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Mousa, M. E., M. A. Ebrahim, Magdy M. Zaky, E. M. Saied, and S. A. Kotb. "Hybrid Optimization Technique for Enhancing the Stability of Inverted Pendulum System." International Journal of Swarm Intelligence Research 12, no. 1 (January 2021): 77–97. http://dx.doi.org/10.4018/ijsir.2021010105.
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The inverted pendulum system (IPS) is considered the milestone of many robotic-based industries. In this paper, a new variant of variable structure adaptive fuzzy (VSAF) is used with new reduced linear quadratic regulator (RLQR) and feedforward gain for enhancing the stability of IPS. The optimal determining of VSAF parameters as well as Q and R matrices of RLQR are obtained by using a modified grey wolf optimizer with adaptive constants property via particle swarm optimization technique (GWO/PSO-AC). A comparison between the hybrid GWO/PSO-AC and classical GWO/PSO based on multi-objective function is provided to justify the superiority of the proposed technique. The IPS equipped with the hybrid GWO/PSO-AC-based controllers has minimum settling time, rise time, undershoot, and overshoot results for the two system outputs (cart position and pendulum angle). The system is subjected to robustness tests to ensure that the system can cope with small as well as significant disturbances.
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Nsour, Heba Al, Mohammed Alweshah, Abdelaziz I. Hammouri, Hussein Al Ofeishat, and Seyedali Mirjalili. "A Hybrid Grey Wolf Optimiser Algorithm for Solving Time Series Classification Problems." Journal of Intelligent Systems 29, no. 1 (July 31, 2018): 846–57. http://dx.doi.org/10.1515/jisys-2018-0129.
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Abstract One of the major objectives of any classification technique is to categorise the incoming input values based on their various attributes. Many techniques have been described in the literature, one of them being the probabilistic neural network (PNN). There were many comparisons made between the various published techniques depending on their precision. In this study, the researchers investigated the search capability of the grey wolf optimiser (GWO) algorithm for determining the optimised values of the PNN weights. To the best of our knowledge, we report for the first time on a GWO algorithm along with the PNN for solving the classification of time series problem. PNN was used for obtaining the primary solution, and thereby the PNN weights were adjusted using the GWO for solving the time series data and further decreasing the error rate. In this study, the main goal was to investigate the application of the GWO algorithm along with the PNN classifier for improving the classification precision and enhancing the balance between exploitation and exploration in the GWO search algorithm. The hybrid GWO-PNN algorithm was used in this study, and the results obtained were compared with the published literature. The experimental results for six benchmark time series datasets showed that this hybrid GWO-PNN outperformed the PNN algorithm for the studied datasets. It has been seen that hybrid classification techniques are more precise and reliable for solving classification problems. A comparison with other algorithms in the published literature showed that the hybrid GWO-PNN could decrease the error rate and could also generate a better result for five of the datasets studied.
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Nahak, Narayan, and Ranjan Kumar Mallick. "Efficacy of GWO Optimized PI and Lead-lag Controller for Design of UPFC based Supplementary Damping Controller." IAES International Journal of Robotics and Automation (IJRA) 6, no. 4 (December 1, 2017): 241. http://dx.doi.org/10.11591/ijra.v6i4.pp241-251.
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<p><span>On line tuning of FACTS based damping controller is a vital decisive task in power system. In this regard two things need to be addressed, one is selection of a proper controller and another one is selection of a powerful optimization technique. In this work Grey Wolf Optimizer (GWO) technique is proposed to tune parameters of PI and lead lag controller based on UPFC to damp intra plant and inter area electromechanical oscillations with single and multi machine power system. A broad comparison has been performed with eigen value analysis between optimized PI and lead lag damping controller subject to different disturbances in power system. The recently revealed GWO, standard PSO and DE techniques are explicitly employed to tune UPFC based PI and lead-lag controller parameters. The system response predicts that performance of GWO is much better than PSO and DE techniques, and also lead lag controller is a better choice than PI controller pertaining to design of UPFC based damping controller.</span></p>
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Badi, Manjulata, Sheila Mahapatra, Bishwajit Dey, and Saurav Raj. "A Hybrid GWO-PSO Technique for the Solution of Reactive Power Planning Problem." International Journal of Swarm Intelligence Research 13, no. 1 (January 2022): 1–30. http://dx.doi.org/10.4018/ijsir.2022010104.
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Over the years the optimization in various areas of power system has immensely attracted the attention of power engineers and researchers. RPP problem is one of such areas. This is done by the placement of reactive power sources in the weak buses and thereafter minimizing the operating cost of the system which is directly dependent on the system transmission loss. The work proposed in this article utilizes FVSI method to detect the weak bus. GWO-PSO is proposed in the current work for providing optimal solution to RPP problem. To test the efficacy of the proposed technique, comparative analysis is then performed among the variants of PSO and hybrid GWO-PSO. The optimal solution rendered by the proposed method is compared with other heuristic algorithms. The proposed method of GWO-PSO generates a reduction of 4.25% in operating cost for IEEE 30 bus and 5.99% for New England 39 bus system. The comparison thus yields that the GWO-PSO hybrid method is superior in generating optimality, diversity and is efficient to generate solution strategies for RPP even in a practical power network.
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Mat Yasin, Zuhaila, Nur Ashida Salim, Nur Fadilah Ab Aziz, Hasmaini Mohamad, and Norfishah Ab Wahab. "Prediction of solar irradiance using grey wolf Optimizer-Least-Square support vector machine." Indonesian Journal of Electrical Engineering and Computer Science 17, no. 1 (January 1, 2020): 10. http://dx.doi.org/10.11591/ijeecs.v17.i1.pp10-17.
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<span>Prediction of solar irradiance is important for minimizing energy costs and providing high power quality in a photovoltaic (PV) system. This paper proposes a new technique for prediction of hourly-ahead solar irradiance namely Grey Wolf Optimizer- Least-Square Support Vector Machine (GWO-LSSVM). Least Squares Support Vector Machine (LSSVM) has strong ability to learn a complex nonlinear problems. In GWO-LSSVM, the parameters of LSSVM are optimized using Grey Wolf Optimizer (GWO). GWO algorithm is derived based on the hierarchy of leadership and the grey wolf hunting mechanism in nature. The main step of the grey wolf hunting mechanism are hunting, searching, encircling, and attacking the prey. The model has four input vectors: time, relative humidity, wind speed and ambient temperature. Mean Absolute Performance Error (MAPE) is used to measure the prediction performance. Comparative study also carried out using LSSVM and Particle Swarm Optimizer-Least Square Support Vector Machine (PSO-LSSVM). The results showed that GWO-LSSVM predicts more accurate than other techniques. </span>
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Sulttan, Mohammed Qasim, Salam Waley Shneen, and Jafaar Mohammed Daif Alkhasraji. "Performance enhancement of large-scale linear dynamic MIMO systems using GWO-PID controller." Bulletin of Electrical Engineering and Informatics 12, no. 5 (October 1, 2023): 2852–59. http://dx.doi.org/10.11591/eei.v12i5.4870.
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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.
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Putri, Ajeng Maharani, Zuherman Rustam, Jacub Pandelaki, Ilsya Wirasati, and Sri Hartini. "Acute sinusitis data classification using grey wolf optimization-based support vector machine." IAES International Journal of Artificial Intelligence (IJ-AI) 10, no. 2 (June 1, 2021): 438. http://dx.doi.org/10.11591/ijai.v10.i2.pp438-445.
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<span id="docs-internal-guid-ebf19048-7fff-9350-093e-7f1e8df23393"><span>Acute sinusitis is the most common form of sinusitis, and it causes swelling and inflammation within the nose. The main thing that can causes sinusitis is probably due to viruses, and also can be caused by other factors, namely bacteria, fungi, irritation, dust, and allergens. In this research, the CT scan data attributes will be used for classification and grey wolf optimization-support vector machine (GWO-SVM) will be the machine learning technique used, where the GWO technique will be used to tuned the parameters in SVM. The performance of methods was analyzed using the python programming language with different percentages of training data, which started from 10% to 90%. The GWO-SVM method proposed provides better accuracy than using SVM without GWO.</span></span>
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Roy, Provas Kumar, Moumita Pradhan, and Tandra Pal. "Multi-Objective Hydro-Thermal Scheduling Problem Using Two Novel Optimization Techniques." International Journal of Swarm Intelligence Research 12, no. 3 (July 2021): 1–36. http://dx.doi.org/10.4018/ijsir.2021070101.
