Whale Optimization Algorithm Algorithm Research Articles

PM2.5 prediction based on modified whale optimization algorithm and support vector regression

In order to obtain the pattern of variation of PM2.5concentrations in the atmosphere in Nanchang City, we build a Support Vector Regression(SVR) with modified Whale Optimization Algorithm(WOA) hybrid model (namely mWOA-SVR model) that can predict the PM2.5concentration. Firstly, according to the Pearson correlation coefficient (PCC) method to examine the dynamic relationship between air pollutants and meteorological factors together with them, PM10, SO2and CO were selected as air pollutant concentration characteristics, while daily maximum and minimum temperatures, and wind power levels were selected as meteorological characteristics; then, using modified WOA algorithm for parameter selection of SVR model, four sets of better parameter combinations were found; finally, the mWOA-SVR model was built by the four sets parameters to predict PM2.5concentration. The results show that the prediction accuracy of mixed mWOA-SVR model with pollutant concentration plus weather factors as the feature was higher than single pollutant concentration.

Comparative study of optimization methods for building energy consumption and daylighting performance

When striving for sustainable development, designers encounter the formidable task of integrating the advantages of daylighting into energy-efficient designs. The process of investigating a multitude of design variables can be arduous and time-intensive. However, computational simulations provide indispensable insights into forecasting performance. A considerable number of these design challenges do not have explicit mathematical solutions; thus, black-box optimization techniques are favored for the purpose of determining the most effective design solutions. Popular meta-heuristic optimization algorithms, including the Genetic Algorithm (GA), Particle Swarm Optimization (PSO), Whale Optimization Algorithm (WOA), Sine Cosine Algorithm (SCA), Grey Wolf Optimizer (GWO), Ant Lion Optimizer (ALO), and Multi-Verse Optimizer (MVO), are described and thoroughly analyzed in this article. In addition, the present study run empirical studies to compare these optimization algorithms by applying them to tackle five distinct daylighting and energy optimization problems associated with enhancing building performance. In the test cases, the present study results demonstrate that the PSO and MVO algorithms exhibit better performance compared to the GA and WOA algorithms. The selection of the most suitable algorithm is contingent upon the distinctive attributes of the problem, and the intricacy of the optimization landscape.

Improved exponential smoothing grey-holt models for electricity price forecasting using whale optimization

This study introduces a ground-breaking approach, the Whale Optimization Algorithm (WOA)-based multivariate exponential smoothing Grey-Holt (GMHES) model, designed for electricity price forecasting. Key features of the proposed WOA-GMHES(1,N) model include leveraging historical data to comprehend the underlying trends in electricity prices and utilizing the WOA algorithm for adaptive optimization of model parameters to capture evolving market dynamics. Evaluating the model on authentic high- and low-voltage electricity price data from Cameroon demonstrates its superiority over competing models. The WOA-GMHES(1,N) model achieves remarkable performance with RMSE and SMAPE scores of 12.63 and 0.01 %, respectively, showcasing its accuracy and reliability. Notably, the model proves to be computationally efficient, generating forecasts in <1.3 s. Three key aspects of customization distinguish this novel approach:•The WOA algorithm dynamically adjusts model parameters based on evolving electricity market dynamics.•The model employs a sophisticated GMHES approach, considering multiple factors for a comprehensive understanding of price trends.•The WOA-GMHES(1,N) model stands out for its computational efficiency, providing rapid and precise forecasts, making it a valuable tool for time-sensitive decision-making in the energy sector.

Resource scheduling optimization for industrial operating system using deep reinforcement learning and WOA algorithm

Industrial operating systems (IOS) are essential for supporting smart manufacturing, particularly in managing and utilizing heterogeneous production resources through resource instantiation scheduling (RIS) technique. However, RIS faces the challenge of efficiently selecting optimal resource service compositions from numerous options with varying quality of service. To boost the solving of the RIS problem and improve the quality of the solution, this paper proposes a novel hybrid algorithm, named DWOA, based on the whale optimization algorithm (WOA) and deep reinforcement learning (DRL). It first incorporates the DRL algorithm to learn experience from the historical data regarding exploration and exploitation in the WOA search process and train an optimal behavior decision model. Subsequently, utilizing the trained model, the DWOA can effectively guide the search agent in achieving a better balance between global exploration and local exploitation, thereby enhancing its convergence speed and solution quality. The effectiveness and efficiency of the DWOA approach are evaluated by the CEC2017 benchmark functions and RIS problems with various scales, compared with 11 state-of-the-art methods. The experimental results indicate that our method converges faster and produces better solutions for RIS problems.

