQDs classification face challenges in dealing with noise interference and feature selection. To address these challenges, this research paper proposes a novel approach that combines the stockwell transform (ST) with an improved grey wolf optimization-based kernel extreme learning machine to enhance classification accuracy. The stockwell transform is utilized to extract meaningful features from the power quality (PQ) signals, which are subsequently input into the kernel extreme learning machine (KELM). Furthermore, the parameters of the KELM model are optimized using the improved grey wolf optimization (IGWO) approach to improve the accuracy of classification. To evaluate the performance of the proposed method, real-time implementation is considered by incorporating PQDs data with signal-to-noise ratios (SNR) of 20 dB, 30 dB, and 40 dB into the original synthetic signals. Multiple noise conditions are simulated to assess the proposed model ability to identify and classify disturbance signals. The results demonstrate a detection accuracy of 99.76% under noiseless conditions, indicating the model’s high accuracy. Moreover, the proposed method exhibits robustness to noise, achieving accuracies of 98.86%, 98.32%, and 97.3% at SNR levels of 40 dB, 30 dB, and 20 dB, respectively. In the end, this work has performed a comparative study with other previously published work. Compared with other classification methods, the algorithm proposed in this paper has higher accuracy, and it is an efficient and feasible classification method.

Improved Hybrid Swarm Intelligence for Optimizing the Energy in WSN

In this current century, most industries are moving towards automation, where human intervention is dramatically reduced. This revolution leads to industrial revolution 4.0, which uses the Internet of Things (IoT) and wireless sensor networks (WSN). With its associated applications, this IoT device is used to compute the received WSN data from devices and transfer it to remote locations for assistance. In general, WSNs, the gateways are a long distance from the base station (BS) and are communicated through the gateways nearer to the BS. At the gateway, which is closer to the BS, energy drains faster because of the heavy load, which leads to energy issues around the BS. Since the sensors are battery-operated, either replacement or recharging of those sensor node batteries is not possible after it is deployed to their corresponding areas. In that situation, energy plays a vital role in sensor survival. Concerning reducing the network energy consumption and increasing the network lifetime, this paper proposed an efficient cluster head selection using Improved Social spider Optimization with a Rough Set (ISSRS) and routing path selection to reduce the network load using the Improved Grey wolf optimization (IGWO) approach. (i) Using ISSRS, the initial clusters are formed with the local nodes, and the cluster head is chosen. (ii) Load balancing through routing path selection using IGWO. The simulation results prove that the proposed optimization-based approaches efficiently reduce the energy through load balancing compared to existing systems in terms of energy efficiency, packet delivery ratio, network throughput, and packet loss percentage.