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This article describes an efficient and reliable strategy for the scheduling of nonlinear multi-objective hydrothermal power systems using the grey wolf optimization (GWO) technique. Moreover, the theory of oppositional-based learning (OBL) is integrated with original GWO for further enhancing its convergence rate and solution accuracy. The constraints related to hydro and thermal plants and environmental aspects are also considered in this paper. To show its efficiency and effectiveness, the proposed GWO and OGWO algorithms are authenticated for the test system consisting of a multi-chain cascade of 4 hydro and 3 thermal units whose valve-point loading effects are also taken into account. Furthermore, statistical outcomes of the conventional heuristic approaches available in the literature are compared with the proposed GWO and OGWO approaches, and these methods give moderately better operational fuel cost and emission in less computational time.
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Yan, Fu, Jianzhong Xu, and Kumchol Yun. "Dynamically Dimensioned Search Grey Wolf Optimizer Based on Positional Interaction Information." Complexity 2019 (December 5, 2019): 1–36. http://dx.doi.org/10.1155/2019/7189653.
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The grey wolf optimizer (GWO) algorithm is a recently developed, novel, population-based optimization technique that is inspired by the hunting mechanism of grey wolves. The GWO algorithm has some distinct advantages, such as few algorithm parameters, strong global optimization ability, and ease of implementation on a computer. However, the paramount challenge is that there are some cases where the GWO is prone to stagnation in local optima. This drawback of the GWO algorithm may be attributed to an insufficiency in its position-updated equation, which disregards the positional interaction information about the three best grey wolves (i.e., the three leaders). This paper proposes an improved version of the GWO algorithm that is based on a dynamically dimensioned search, spiral walking predation technique, and positional interaction information (referred to as the DGWO). In addition, a nonlinear control parameter strategy, i.e., the control parameter that is nonlinearly increased with an increase in iterations, is designed to balance the exploration and exploitation of the GWO algorithm. The experimental results for 23 general benchmark functions and 3 well-known engineering optimization design applications validate the effectiveness and feasibility of the proposed DGWO algorithm. The comparison results for the 23 benchmark functions show that the proposed DGWO algorithm performs significantly better than the GWO and its improved variant for most benchmarks. The DGWO provides the highest solution precision, strongest robustness, and fastest convergence rate among the compared algorithms in almost all cases.
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Oliveira, Josenalde, Paulo Moura Oliveira, José Boaventura-Cunha, and Tatiana Pinho. "Evaluation of Hunting-Based Optimizers for a Quadrotor Sliding Mode Flight Controller." Robotics 9, no. 2 (April 7, 2020): 22. http://dx.doi.org/10.3390/robotics9020022.
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The design of Multi-Input Multi-Output nonlinear control systems for a quadrotor can be a difficult task. Nature inspired optimization techniques can greatly improve the design of non-linear control systems. Two recently proposed hunting-based swarm intelligence inspired techniques are the Grey Wolf Optimizer (GWO) and the Ant Lion Optimizer (ALO). This paper proposes the use of both GWO and ALO techniques to design a Sliding Mode Control (SMC) flight system for tracking improvement of altitude and attitude in a quadrotor dynamic model. SMC is a nonlinear technique which requires that its strictly coupled parameters related to continuous and discontinuous components be correctly adjusted for proper operation. This requires minimizing the tracking error while keeping the chattering effect and control signal magnitude within suitable limits. The performance achieved with both GWO and ALO, considering realistic disturbed flight scenarios are presented and compared to the classical Particle Swarm Optimization (PSO) algorithm. Simulated results are presented showing that GWO and ALO outperformed PSO in terms of precise tracking, for ideal and disturbed conditions. It is shown that the higher stochastic nature of these hunting-based algorithms provided more confidence in local optima avoidance, suggesting feasibility of getting a more precise tracking for practical use.
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Mohd, Razeef, Muheet Ahmed Butt, and Majid Zaman Baba. "Grey Wolf-Based Linear Regression Model for Rainfall Prediction." International Journal of Information Technologies and Systems Approach 15, no. 1 (January 2022): 1–18. http://dx.doi.org/10.4018/ijitsa.290004.
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This paper develops a rainfall prediction technique, named GWO-based Linear Regression (GWLR) model, using the linear regression model and Grey Wolf Optimizer (GWO). The linear regression model is used to predict the value of a dependent variable from an independent variable on the basis of regression coefficient. The proposed GWLR predicts rainfall based on the input time-series weather data using the proposed GWLR model, in which the regression coefficients are obtained optimally using the GWO. Thus, the rainfall detection is done on the accumulated data of India and the state, Jammu and Kashmir over the years 1901 to 2015. The effectiveness of the proposed GWLR is checked with MSE and PRD values and is evaluated to be the best when compared to other existing techniques with least MSE value as 0.005 and PRD value as 1.700%.
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Deif, Mohanad A., Hani Attar, Ayman Amer, Haitham Issa, Mohammad R. Khosravi, and Ahmed A. A. Solyman. "A New Feature Selection Method Based on Hybrid Approach for Colorectal Cancer Histology Classification." Wireless Communications and Mobile Computing 2022 (May 5, 2022): 1–14. http://dx.doi.org/10.1155/2022/7614264.
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Colorectal cancer (CRC) is one of the most common malignant cancers worldwide. To reduce cancer mortality, early diagnosis and treatment are essential in leading to a greater improvement and survival length of patients. In this paper, a hybrid feature selection technique (RF-GWO) based on random forest (RF) algorithm and gray wolf optimization (GWO) was proposed for handling high dimensional and redundant datasets for early diagnosis of colorectal cancer (CRC). Feature selection aims to properly select the minimal most relevant subset of features out of a vast amount of complex noisy data to reach high classification accuracy. Gray wolf optimization (GWO) and random forest (RF) algorithm were utilized to find the most suitable features in the histological images of the human colorectal cancer dataset. Then, based on the best-selected features, the artificial neural networks (ANNs) classifier was applied to classify multiclass texture analysis in colorectal cancer. A comparison between the GWO and another optimizer technique particle swarm optimization (PSO) was also conducted to determine which technique is the most successful in the enhancement of the RF algorithm. Furthermore, it is crucial to select an optimizer technique having the capability of removing redundant features and attaining the optimal feature subset and therefore achieving high CRC classification performance in terms of accuracy, precision, and sensitivity rates. The Heidelberg University Medical Center Pathology archive was used for performance check of the proposed method which was found to outperform benchmark approaches. The results revealed that the proposed feature selection method (GWO-RF) has outperformed the other state of art methods where it achieved overall accuracy, precision, and sensitivity rates of 98.74%, 98.88%, and 98.63%, respectively.
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Mohammadi, Babak, Yiqing Guan, Pouya Aghelpour, Samad Emamgholizadeh, Ramiro Pillco Zolá, and Danrong Zhang. "Simulation of Titicaca Lake Water Level Fluctuations Using Hybrid Machine Learning Technique Integrated with Grey Wolf Optimizer Algorithm." Water 12, no. 11 (October 27, 2020): 3015. http://dx.doi.org/10.3390/w12113015.
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Lakes have an important role in storing water for drinking, producing hydroelectric power, and environmental, agricultural, and industrial uses. In order to optimize the use of lakes, precise prediction of the lake water level (LWL) is a main issue in water resources management. Due to the existence of nonlinear relations, uncertainty, and characteristics of the time series variables, the exact prediction of the lake water level is difficult. In this study the hybrid support vector regression (SVR) and the grey wolf algorithm (GWO) are used to predict lake water level fluctuations. Also, three types of data preprocessing methods, namely Principal component analysis, Random forest, and Relief algorithm were used for finding the best input variables for prediction LWL by the SVR and SVR-GWO models. Before the LWL simulation on monthly time step using the hybrid model, an evolutionary approach based on different monthly lags was conducted for determining the best mask of the input variables. Results showed that based on the random forest method, the best scenario of the inputs was Xt−1, Xt−2, Xt−3, Xt−4 for the SVR-GWO model. Also, the performance of the SVR-GWO model indicated that it could simulate the LWL with acceptable accuracy (with RMSE = 0.08 m, MAE = 0.06 m, and R2 = 0.96).