Enhancing residential energy access with optimized stand-alone hybrid solar-diesel-battery systems in Buea, Cameroon

This study examined the optimal size of an autonomous hybrid renewable energy system (HRES) for a residential application in Buea, located in the southwest region of Cameroon. Two hybrid systems, PV-Battery and PV-Battery-Diesel, have been evaluated in order to determine which was the better option. The goal of this research was to propose a dependable, low-cost power source as an alternative to the unreliable and highly unstable electricity grid in Buea. The decision criterion for the proposed HRES was the cost of energy (COE), while the system’s dependability constraint was the loss of power supply probability (LPSP). The crayfish optimization algorithm (COA) was used to optimize the component sizes of the proposed HRES, and the results were contrasted to those obtained from the whale optimization algorithm (WOA), sine cosine algorithm (SCA), and grasshopper optimization algorithm (GOA). The MATLAB software was used to model the components, criteria, and constraints of this single-objective optimization problem. The results obtained after simulation for LPSP of less than 1% showed that the COA algorithm outperformed the other three techniques, regardless of the configuration. Indeed, the COE obtained using the COA algorithm was 0.06%, 0.12%, and 1% lower than the COE provided by the WOA, SCA, and GOA algorithms, respectively, for the PV-Battery configuration. Likewise, for the PV-Battery-Diesel configuration, the COE obtained using the COA algorithm was 0.065%, 0.13%, and 0.39% lower than the COE provided by the WOA, SCA, and GOA algorithms, respectively. A comparative analysis of the outcomes obtained for the two configurations indicated that the PV-Battery-Diesel configuration exhibited a COE that was 4.32% lower in comparison to the PV-Battery configuration. Finally, the impact of the LPSP reduction on the COE was assessed in the PV-Battery-Diesel configuration. The decrease in LPSP resulted in an increase in COE owing to the nominal capacity of the diesel generator.

A whale optimization algorithm-based multivariate exponential smoothing grey-holt model for electricity price forecasting

Accurately forecasting electricity prices is essential for a variety of stakeholders in the energy sector, including market investors, policymakers, and consumers. However, existing forecasting techniques are often limited by complex parametric estimates and strict restrictions on input variables. This paper proposes a Whale Optimization Algorithm (WOA)-based multivariate exponential smoothing Grey-Holt (GMHES) model for electricity price forecasting. The proposed WOA-GMHES(1,N) model uses historical data to learn the underlying trends and patterns of electricity prices. The WOA algorithm is used to optimize the model parameters, which are adaptively adjusted to reflect the changing dynamics of the electricity market. The proposed model is evaluated on real high- and low-voltage electricity price data from Cameroon. The results show that the novel WOA-GMHES(1,N) model outperforms competing models, achieving RMSE and SMAPE scores of 0.1359 and 0.61%, respectively. This novel model is also computationally efficient, requiring less than 1.3 s to generate a forecast. The proposed WOA-GMHES(1,N) model is a promising novel approach for electricity price forecasting. The model is accurate, efficient, and flexible, making it a valuable tool for a variety of stakeholders in the energy sector.

Internet of Things Based-Early Diagnosis System of Diabetes Using Optimized Neural Network

One of the most common diseases today is diabetes. This disease has an initial phase, if it is diagnosed on time, it helps to control and treat this disease to a great extent. But early diagnosis of this disease is very challenging due to the difficulty of repeated tests, especially in some geographical areas. Therefore, in smart health systems, a series of information is frequently taken from users by mobile phone, and by analyzing this information using artificial intelligence and machine learning algorithms and with the help of the Internet of Things, the doctor can diagnose diabetes in the first steps remotely. In this paper, we used a database that has 16 features related to early diabetes. Our proposed method for early diagnosis of diabetes is based on feature ranking along with MLP neural network optimized with whale optimization algorithm (WOA). also, the NCA algorithm is used to rank the features, and the WOA algorithm is used to optimize the parameters of the MLP neural network. Finally, based on the simulation results of the proposed method, we achieved 98.1% accuracy in the early diagnosis of diabetes.