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Shabeerkhan, S., and A. Padma. "A novel GWO optimized pruning technique for inexact circuit design." Microprocessors and Microsystems 73 (March 2020): 102975. http://dx.doi.org/10.1016/j.micpro.2019.102975.
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Negi, Ganga, Anuj Kumar, Sangeeta Pant, and Mangey Ram. "Optimization of Complex System Reliability using Hybrid Grey Wolf Optimizer." Decision Making: Applications in Management and Engineering 4, no. 2 (October 15, 2021): 241–56. http://dx.doi.org/10.31181/dmame210402241n.
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Reliability allocation to increase the total reliability has become a successful way to increase the efficiency of the complex industrial system designs. A lot of research in the past have tackled this problem to a great extent. This is evident from the different techniques developed so far to achieve the target. Stochastic metaheuristics like simulated annealing, Tabu search (TS), Particle Swarm Optimization (PSO), Cuckoo Search Optimization (CS), Genetic Algorithm (GA), Grey wolf optimization technique (GWO) etc. have been used in recent years. This paper proposes a framework for implementing a hybrid PSO-GWO algorithm for solving some reliability allocation and optimization problems. A comparison of the results obtained is done with the results of other well-known methods like PSO, GWO, etc. The supremacy/competitiveness of the proposed framework is demonstrated from the numerical experiments. These results with regard to the time taken for the computation and quality of solution outperform the previously obtained results by the other well-known optimization methods.
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Saravanan, R., S. Subramanian, S. SooriyaPrabha, and S. Ganesan. "Generation scheduling with large-scale integration of renewable energy sources using grey wolf optimization." International Journal of Energy Sector Management 12, no. 4 (November 5, 2018): 675–95. http://dx.doi.org/10.1108/ijesm-07-2016-0001.
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Purpose Generation scheduling (GS) is the most prominent and hard-hitting problem in the electrical power industry especially in an integrated power system. Countless techniques have been used so far to solve this GS problem for proper functioning of the units in the power system to dispatch the load economically to consumers at once. Therefore, this work aims to study for the best possible function of integrated power plants to obtain the most favourable solution to the GS problem. Design/methodology/approach An appropriate method works in a proper way and assures to give the best solution to the GS problem. The finest function of incorporated power plants should be mathematically devised as a problem and via that the aim of the GS problem to minimize the total fuel cost subject to different constraints will be achieved. In this research work, the latest meta-heuristic and swarm intelligence-based technique called grey wolf optimization (GWO) technique is used as an optimization tool that will work along with the formulated problem for correct scheduling of generating units and thus achieve the objective function. Findings The recommended GWO technique provides the best feasible solution which is optimal in its performance for different test cases in the GS problem of integrated power plant. It is further found that the obtained solutions using GWO method are better than the former reports of other traditional methods in terms of solution excellence. The GWO method is found to be unique in its performance and having superior computational efficiency. Practical implications Decision making is significant for effective operation of integrated power plants in an electrical power system. The recommended tactic implements a modern meta-heuristic procedure that is applied to diverse test systems. The method that is proposed is efficient in providing the best solutions of solving GS problems. The suggested method surpasses the early techniques by offering the most excellent feasible solutions. Thus, it is obvious that the proposed method may be the appropriate substitute to attain the optimal operation of GS problem. Social implications Renewable energy sources are discontinuous and infrequent in nature, and it is tough to predict them in general. Further, integrating renewable energy source-based plants with the conventional plant is extremely difficult to operate and maintain. Operation of integrated power system is full of challenges and complications. To handle those complications and challenges, the GWO algorithm is suggested for solving the GS problem and thus obtain the optimal solution in integrated power systems by considering the reserve requirement, load balance, equality and inequality constraints. Originality/value The proposed system should be further tested on diverse test systems to evaluate its performance in solving a GS problem and the results should be compared. Computation results reveal that the proposed GWO method is efficient in attaining best solution in GS problem. Further, its performance is effectively established by comparing the result obtained by GWO with other traditional methods.
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Sharma, Abhishek, Abhinav Sharma, Averbukh Moshe, Nikhil Raj, and Rupendra Kumar Pachauri. "An Effective Method for Parameter Estimation of Solar PV Cell Using Grey-Wolf Optimization Technique." International Journal of Mathematical, Engineering and Management Sciences 6, no. 3 (June 1, 2021): 911–31. http://dx.doi.org/10.33889/ijmems.2021.6.3.054.
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In the field of renewable energy, the extraction of parameters for solar photovoltaic (PV) cells is a widely studied area of research. Parameter extraction of solar PV cell is a highly non-linear complex optimization problem. In this research work, the authors have explored grey wolf optimization (GWO) algorithm to estimate the optimized value of the unknown parameters of a PV cell. The simulation results have been compared with five different pre-existing optimization algorithms: gravitational search algorithm (GSA), a hybrid of particle swarm optimization and gravitational search algorithm (PSOGSA), sine cosine (SCA), chicken swarm optimization (CSO) and cultural algorithm (CA). Furthermore, a comparison with the algorithms existing in the literature is also carried out. The comparative results comprehensively demonstrate that GWO outperforms the existing optimization algorithms in terms of root mean square error (RMSE) and the rate of convergence. Furthermore, the statistical results validate and indicate that GWO algorithm is better than other algorithms in terms of average accuracy and robustness. An extensive comparison of electrical performance parameters: maximum current, voltage, power, and fill factor (FF) has been carried out for both PV model.
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S., Mounika, R. Narmatha Banu, and B. Kiruthiga. "Optimal Placement Identification of Multiple DG Types Using Optimization Technique." E3S Web of Conferences 387 (2023): 01009. http://dx.doi.org/10.1051/e3sconf/202338701009.
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In this paper, a combination algorithm called GAIPSO, which combines GA and a better version of the classic particle swarm optimization process, is used. In order to calculate the data enhancement in voltage profile, this study uses the GWO algorithm. The ideal position for the proposed charging points inside the distribution system is the goal. The received comment thread solution (site & station size) is further re-optimized by PSO, improving both the functionality and outcome overall. Studies based on simulations show that the above mentioned technique outperforms GA, GWO, and PSO in respect of an improved voltage profiles as well as the quality of the solution found for the objective function. Optimum planning for the charging station’s location and size. the IEEE 33 bus system. The suggested approach takes into consideration the IEEE 33 bus service. The received thread solutions (site and station size) is further re-optimized by PSO, improving both the performance and outcome overall.
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Ouhame, Soukaina, Youssef Hadi, and Arifullah Arifullah. "A Hybrid Grey Wolf Optimizer and Artificial Bee Colony Algorithm Used for Improvement in Resource Allocation System for Cloud Technology." International Journal of Online and Biomedical Engineering (iJOE) 16, no. 14 (November 30, 2020): 4. http://dx.doi.org/10.3991/ijoe.v16i14.16623.
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<p class="0abstract">Cloud computing is the next generation of technology which provide different service with the rule of pay and gain with the help of internet. These services consist of hardware and software used in different field of life. Due the growth of user in cloud environment the number of access and share system of technology increases which causes different issue and resource allocation system is one of them. In this paper for improvement in resource allocation system in VM a hybrid algorithm used because in some situation VM become underloaded and overloaded in cloud data centre due to lack of proper load balancing technique system. Therefore a hybrid technique used for improvement in VM allocation system. The hybrid technique consist of GWO and ABC algorithm three main section of GWO technique improve first improvment occur at local search section in this section ABC algorithm local search technique used second improvement occur at fitness function along with the energy parameter. The above proposed technique used to improve four main parameter of scheduling which are energy consumption, throughput network stability and average network executation time in resource allocation system in VM for cloud computing. The proposed technique result are compare with ABC algorithm , GWO algorithm, RAA algorithm based on those result the proposed algorithm improve 1.25 % accuracy and efficiency for resource allocation system in VM for cloud computing.</p>
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Dhakhinamoorthy, Chitradevi, Sathish Kumar Mani, Sandeep Kumar Mathivanan, Senthilkumar Mohan, Prabhu Jayagopal, Saurav Mallik, and Hong Qin. "Hybrid Whale and Gray Wolf Deep Learning Optimization Algorithm for Prediction of Alzheimer’s Disease." Mathematics 11, no. 5 (February 24, 2023): 1136. http://dx.doi.org/10.3390/math11051136.
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In recent years, finding the optimal solution for image segmentation has become more important in many applications. The whale optimization algorithm (WOA) is a metaheuristic optimization technique that has the advantage of achieving the global optimal solution while also being simple to implement and solving many real-time problems. If the complexity of the problem increases, the WOA may stick to local optima rather than global optima. This could be an issue in obtaining a better optimal solution. For this reason, this paper recommends a hybrid algorithm that is based on a mixture of the WOA and gray wolf optimization (GWO) for segmenting the brain sub regions, such as the gray matter (GM), white matter (WM), ventricle, corpus callosum (CC), and hippocampus (HC). This hybrid mixture consists of two steps, i.e., the WOA and GWO. The proposed method helps in diagnosing Alzheimer’s disease (AD) by segmenting the brain sub regions (SRs) by using a hybrid of the WOA and GWO (H-WOA-GWO, which is represented as HWGO). The segmented region was validated with different measures, and it shows better accuracy results of 92%. Following segmentation, the deep learning classifier was utilized to categorize normal and AD images. The combination of WOA and GWO yields an accuracy of 90%. As a result, it was discovered that the suggested method is a highly successful technique for identifying the ideal solution, and it is paired with a deep learning algorithm for classification.
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Ouhame, Soukaina, and Youssef Hadi. "Enhancement in resource allocation system for cloud environment using modified grey wolf technique." Indonesian Journal of Electrical Engineering and Computer Science 20, no. 3 (December 1, 2020): 1530. http://dx.doi.org/10.11591/ijeecs.v20.i3.pp1530-1537.
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<p class="normal">Cloud computing is an advanced technology which provides services with the help of internet. These services work under the rule of pay and gain. The services consist of hardware and software used in different fields of life. Due to growth of cloud computing the number of users are increased and their demand for better services also increased with the passage of time. Cloud computing faces different issues. One of them is resource scheduling. In this paper a new technique is used for improvement of scheduling in cloud computing. The improvement took place in GWO algorithm. Two main sections of this algorithm are modified, which are local search section and fitness function value. The above proposed technique is used to improve three main parameters of scheduling that are energy consumption, throughput and average network executation time in VM for cloud computing. The techniques results are compared with ABC algorithm and GWO algorithm. The results show that proposed algorithm improves in the three main parameters of scheduling for cloud computing.</p>
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Aziz, Ghada Adel, Salam Waley Shneen, Fatin Nabeel Abdullah, and Dina Harith Shaker. "Advanced optimal GWO-PID controller for DC motor." International Journal of Advances in Applied Sciences 11, no. 3 (September 1, 2022): 263. http://dx.doi.org/10.11591/ijaas.v11.i3.pp263-276.
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<p><span>The current work aims to use traditional control algorithms and advanced optimization algorithms that was chosen for its ease of control and the possibility of using it in many industrial applications. By setting the appropriate specifications for the simulation model and after conducting the planned tests that simulate different applications of the motor’s work within electrical systems, the results proved to obtain good performance of the motor’s work, better response, high accuracy, in addition to the speed. The goal is to design and tune a <a name="_Hlk112847502"></a>proportional–integral–derivative (PID) controller by <a name="_Hlk112847139"></a>grey wolf optimization (GWO) using T.F for a direct current (DC) motor. To adjust the parameters of the traditional controllers using the optimum advanced, an appropriate mechanism and technology from the advanced optimization techniques were chosen, as the gray wolf technology algorithm was chosen as an optimization technique and integral time absolute error (ITAE) to adjust the parameters of the traditional PID controller.</span></p>
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Izzaqi, Fahmi Ahyar, Novie Ayub Windarko, and Ony Asrarul Qudsi. "Minimization of total harmonic distortion in neutral point clamped multilevel inverter using grey wolf optimizer." International Journal of Power Electronics and Drive Systems (IJPEDS) 13, no. 3 (September 1, 2022): 1486. http://dx.doi.org/10.11591/ijpeds.v13.i3.pp1486-1497.
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<span lang="EN-US">The inverter has been attracting researchers for their application in renewable energy. So far, multilevel inverter is considered as low distortion class, which produces multilevel output voltage imitating a pure sine waveform. However, the needs for free distortion of output voltage have been motivating to improve multilevel pulse width modulation PWM generation method. In this paper, the modified PWM technique is proposed to reduce the voltage total harmonics distortion (THD) of multilevel inverter. This modulation technique is then applied to control a single-phase threelevel neutral point clamped multilevel inverter (NPC-MLI). Grey wolf optimizer (GWO) algorithm is utilized to generate optimal amplitude and phase shift of modified reference signal. The GWO algorithm is then compared with other optimization algorithms such as differential evolution (DE), human psychology optimization (HPO), and particle swarm optimization (PSO) to evaluate their performance in harmonic minimization. The performance of the proposed work is validated through simulation and experimentation on a prototype. The results show that the modified PWM technique optimized with GWO can reduce THD on NPC-MLI output voltage.</span>
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R, Ramamoorthi, and Balamurugan R. "Solving Economic Load Dispatch Problem Using Grey Wolf Optimization Algorithm." International Journal for Research in Applied Science and Engineering Technology 11, no. 5 (May 31, 2023): 2556–62. http://dx.doi.org/10.22214/ijraset.2023.52161.
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Abstract: This article presents a new evolutionary optimization approach named grey wolf optimization (GWO), which is based on the behavior of grey wolves, for the optimal operating strategy of economic load dispatch (ELD). Nonlinear characteristics of generators like ramp rate limits, valve point discontinuities and prohibited operating zones are considered in the problem. GWO method does not require any information about the gradient of the objective function, while searching for an optimum solution. The GWO algorithm concept appears to be a robust and reliable optimization algorithm is applied to the nonlinear ELD problems. The proposed algorithm is implemented and tested on two test systems having 40 Thermal generators. The results confirm the potential and effectiveness of the proposed algorithm compared to various other methods available in the literature. The outcome is very encouraging and proves that the GWO is a very effective optimization technique for solving various ELD problems.
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Ghith, Ehab Saif, and Farid Abdel Aziz Tolba. "Real-time implementation of an enhanced proportional-integral-derivative controller based on sparrow search algorithm for micro-robotics system." IAES International Journal of Artificial Intelligence (IJ-AI) 11, no. 4 (December 1, 2022): 1395. http://dx.doi.org/10.11591/ijai.v11.i4.pp1395-1404.
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This paper presents a new approach to control the position of the microrobotics system with a proportional-integral-derivative (PID) controller. By using sparrow search algorithm (SSA), the optimal PID controller indicators were obtained by applying a new objective function namely, integral square time multiplied square error (ISTES). The effeciency of the proposed SSAbased controller was verified by comparisons made with grey wolf optimization (GWO) algorithm-based controllers in terms of time. Each control technique will be applied to the identified model using MATLAB Simulink and the experimental test facility was conducted using LabVIEW software. The simulation and experimental results show that the performance of SSA-PID controller based on ISTES cost function achieves the best performance among various techniques. Moreover, the SSA technique had the highest performance compared to GWO technique based on rising and setting time and many other performance measurements. Thus, it is recommended to apply SSA for tuning the parameters of PID as it can enhance its performance in micro-robotic systems. It was found that the amount of error is reduced by 50% using SSA than other former experiments.