Efficient knowledge model for whale optimization algorithm to solve large-scale problems

In the process of digital transformation and development in various industries, there are more and more large-scale optimization problems. Currently, swarm intelligence optimization algorithms are the best method to solve such problems. However, previous experimental research has found that there is still room for improvement in the performance of using existing swarm intelligence optimization algorithms to solve such problems. To obtain the high-precision optimal value of whale optimization algorithm (WOA) for solving large-scale optimization problems, the optimization problem knowledge model is studied to guide the iterative process of WOA algorithm, and a novel whale optimization algorithm based on knowledge model guidance (KMGWOA) is proposed. First, a population update strategy based on multiple elite individuals is proposed to reduce the impact of the local optimal values, and the knowledge model to guide population update is constructed by combining the proposed population update strategy with the population update strategy based on global optimal individual. Second, a collaborative reverse learning knowledge model with multiple elite and poor individuals in the solution space is proposed to prevent long-term non-ideal region search. The above two knowledge models guide the iterative process of WOA algorithm in solving large-scale optimization problems. The performance of the KMGWOA algorithm guided by the proposed knowledge models is tested through the well-known classical test functions. The results demonstrate that the proposed KMGWOA algorithm not only has good search ability for the theoretical optimal value, but also achieves higher accuracy in obtaining the optimal value when it is difficult to obtain the theoretical optimal value. Moreover, KMGWOA algorithm has fast convergence speed and high effective iteration percentage.

Optimization of Electrical Discharge Machining Process by Metaheuristic Algorithms

Because of its versatility and ability to work with difficult materials, Electrical Discharge Machining (EDM) has become an essential tool in many different industries. It can produce precise shapes and intricate details. EDM has transformed fabrication processes in a variety of industries, including aerospace and electronics, medical implants and surgical instruments, and the shaping of small components. Its capacity to machine undercuts and deep cavities with little material removal makes it ideal for producing complex geometries that would be challenging or impossible to accomplish with conventional machining techniques. Several attempts have been carried out to solve the optimization problem involved in the EDM process. This paper emphasizes optimizing the EDM process using three metaheuristic algorithms: Glowworm Swarm Optimization (GSO), Grey Wolf Optimizer (GWO), and Whale Optimization Algorithm (WOA). The study's outcome showed that the GWO algorithm outperformed the GSO and WOA algorithms in solving the EDM optimization problem and achieved the minimum surface roughness value of 1.7593µm.

An Interval Type-2 Fuzzy Logic Approach for Dynamic Parameter Adaptation in a Whale Optimization Algorithm Applied to Mathematical Functions

In this paper, an improved whale optimization algorithm (WOA) based on the utilization of an interval type-2 fuzzy logic system (IT2FLS) is presented. The main idea is to present a proposal for adjusting the values of the r→1 and r→2 parameters in the WOA using an IT2FLS to achieve excellent results in the execution of the WOA. The original WOA has already proven itself as an algorithm with excellent results; therefore, a wide variety of improvements have been made to it. Herein, the main purpose is to provide a hybridization of the WOA algorithm employing fuzzy logic to find the appropriate values of the r→1 and r→2 parameters that can optimize the mathematical functions used in this study, thereby providing an improvement to the original WOA algorithm. The performance of the fuzzy WOA using IT2FLS (FWOA-IT2FLS) shows good results in the case study of the benchmark function optimization. An important comparative with other metaheuristics is also presented. A statistical test and the comparative with other bio-inspired algorithms, namely, the original WOA with type-1 FLS (FWOA-T1FLS) are analyzed. The performance index used is the average of the minimum errors in each proposed method.

Whale optimization approach for early heart disease prediction based on FCM using DCNN

Heart disease is the leading cause of death worldwide. It has an impact on not only the health of patients but also the economies and expenses of the countries. Numerous machine learning and data mining approaches are being developed and explored currently in order to predict various diseases. This paper aims to address the pressing global issue of heart disease by leveraging machine learning and data mining techniques. Specifically, it focuses on utilizing a Fuzzy C means (FCM) approach for attribute segmentation, employing the Whale Optimization Algorithm (WOA) for feature selection, and utilizing Deep Convolutional Neural Networks (DCNNs) for medical diagnosis and early prediction. In this study, the initial stage involves segmenting patient records' attributes using the FCM method. Subsequently, high-ranking features are selected through the WOA algorithm. These segmented features are then input into DCNNs to construct a robust medical diagnosis system and enable early-stage prediction. The DCNNs autonomously extract crucial features without human intervention, enhancing the accuracy of disease prediction. The performance evaluation of the proposed classifier is conducted using the Python platform, with the DCNN achieving an impressive accuracy level of 90.12% during testing. This indicates the DCNN's capability to accurately predict the presence or absence of cardiac disease, showcasing its potential as an effective tool in healthcare. The integration of FCM attribute segmentation, WOA feature selection, and DCNN-based prediction holds significant practical implications. It offers healthcare professionals a valuable tool for diagnosing and predicting heart disease early, potentially saving lives.