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K. Faraj, Blqees, and Nazar K. ء. Hussein. "Gray Wolf Optimization and Least Square Estimatation As A New Learning Algorithm For Interval Type-II ANFIS." Tikrit Journal of Pure Science 24, no. 1 (June 23, 2019): 107. http://dx.doi.org/10.25130/j.v24i1.832.
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Gray Wolfe Optimization (GWO) is one of the meta-heuristic method and it is a popular technique in Many engineering and economic applications. GWO and Least Square Estimatation (LSE) are used to optimize the antecedents and consequents parameters of interval type-2 ANFIS respectively. We are checking the new learning algorithm by using the interval type-2 ANFIS in prediction of Mackey-Glass time series and the results were very encouraging compared to other algorithms.
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Blqees K. Faraj and Nazar K. Hussein. "Gray Wolf Optimization and Least Square Estimatation As A New Learning Algorithm For Interval Type-II ANFIS." Tikrit Journal of Pure Science 24, no. 1 (March 18, 2019): 107–11. http://dx.doi.org/10.25130/tjps.v24i1.339.
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Gray Wolfe Optimization (GWO) is one of the meta-heuristic method and it is a popular technique in Many engineering and economic applications. GWO and Least Square Estimatation (LSE) are used to optimize the antecedents and consequents parameters of interval type-2 ANFIS respectively. We are checking the new learning algorithm by using the interval type-2 ANFIS in prediction of Mackey-Glass time series and the results were very encouraging compared to other algorithms.
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Nagarajan, Vengatajalapathi, Ayyappan Solaiyappan, Siva Kumar Mahalingam, Lenin Nagarajan, Sachin Salunkhe, Emad Abouel Nasr, Ragavanantham Shanmugam, and Hussein Mohammed Abdel Moneam Hussein. "Meta-Heuristic Technique-Based Parametric Optimization for Electrochemical Machining of Monel 400 Alloys to Investigate the Material Removal Rate and the Sludge." Applied Sciences 12, no. 6 (March 9, 2022): 2793. http://dx.doi.org/10.3390/app12062793.
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Electrochemical machining (ECM) is a preferred advanced machining process for machining Monel 400 alloys. During the machining, the toxic nickel hydroxides in the sludge are formed. Therefore, it becomes necessary to determine the optimum ECM process parameters that minimize the nickel presence (NP) emission in the sludge while maximizing the material removal rate (MRR). In this investigation, the predominant ECM process parameters, such as the applied voltage, flow rate, and electrolyte concentration, were controlled to study their effect on the performance measures (i.e., MRR and NP). A meta-heuristic algorithm, the grey wolf optimizer (GWO), was used for the multi-objective optimization of the process parameters for ECM, and its results were compared with the moth-flame optimization (MFO) and particle swarm optimization (PSO) algorithms. It was observed from the surface, main, and interaction plots of this experimentation that all the process variables influenced the objectives significantly. The TOPSIS algorithm was employed to convert multiple objectives into a single objective used in meta-heuristic algorithms. In the convergence plot for the MRR model, the PSO algorithm converged very quickly in 10 iterations, while GWO and MFO took 14 and 64 iterations, respectively. In the case of the NP model, the PSO tool took only 6 iterations to converge, whereas MFO and GWO took 48 and 88 iterations, respectively. However, both MFO and GWO obtained the same solutions of EC = 132.014 g/L, V = 2406 V, and FR = 2.8455 L/min with the best conflicting performances (i.e., MRR = 0.242 g/min and NP = 57.7202 PPM). Hence, it is confirmed that these metaheuristic algorithms of MFO and GWO are more suitable for finding the optimum process parameters for machining Monel 400 alloys with ECM. This work explores a greater scope for the ECM process with better machining performance.
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Das, Debashish, Ali Safa Sadiq, Seyedali Mirjalili, and A. Noraziah. "Hybrid Clustering-GWO-NARX neural network technique in predicting stock price." Journal of Physics: Conference Series 892 (September 2017): 012018. http://dx.doi.org/10.1088/1742-6596/892/1/012018.
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Armaghani, Danial Jahed, Mohammadreza Koopialipoor, Maziyar Bahri, Mahdi Hasanipanah, and M. M. Tahir. "A SVR-GWO technique to minimize flyrock distance resulting from blasting." Bulletin of Engineering Geology and the Environment 79, no. 8 (May 14, 2020): 4369–85. http://dx.doi.org/10.1007/s10064-020-01834-7.
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Shauqee, Mohamad Norherman, Parvathy Rajendran, and Nurulasikin Mohd Suhadis. "Proportional Double Derivative Linear Quadratic Regulator Controller Using Improvised Grey Wolf Optimization Technique to Control Quadcopter." Applied Sciences 11, no. 6 (March 17, 2021): 2699. http://dx.doi.org/10.3390/app11062699.
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A hybrid proportional double derivative and linear quadratic regulator (PD2-LQR) controller is designed for altitude (z) and attitude (roll, pitch, and yaw) control of a quadrotor vehicle. The derivation of a mathematical model of the quadrotor is formulated based on the Newton–Euler approach. An appropriate controller’s parameter must be obtained to obtain a superior control performance. Therefore, we exploit the advantages of the nature-inspired optimization algorithm called Grey Wolf Optimizer (GWO) to search for those optimal values. Hence, an improved version of GWO called IGWO is proposed and used instead of the original one. A comparative study with the conventional controllers, namely proportional derivative (PD), proportional integral derivative (PID), linear quadratic regulator (LQR), proportional linear quadratic regulator (P-LQR), proportional derivative and linear quadratic regulator (PD-LQR), PD2-LQR, and original GWO-based PD2-LQR, was undertaken to show the effectiveness of the proposed approach. An investigation of 20 different quadcopter models using the proposed hybrid controller is presented. Simulation results prove that the IGWO-based PD2-LQR controller can better track the desired reference input with shorter rise time and settling time, lower percentage overshoot, and minimal steady-state error and root mean square error (RMSE).
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Tinnathi, Sreenivasu, and G. Sudhavani. "Copy-Move Forgery Detection Using Superpixel Clustering Algorithm and Enhanced GWO Based AlexNet Model." Cybernetics and Information Technologies 22, no. 4 (November 1, 2022): 91–110. http://dx.doi.org/10.2478/cait-2022-0041.
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Abstract In this work a model is introduced to improve forgery detection on the basis of superpixel clustering algorithm and enhanced Grey Wolf Optimizer (GWO) based AlexNet. After collecting the images from MICC-F600, MICC-F2000 and GRIP datasets, patch segmentation is accomplished using a superpixel clustering algorithm. Then, feature extraction is performed on the segmented images to extract deep learning features using an enhanced GWO based AlexNet model for better forgery detection. In the enhanced GWO technique, multi-objective functions are used for selecting the optimal hyper-parameters of AlexNet. Based on the obtained features, the adaptive matching algorithm is used for locating the forged regions in the tampered images. Simulation outcome showed that the proposed model is effective under the conditions: salt & pepper noise, Gaussian noise, rotation, blurring and enhancement. The enhanced GWO based AlexNet model attained maximum detection accuracy of 99.66%, 99.75%, and 98.48% on MICC-F600, MICC-F2000 and GRIP datasets.
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Keserwani, Pankaj Kumar, Mahesh Chandra Govil, E. S. Pilli, and Prajjval Govil. "An Optimal NIDS for VCN Using Feature Selection and Deep Learning Technique." International Journal of Digital Crime and Forensics 13, no. 6 (November 2021): 1–25. http://dx.doi.org/10.4018/ijdcf.20211101.oa10.
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In this modern era, due to demand for cloud environments in business, the size, complexity, and chance of attacks to virtual cloud network (VCN) are increased. The protection of VCN is required to maintain the faith of the cloud users. Intrusion detection is essential to secure any network. The existing approaches that use the conventional neural network cannot utilize all information for identifying the intrusions. In this paper, the anomaly-based NIDS for VCN is proposed. For feature selection, grey wolf optimization (GWO) is hybridized with a bald eagle search (BES) algorithm. For classification, a deep learning approach - deep sparse auto-encoder (DSAE) is employed. In this way, this paper proposes a NIDS model for VCN named - GWO-DES-DSAE. The proposed system is simulated in the python programming environment. The proposed NIDS model's performance is compared with other recent approaches for both binary and multi-class classification on the considered datasets - NSL-KDD, UNSW-NB15, and CICIDS 2017 and found better than other methods.