Whale optimization algorithm and internet of things for horizontal axis solar tracker-basedload optimization

&lt;p&gt;Renewable solar energy is the future of all other resources because of its reliability and availability all over the earth. Optimization of the energy consumption and utilization of internet of things (IoT) devices deployed in such systems poses significant challenges. Axis tracker panel is the scope for the next decade toincrease the performance of the existing panels. This research focuses on the development of intelligent energy optimization algorithms for IoT devices. The integration of renewable energy sources and IoT devices in solar-microgrid energy systems offers promising solutions for sustainable and efficient energy management. The proposed whale optimization algorithm (WOA) takes into account dynamic factors, including varying energy availability and fluctuating demand patterns, to optimize the overall performance. Leveraging real-time data from IoT sensors and smart meters, the algorithms balance energy generation and consumption, prioritize critical loads, and incorporate energy forecasting techniques to handle fluctuations in renewable energy production. Moreover, they integrate demand response mechanisms and dynamic pricing models to encourage flexible energy consumption patterns and minimize operational costs. The results of this study demonstrate the significant potential of the WOA algorithm in enhancing the sustainability of microgrid energy systems, paving the way for a greener and more reliable energy future.&lt;/p&gt;

An improved hybrid whale optimization algorithm for global optimization and engineering design problems

The whale optimization algorithm (WOA) is a widely used metaheuristic optimization approach with applications in various scientific and industrial domains. However, WOA has a limitation of relying solely on the best solution to guide the population in subsequent iterations, overlooking the valuable information embedded in other candidate solutions. To address this limitation, we propose a novel and improved variant called Pbest-guided differential WOA (PDWOA). PDWOA combines the strengths of WOA, particle swarm optimizer (PSO), and differential evolution (DE) algorithms to overcome these shortcomings. In this study, we conduct a comprehensive evaluation of the proposed PDWOA algorithm on both benchmark and real-world optimization problems. The benchmark tests comprise 30-dimensional functions from CEC 2014 Test Functions, while the real-world problems include pressure vessel optimal design, tension/compression spring optimal design, and welded beam optimal design. We present the simulation results, including the outcomes of non-parametric statistical tests including the Wilcoxon signed-rank test and the Friedman test, which validate the performance improvements achieved by PDWOA over other algorithms. The results of our evaluation demonstrate the superiority of PDWOA compared to recent methods, including the original WOA. These findings provide valuable insights into the effectiveness of the proposed hybrid WOA algorithm. Furthermore, we offer recommendations for future research to further enhance its performance and open new avenues for exploration in the field of optimization algorithms. The MATLAB Codes of FISA are publicly available at https://github.com/ebrahimakbary/PDWOA.

A Hybrid Particle Whale Optimization Algorithm with application to workflow scheduling in cloud–fog environment

Workflow scheduling aims to optimize task allocation and execution in cloud–fog​ computing environments. Finding an optimal algorithm for workflow scheduling poses a significant challenge due to the complexity and variability of tasks and resources involved. Although meta-heuristic algorithms have been proposed to address these challenges, they often suffer from being trapped in local optima, failing to achieve the global optimal solution. Our work used a hybrid approach “Particle Whale Optimization Algorithm” (PWOA) to overcome the above-mentioned problem by combining Particle Swarm Optimization (PSO) and Whale Optimization Algorithm (WOA).PSO’s limitation in high-dimensional search spaces, characterized by slow convergence toward the global optimum, and the population-based WOA algorithm’s limited exploration of search spaces are overcome by the proposed algorithm PWOA. The proposed algorithm PWOA overcomes the limitations of PSO and WOA.The objective of the PWOA algorithm is to minimize the Total Execution Time (TET) and Total Execution Cost (TEC) associated with dependent tasks in a cloud–fog computing environment. To evaluate the performance of the PWOA algorithm, extensive simulations are conducted using test cases (30, 50, 100 and 1000) from five different scientific workflows: Cybershake, Epigenomics, Inspiral, Montage and Sipht. These workflows encompassed varying numbers of assigned tasks. The simulation results demonstrated that the proposed PWOA algorithm outperformed the standard PSO and WOA algorithms to improve TET and TEC across all the diverse workflows. This study also provides a comparative view of the effectiveness of the PWOA algorithm in enhancing workflow scheduling performance.