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V.S.Acharyulu, B., P. K.Hota, and Banaja Mohanty. "Automatic Generation Control of Multi-Area Solar-Thermal Power System Using Fruit-Fly Optimization Algorithm." International Journal of Engineering & Technology 7, no. 4.5 (September 22, 2018): 56. http://dx.doi.org/10.14419/ijet.v7i4.5.20009.
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In this paper, fruit-fly optimization algorithm (FOA) is applied to automatic generation control (AGC) of multi-area power systems. In the proposed three-area system, reheat thermal systems are considered in all areas incorporating solar thermal power plant (STPP) in one of the areas. The optimum gain of proportional-integral-derivative (PID) controller is optimized applying FOA technique. The strength of FOA is established by comparing the results with well-established Grey Wolf optimizer (GWO) technique for the same interconnected power system. The performances of the system with FOA technique are found to be better than GWO algorithm for both with and without incorporating STPP in area-1. Further, from the sensitivity analysis, it is evident that the PID controller gains obtained by FOA technique under normal conditions are found to be better even for large changes in slip and system load conditions.
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Almasoudi, Fahad M., Gaber Magdy, Abualkasim Bakeer, Khaled Saleem S. Alatawi, and Mahmoud Rihan. "A New Load Frequency Control Technique for Hybrid Maritime Microgrids: Sophisticated Structure of Fractional-Order PIDA Controller." Fractal and Fractional 7, no. 6 (May 27, 2023): 435. http://dx.doi.org/10.3390/fractalfract7060435.
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This paper proposes an efficient load frequency control (LFC) technique based on a fractional-order proportional–integral–derivative–accelerator with a low-pass filter compensator (FOPIDA-LPF) controller, which can also be accurately referred to as the PIλDND2N2 controller. A trustworthy metaheuristic optimization algorithm, known as the gray wolf optimizer (GWO), is used to fine-tune the suggested PIλDND2N2 controller parameters. Moreover, the proposed PIλDND2N2 controller is designed for the LFC of a self-contained hybrid maritime microgrid system (HMμGS) containing solid oxide fuel cell energy units, a marine biodiesel generator, renewable energy sources (RESs), non-sensitive loads, and sensitive loads. The proposed controller enables the power system to deal with random variations in load and intermittent renewable energy sources. Comparisons with various controllers used in the literature demonstrate the excellence of the proposed PIλDND2N2 controller. Additionally, the proficiency of GWO optimization is checked against other powerful optimization techniques that have been extensively researched: particle swarm optimization and ant lion optimization. Finally, the simulation results performed by the MATLAB software prove the effectiveness and reliability of the suggested PIλDND2N2 controller built on the GWO under several contingencies of different load perturbations and random generation of RESs. The proposed controller can maintain stability within the system, while also greatly decreasing overshooting and minimizing the system’s settling time and rise time.
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Priyanka, Priyanka, Narender Kumar, and Dharmender Kumar. "Enhanced Grey Wolf Optimization based Hyper-parameter optimized Convolution Neural Network for Kidney Image Classification." International Journal on Recent and Innovation Trends in Computing and Communication 11, no. 5 (May 17, 2023): 363–74. http://dx.doi.org/10.17762/ijritcc.v11i5.6624.
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Over the last few years, Convolution Neural Networks (CNN) have shown dominant performance over real world applications due to their ability to find good solutions and deal with image data. However their performance is highly dependent on the network architecture and methods for optimizing their hyper parameters especially number and size of filters. Designing a good CNN architecture requires human expertise and domain knowledge. So, it is difficult in CNN to find sufficient number and size of filters for classification problems. The standard GWO algorithm used for any optimization purpose suffers from some issues such as slow convergence speed, trapping in local minima and unable to maintain balance between exploration and exploitation. In order to have proper balance between these phases, two modifications in GWO are introduced in this paper. A technique for finding optimum CNN architecture using methods based on Enhanced Grey Wolf Optimization (E-GWO) is proposed. The paper presents optimization of hyper parameters (numbers and size of filters in convolution layer) of CNN using E-GWO to improve the performance of the model. Kidney ultrasound images dataset collected from ultrasound centre is used to evaluate the performance of the proposed algorithm. Experimental results showed that optimization of CNN with E-GWO outperformed CNN optimized with traditional GA, PSO and GWO and conventional CNN yielding 97.01% accuracy. At last, the obtained results are statistically validated using t-test.
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Zhang, Sen, and Yongquan Zhou. "Grey Wolf Optimizer Based on Powell Local Optimization Method for Clustering Analysis." Discrete Dynamics in Nature and Society 2015 (2015): 1–17. http://dx.doi.org/10.1155/2015/481360.
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One heuristic evolutionary algorithm recently proposed is the grey wolf optimizer (GWO), inspired by the leadership hierarchy and hunting mechanism of grey wolves in nature. This paper presents an extended GWO algorithm based on Powell local optimization method, and we call it PGWO. PGWO algorithm significantly improves the original GWO in solving complex optimization problems. Clustering is a popular data analysis and data mining technique. Hence, the PGWO could be applied in solving clustering problems. In this study, first the PGWO algorithm is tested on seven benchmark functions. Second, the PGWO algorithm is used for data clustering on nine data sets. Compared to other state-of-the-art evolutionary algorithms, the results of benchmark and data clustering demonstrate the superior performance of PGWO algorithm.
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Abderrahim, Zemmit, Herraguemi Kamel Eddine, and Messalti Sabir. "A New Improved Variable Step Size MPPT Method for Photovoltaic Systems Using Grey Wolf and Whale Optimization Technique Based PID Controller." Journal Européen des Systèmes Automatisés 54, no. 1 (February 28, 2021): 175–85. http://dx.doi.org/10.18280/jesa.540120.
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In this work, we have developed two new intelligent maximum power point tracking (MPPT) techniques for photovoltaic (PV) solar systems. To optimize the PWM duty cycle driving the DC/DC boost converter, we have used two optimization algorithms namely the whale optimization algorithm (WOA) and grey wolf optimization (GWO) so we can tune the PID controller gains. The oscillation around the MPP and the fail accuracy under fast variable isolation are among the well-known drawbacks of conventional MPPT algorithms. To overcome these two drawbacks, we have formulated a new objective fitness function that includes WOA/GWO based accuracy, ripple, and overshoot. To provide the most relevant variable step size, this objective fitness function was optimized using the two aforementioned optimization algorithms (i.e., WOA and GWO). We have carried out several tests on Solarex MSX-150 panel and DC/DC boost converter based PV systems. In the simulation results section, we can clearly see that the two proposed algorithms perform better than the conventional ones in term of power overshoot, ripple and the response time.
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Luo, Yuan, Qiong Qin, Zhangfang Hu, and Yi Zhang. "Path Planning for Unmanned Delivery Robots Based on EWB-GWO Algorithm." Sensors 23, no. 4 (February 7, 2023): 1867. http://dx.doi.org/10.3390/s23041867.
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With the rise of robotics within various fields, there has been a significant development in the use of mobile robots. For mobile robots performing unmanned delivery tasks, autonomous robot navigation based on complex environments is particularly important. In this paper, an improved Gray Wolf Optimization (GWO)-based algorithm is proposed to realize the autonomous path planning of mobile robots in complex scenarios. First, the strategy for generating the initial wolf pack of the GWO algorithm is modified by introducing a two-dimensional Tent–Sine coupled chaotic mapping in this paper. This guarantees that the GWO algorithm generates the initial population diversity while improving the randomness between the two-dimensional state variables of the path nodes. Second, by introducing the opposition-based learning method based on the elite strategy, the adaptive nonlinear inertia weight strategy and random wandering law of the Butterfly Optimization Algorithm (BOA), this paper improves the defects of slow convergence speed, low accuracy, and imbalance between global exploration and local mining functions of the GWO algorithm in dealing with high-dimensional complex problems. In this paper, the improved algorithm is named as an EWB-GWO algorithm, where EWB is the abbreviation of three strategies. Finally, this paper enhances the rationalization of the initial population generation of the EWB-GWO algorithm based on the visual-field line detection technique of Bresenham’s line algorithm, reduces the number of iterations of the EWB-GWO algorithm, and decreases the time complexity of the algorithm in dealing with the path planning problem. The simulation results show that the EWB-GWO algorithm is very competitive among metaheuristics of the same type. It also achieves optimal path length measures and smoothness metrics in the path planning experiments.
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Guttula, Ramakrishna, and Venkateswara Rao Nandanavanam. "Patch antenna design optimization using opposition based grey wolf optimizer and map-reduce framework." Data Technologies and Applications 54, no. 1 (January 13, 2020): 103–20. http://dx.doi.org/10.1108/dta-06-2019-0084.
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Purpose Microstrip patch antenna is generally used for several communication purposes particularly in the military and civilian applications. Even though several techniques have been made numerous achievements in several fields, some systems require additional improvements to meet few challenges. Yet, they require application-specific improvement for optimally designing microstrip patch antenna. The paper aims to discuss these issues. Design/methodology/approach This paper intends to adopt an advanced meta-heuristic search algorithm called as grey wolf optimization (GWO), which is said to be inspired by the hunting behaviour of grey wolves, for the design of patch antenna parameters. The searching for the optimal design of the antenna is paced up using the opposition-based solution search. Moreover, the proposed model derives a nonlinear objective model to aid the design of the solution space of antenna parameters. After executing the simulation model, this paper compares the performance of the proposed GWO-based microstrip patch antenna with several conventional models. Findings The gain of the proposed model is 27.05 per cent better than WOAD, 2.07 per cent better than AAD, 15.80 per cent better than GAD, 17.49 per cent better than PSAD and 3.77 per cent better than GWAD model. Thus, it has proved that the proposed antenna model has attained high gain, leads to cause superior performance. Originality/value This paper presents a technique for designing the microstrip patch antenna, using the proposed GWO algorithm. This is the first work utilizes GWO-based optimization for microstrip patch antenna.
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Ahmed, Rasel, Amril Nazir, Shuhaimi Mahadzir, Mohammad Shorfuzzaman, and Jahedul Islam. "Niching Grey Wolf Optimizer for Multimodal Optimization Problems." Applied Sciences 11, no. 11 (May 24, 2021): 4795. http://dx.doi.org/10.3390/app11114795.
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Metaheuristic algorithms are widely used for optimization in both research and the industrial community for simplicity, flexibility, and robustness. However, multi-modal optimization is a difficult task, even for metaheuristic algorithms. Two important issues that need to be handled for solving multi-modal problems are (a) to categorize multiple local/global optima and (b) to uphold these optima till the ending. Besides, a robust local search ability is also a prerequisite to reach the exact global optima. Grey Wolf Optimizer (GWO) is a recently developed nature-inspired metaheuristic algorithm that requires less parameter tuning. However, the GWO suffers from premature convergence and fails to maintain the balance between exploration and exploitation for solving multi-modal problems. This study proposes a niching GWO (NGWO) that incorporates personal best features of PSO and a local search technique to address these issues. The proposed algorithm has been tested for 23 benchmark functions and three engineering cases. The NGWO outperformed all other considered algorithms in most of the test functions compared to state-of-the-art metaheuristics such as PSO, GSA, GWO, Jaya and two improved variants of GWO, and niching CSA. Statistical analysis and Friedman tests have been conducted to compare the performance of these algorithms thoroughly.
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Wang, Guanghua, Di Feng, and Wenlai Tang. "Electrical Impedance Tomography Based on Grey Wolf Optimized Radial Basis Function Neural Network." Micromachines 13, no. 7 (July 15, 2022): 1120. http://dx.doi.org/10.3390/mi13071120.
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Electrical impedance tomography (EIT) is a non-invasive, radiation-free imaging technique with a lot of promise in clinical monitoring. However, since EIT image reconstruction is a non-linear, pathological, and ill-posed issue, the quality of the reconstructed images needs constant improvement. To increase image reconstruction accuracy, a grey wolf optimized radial basis function neural network (GWO-RBFNN) is proposed in this paper. The grey wolf algorithm is used to optimize the weights in the radial base neural network, determine the mapping between the weights and the initial position of the grey wolf, and calculate the optimal position of the grey wolf to find the optimal solution for the weights, thus improving the image resolution of EIT imaging. COMSOL and MATLAB were used to numerically simulate the EIT system with 16 electrodes, producing 1700 simulation samples. The standard Landweber, RBFNN, and GWO-RBFNN approaches were used to train the sets separately. The obtained image correlation coefficient (ICC) of the test set after training with GWO-RBFNN is 0.9551. After adding 30, 40, and 50 dB of Gaussian white noise to the test set, the attained ICCs with GWO-RBFNN are 0.8966, 0.9197, and 0.9319, respectively. The findings reveal that the proposed GWO-RBFNN approach outperforms the existing methods when it comes to image reconstruction.
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Prashar, Sushil, Vikram Kumar Kamboj, and Kultaar Deep Singh. "A Cost Effective Solution to Security Constrained Unit Commitment and Dispatch Problem using Hybrid Search Algorithm." E3S Web of Conferences 184 (2020): 01071. http://dx.doi.org/10.1051/e3sconf/202018401071.
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The Security constraints unit commitment (SCUC) is a powerful scheduling technique used in power markets for daily planning. SCUC is a framework that combines two common algorithms in the electricity industry: Unit Commitment (UC) and Economic Dispatch (ED), while adding a new dimension – Security. Load demand is changing continuously due to variation of load of industrial, residential and commercial sectors. Thus, it is very important to decide which generating unit should be kept on and how much power should be dispatched so that time varying load demand can be meet on Hourly basis and there should be no scope of occurrence of loss of load hour (LOLH). In the proposed research, the recently developed hybrid meta-heuristics search algorithm i.e. GWO-RES has been applied to solve the security constrained Unit Commitment and dispatch problem of Electric power system. The efficiency of the proposed hybrid algorithm has been tested for standard IEEE-14 Bus, 30-Bus and 56-bus system and it has been experimentally found that GWO-RES performs much better than hybrid GWO-PS and GWO-RS algorithm.
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Rajakumar, R., J. Amudhavel, P. Dhavachelvan, and T. Vengattaraman. "GWO-LPWSN: Grey Wolf Optimization Algorithm for Node Localization Problem in Wireless Sensor Networks." Journal of Computer Networks and Communications 2017 (2017): 1–10. http://dx.doi.org/10.1155/2017/7348141.
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Seyedali Mirjalili et al. (2014) introduced a completely unique metaheuristic technique particularly grey wolf optimization (GWO). This algorithm mimics the social behavior of grey wolves whereas it follows the leadership hierarchy and attacking strategy. The rising issue in wireless sensor network (WSN) is localization problem. The objective of this problem is to search out the geographical position of unknown nodes with the help of anchor nodes in WSN. In this work, GWO algorithm is incorporated to spot the correct position of unknown nodes, so as to handle the node localization problem. The proposed work is implemented using MATLAB 8.2 whereas nodes are deployed in a random location within the desired network area. The parameters like computation time, percentage of localized node, and minimum localization error measures are utilized to analyse the potency of GWO rule with other variants of metaheuristics algorithms such as particle swarm optimization (PSO) and modified bat algorithm (MBA). The observed results convey that the GWO provides promising results compared to the PSO and MBA in terms of the quick convergence rate and success rate.
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Masadeh, Raja, Abdullah Alzaqebah, and Amjad Hudaib. "Grey Wolf Algorithm for Requirements Prioritization." Modern Applied Science 12, no. 2 (January 15, 2018): 54. http://dx.doi.org/10.5539/mas.v12n2p54.