Synthesis of Elliptical Antenna Array using Hybrid SSWOA Algorithm

In terms of research, the elliptical antenna arrays (EAA) synthesis is relatively novel. As it does not have to be circular in construction, this novel synthesis can maneuver the primary beam in the right direction, making it easier to realize. The amplitude and angular location of the ellipse, as well as the eccentricity of the ellipse, are all taken into account in the optimization process. The proposed hybrid algorithm is the SSWOA (Salp Swarm Whale Optimization Algorithm), which combines the Salp Swarm Optimization Algorithm (SSA) with the Whale Optimization Algorithm (WOA). The SSA algorithm serves as a guide, while the WOA algorithm serves as a helper in this method. We discover that optimization has a faster convergence time and high convergence accuracy when considering the benefits of SSA and WOA and applying them to the synthesis of antenna array layouts. If Griewank, Rosenbrock, Sphere, and Rastrigin test functions are used, it’s worth noting that the hybrid method outperforms both WOA and SSA.

Rapid Estimation of Soil Pb Concentration Based on Spectral Feature Screening and Multi-Strategy Spectral Fusion.

Traditional methods for obtaining soil heavy metal content are expensive, inefficient, and limited in monitoring range. In order to meet the needs of soil environmental quality evaluation and health status assessment, visible near-infrared spectroscopy and XRF spectroscopy for monitoring heavy metal content in soil have attracted much attention, because of their rapid, nondestructive, economical, and environmentally friendly features. The use of either of these spectra alone cannot meet the accuracy requirements of traditional measurements, while the synergistic use of the two spectra can further improve the accuracy of monitoring heavy metal lead content in soil. Therefore, this study applied various spectral transformations and preprocessing to vis-NIR and XRF spectra; used the whale optimization algorithm (WOA) and competitive adaptive re-weighted sampling (CARS) algorithms to identify feature spectra; designed a combination variable model (CVM) based on multi-layer spectral data fusion, which improved the spectral preprocessing and spectral feature screening process to increase the efficiency of spectral fusion; and established a quantitative model for soil Pb concentration using partial least squares regression (PLSR). The estimation performance of three spectral fusion strategies, CVM, outer-product analysis (OPA), and Granger-Ramanathan averaging (GRA), was discussed. The results showed that the accuracy and efficiency of the CARS algorithm in the fused spectra estimation model were superior to those of the WOA algorithm, with an average coefficient of determination (R2) value of 0.9226 and an average root mean square error (RMSE) of 0.1984. The accuracy of the estimation models established, based on the different spectral types, to predict the Pb content of the soil was ranked as follows: the CVM model > the XRF spectral model > the vis-NIR spectral model. Within the CVM fusion strategy, the estimation model based on CARS and PLSR (CARS_D1+D2) performed the best, with R2 and RMSE values of 0.9546 and 0.2035, respectively. Among the three spectral fusion strategies, CVM had the highest accuracy, OPA had the smallest errors, and GRA showed a more balanced performance. This study provides technical means for on-site rapid estimation of Pb content based on multi-source spectral fusion and lays the foundation for subsequent research on dynamic, real-time, and large-scale quantitative monitoring of soil heavy metal pollution using high-spectral remote sensing images.

A fusion algorithm based on whale and grey wolf optimization algorithm for solving real-world optimization problems

In order to better understand and analyze population-based meta-heuristic optimization algorithms, this paper proposed a new hybrid algorithm combined Lévy flight with modified Whale Optimization Algorithm (WOA) and Grey Wolf Optimizer (GWO) , which is called LMWOAGWO to discard the dross and select the essence. Firstly, the population is initialized by using the uniform distribution space combined with the pseudo-reverse learning strategy, which lays the foundation for global search. Then, modifications were made to both WOA and GWO. For WOA algorithm, random adjustment control parameters strategy and different chaotic maps are used to adjust the main parameters of WOA to avoid the algorithm falling into local optimum in the later stage. For GWO algorithm, a new optimal solution is added to the grey wolf population to increase the optimal update position of the algorithm. On this basis, the dynamic weighting strategy is introduced to improve the convergence accuracy and convergence speed of the algorithm. Subsequently, new conditions were added during the WOA exploitation phase to formulate LMWOAGWO and the greedy strategy is used to retain better iteration update locations. Finally, Lévy flight is used to improve the global search ability of the algorithm. Extensive numerical experiments were conducted using 23 standard test benchmark functions, 25 CEC2005 functions, 15 popular benchmark functions and 10 CEC2019 functions to test the performance of LMWOAGWO compared with other well-known swarm optimization algorithms. Experimental and statistical results show that the performance of LMWOAGWO algorithm is better than many state-of-the-art algorithms. Then, 22 real-world optimization problems were used to further study the effectiveness of LMWOAGWO. Winners of CEC2020 Real World Single Objective Constraint Optimization Competition, such as iLSHADEϵ algorithm, sCMAgES algorithm, COLSHADE algorithm and EnMODE algorithm are selected as four comparison algorithms in real world optimization problems. Experimental results show that the proposed LMWOAGWO has the capability to solve real-world optimization problems. Finally, the application efficiency of LMWOAGWO in solving two basic optimization problems in wireless networks is briefly introduced, and compared with the original WOA and GWO. Simulation results show that the performance of the LMWOAGWO is better than WOA and GWO.