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Requirement prioritization is one of the most important approach in the process of requirement engineering due to use it in order to prioritize the execution sort of requirements with taking into account the viewpoints of stakeholders. Thus, in this study, grey wolf optimization (GWO) algorithm is applied in order to prioritize the requirements of a software project. GWO imitates the hunting behavior of grey wolves in nature. Which distinct from others that it has dominant leadership hierarchy which contains four main types; alpha, beta delta and omega wolves. In this paper, a proposed algorithm is presented to prioritize the requirements into ordered list. Furthermore, it is compared and evaluated with analytical hierarchy process (AHP) technique in terms of average running time and dataset size. The findings display that the RP-GWO performs better than AHP mechanism by approximately (30%).
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Nouri, Nabil Abdelkader, Abdenacer Naouri, and Sahraoui Dhelim. "Accurate range-based distributed localization of wireless sensor nodes using grey wolf optimizer." Journal of Engineering and Exact Sciences 9, no. 4 (June 8, 2023): 15920–01. http://dx.doi.org/10.18540/jcecvl9iss4pp15920-01e.
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Various ranging techniques are frequently employed in wireless sensor networks (WSNs) to determine the distance between a node and its neighboring anchor nodes. The distance measurement, as mentioned earlier is subsequently employed to estimate the location of the node whose location is unknown. The present paper presents an Accurate Localization Scheme that utilizes Grey Wolf Optimization (GWO) and is based on the Radio Signal Strength (RSS) ranging technique. The efficiency of our technique has been proved through extensive simulations, showing a consistent improvement in localization accuracy ratios and a decrease in location errors while maintaining cost-effectiveness.
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Iqbal, Javed, Abid Mehmood, Aboubakar Mirza, and Abdul Khaliq. "Asset Allocation through Grey Wolf Optimization: A Case of KSE-30 Index." Pakistan Journal of Humanities and Social Sciences 11, no. 1 (March 31, 2023): 670–81. http://dx.doi.org/10.52131/pjhss.2023.1101.0383.
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This article is an effort to aid investors by drawing attention to select investment items. Grey Wolf Optimization (GWO), TOPSIS with Eigenvector, Market Capitalization, and the Equal Weighted Technique are the four main methods discussed in this paper. This study uses the KSE-30 Index as its sample size; however, because to a lack of data, only 26 businesses are chosen for analysis using 10 criteria. All four methods are implemented and weights are determined based on these criteria. These weights are then utilized in conjunction with MATLAB's in-built tools to construct a portfolio. Based on its ability to generate the greatest possible portfolio, GWO appears to be a powerful resource for affluent investors. Equal-weighted portfolios performed the worst, followed by the Eigenvector-TOPSIS technique, then Market Capitalization.
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Alejo-Reyes, Avelina, Erik Cuevas, Alma Rodríguez, Abraham Mendoza, and Elias Olivares-Benitez. "An Improved Grey Wolf Optimizer for a Supplier Selection and Order Quantity Allocation Problem." Mathematics 8, no. 9 (August 31, 2020): 1457. http://dx.doi.org/10.3390/math8091457.
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Supplier selection and order quantity allocation have a strong influence on a company’s profitability and the total cost of finished products. From an optimization perspective, the processes of selecting the right suppliers and allocating orders are modeled through a cost function that considers different elements, such as the price of raw materials, ordering costs, and holding costs. Obtaining the optimal solution for these models represents a complex problem due to their discontinuity, non-linearity, and high multi-modality. Under such conditions, it is not possible to use classical optimization methods. On the other hand, metaheuristic schemes have been extensively employed as alternative optimization techniques to solve difficult problems. Among the metaheuristic computation algorithms, the Grey Wolf Optimization (GWO) algorithm corresponds to a relatively new technique based on the hunting behavior of wolves. Even though GWO allows obtaining satisfying results, its limited exploration reduces its performance significantly when it faces high multi-modal and discontinuous cost functions. In this paper, a modified version of the GWO scheme is introduced to solve the complex optimization problems of supplier selection and order quantity allocation. The improved GWO method called iGWO includes weighted factors and a displacement vector to promote the exploration of the search strategy, avoiding the use of unfeasible solutions. In order to evaluate its performance, the proposed algorithm has been tested on a number of instances of a difficult problem found in the literature. The results show that the proposed algorithm not only obtains the optimal cost solutions, but also maintains a better search strategy, finding feasible solutions in all instances.
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Kumar, Narender, and Dharmender Kumar. "AN IMPROVED GREY WOLF OPTIMIZATION-BASED LEARNING OF ARTIFICIAL NEURAL NETWORK FOR MEDICAL DATA CLASSIFICATION." Journal of Information and Communication Technology 20, Number 2 (February 21, 2021): 213–48. http://dx.doi.org/10.32890/jict2021.20.2.4.
Full textAbstract:
Grey wolf optimization (GWO) is a recent and popular swarm-based metaheuristic approach. It has been used in numerous fields such as numerical optimization, engineering problems, and machine learning. The different variants of GWO have been developed in the last 5 years for solving optimization problems in diverse fields. Like other metaheuristic algorithms, GWO also suffers from local optima and slow convergence problems, resulted in degraded performance. An adequate equilibrium among exploration and exploitation is a key factor to the success of meta-heuristic algorithms especially for optimization task. In this paper, a new variant of GWO, called inertia motivated GWO (IMGWO) is proposed. The aim of IMGWO is to establish better balance between exploration and exploitation. Traditionally, artificial neural network (ANN) with backpropagation (BP) depends on initial values and in turn, attains poor convergence. The metaheuristic approaches are better alternative instead of BP. The proposed IMGWO is used to train the ANN to prove its competency in terms of prediction. The proposed IMGWO-ANN is used for medical diagnosis task. Some benchmark medical datasets including heart disease, breast cancer, hepatitis, and parkinson's diseases are used for assessing the performance of IMGWO-ANN. The performance measures are described in terms of mean squared errors (MSEs), classification accuracies, sensitivities, specificities, the area under the curve (AUC), and receiver operating characteristic (ROC) curve. It is found that IMGWO outperforms than three popular metaheuristic approaches including GWO, genetic algorithm (GA), and particle swarm optimization (PSO). Results confirmed the potency of IMGWO as a viable learning technique for an ANN.
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Zhou, Xu, Jing Liu, Huiwen Men, Shangsheng Ren, and Liwen Guo. "Study on Downhole Geomagnetic Suitability Problems Based on Improved Back Propagation Neural Network." Electronics 12, no. 11 (June 2, 2023): 2520. http://dx.doi.org/10.3390/electronics12112520.
Full textAbstract:
The analysis of geomagnetic suitability is the basis and premise of geomagnetic matching navigation and positioning. A geomagnetic suitability evaluation model using mixed sampling and an improved back propagation neural network (BPNN) based on the gray wolf optimization (GWO) algorithm by incorporating the dimension learning-based hunting (DLH) search strategy algorithm was proposed in this paper to accurately assess the geomagnetic suitability. Compared with the traditional geomagnetic suitability evaluation model, its generalization ability and accuracy were better improved. Firstly, the key indicators and matching labels used for geomagnetic suitability evaluation were analyzed, and an evaluation system was established. Then, a mixed sampling method based on the synthetic minority over-sampling technique (SMOTE) and Tomek Links was employed to extend the original dataset and construct a new dataset. Next, the dataset was divided into a training set and a test set, according to 7:3. The geomagnetic standard deviation, kurtosis coefficient, skewness coefficient, geomagnetic information entropy, geomagnetic roughness, variance of geomagnetic roughness, and correlation coefficient were used as input indicators and put into the DLH-GWO-BPNN model for model training with matching labels as output. Accuracy, recall, the ROC curve, and the AUC value were taken as evaluation indexes. Finally, PSO (Particle Swarm Optimization)-BPNN, WOA (Whale Optimization Algorithm)-BPNN, GA (Genetic Algorithm)-BPNN, and GWO-BPNN algorithms were selected as compared methods to verify the predictable ability of the DLH-GWO-BPNN. The accuracy ranking of the five models on the test set was as follows: PSO-BPNN (80.95 %) = WOA-BPNN (80.95%) < GA-BPNN (85.71%) = GWO-BPNN (85.71%) < DLH-GWO-BPNN (95.24%). The results indicate that the DLH-GWO-BPNN model can be used as a reliable method for underground geomagnetic suitability research, which can be applied to the research of geomagnetic matching navigation.