Microwave Imaging for Half-Space Conductors Using the Whale Optimization Algorithm and the Spotted Hyena Optimizer

This research implements the whale optimization algorithm (WOA) and spotted hyena optimizer (SHO) in inverse scattering to regenerate the conductor shape concealed in the half-space. TM waves are irradiated from the other half-space to a perfect conductor with an unknown shape buried in one half-space. The scattered field measured outside the conductor surface with the boundary condition is used to reconstruct the object using the WOA and SHO algorithms. Several scenarios of reconstruction accuracy were compared for the WOA and SHO. The numerical simulations prove that the WOA has a better reconstruction capability.

Study on Denoising Method of Vibration Signal Induced by Tunnel Portal Blasting Based on WOA-VMD Algorithm

Because of the impact of the complex environment of tunnel portals, the measured blasting vibration signals in a tunnel portal contains a lot of high-frequency noise. To achieve effective noise reduction, a novel method of noise reduction for blasting vibration signals based on the whale optimization algorithm (WOA) optimized with the variational mode decomposition (VMD) algorithm was proposed. The WOA algorithm is used to optimize globally for the mode number K and penalty factor α of VMD for measured signals and to determine the optimal parameters of [K, α], and to obtain the intrinsic mode function (IMF). Multi-scale permutation entropy (MPE) was used to identify and remove noise components in IMF, and then the reserved IMF was reconstructed to achieve a denoised signal. The method is applied to the blasting vibration analysis of the Xiali tunnel of the Jixin expressway in the Henan Province. Results indicate that the novel method can acquire the optimal decomposition mode number and identify the high frequency noise. Its denoising effect is better than the conventional VMD algorithm and the complete ensemble empirical mode decomposition with adaptive ability (CEEMDAN), which verifies the self-adaptivity and effectiveness of the WOA-VMD denoising method.

Application of whale algorithm optimizer for unified power flow controller optimization with consideration of renewable energy sources uncertainty

Purpose. In this paper an allocation methodology of Flexible Alternating Current Transmission Systems (FACTS) controllers, more specifically, the Unified Power Flow Controller (UPFC) is proposed. As the penetration of Renewable Energy Sources (RESs) into the conventional electric grid increases, its effect on this location must be investigated. Research studies have shown that the uncertainty of RESs in power generation influences the reactive power of a power system network and consequently its overall transmission losses. The novelty of the proposed work consists in the improvement of voltage profile and the minimization of active power loss by considering renewable energy sources intermittency in the network via optimal location of UPFC device. The allocation strategy associates the steady-state analysis of the electrical network, with the location and adjustment of controller parameters using the Whale Optimization Algorithm (WOA) technique. Methodology. In order to determine the location of UPFC, approaches are proposed based on identification of a line which is the most sensitive and effective with respect to voltage security enhancement, congestion alleviation as well as direct optimization approach. The optimum location of UPFC in the power system is discussed in this paper using line loading index, line stability index and optimization method. The objective function is solved using the WOA algorithm and its performance is evaluated by comparison with Particle Swarm Optimization (PSO) algorithm. Results. The effectiveness of the proposed allocation methodology is verified through the analysis of simulations performed on standard IEEE 30 bus test system considering different load conditions. The obtained results demonstrate that feasible and effective solutions are obtained using the proposed approach and can be used to overcome the optimum location issue. Additionally, the results show that when UPFC device is strategically positioned in the electrical network and uncertainty of RES is considered, there is a significant influence on the overall transmission loss and voltage profile enhancements of the network.