A Multi Hidden Recurrent Neural Network with a Modified Grey Wolf Optimizer

Identifying university students' weaknesses results in better learning and can function as an early warning system to enable students to improve. However, the satisfaction level of existing systems is not promising. New and dynamic hybrid systems are needed to imitate this mechanism. A hybrid system (a modified Recurrent Neural Network with an adapted Grey Wolf Optimizer) is used to forecast students' outcomes. This proposed system would improve instruction by the faculty and enhance the students' learning experiences. The results show that a modified recurrent neural network with an adapted Grey Wolf Optimizer has the best accuracy when compared with other models.Comment: 34 pages, published in PLoS ON

Niching grey wolf optimizer for multimodal optimization problems

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

Fractional Order PID Design for a Proton Exchange Membrane Fuel Cell System Using an Extended Grey Wolf Optimizer

This paper presents a comparison of optimizers for tuning a fractional-order proportional-integral-derivative (FOPID) and proportional-integral-derivative (PID) controllers, which were applied to a DC/DC boost converter. Grey wolf optimizer (GWO) and extended grey wolf optimizer (EGWO) have been chosen to achieve suitable parameters. This strategy aims to improve and optimize a proton exchange membrane fuel cell (PEMFC) output power quality through its link with the boost converter. The model and controllers have been implemented in a MATLAB/SIMULINK environment. This study has been conducted to compare the effectiveness of the proposed controllers in the transient, accuracy in tracking the reference current, steady-state, dynamic responses, overshoots, and response time. Results showed that the combination EGWO-FOPID had significant advantages over the rest of the optimized controllersThe authors wish to express their gratitude to the Basque Government, through the project EKOHEGAZ (ELKARTEK KK-2021/00092), to the Diputación Foral de Álava (DFA), through the project CONAVANTER, and to the UPV/EHU, through the project GIU20/063, for supporting this work

Rekonfigurasi Jaringan Distribusi Untuk Meminimalisasi Rugi-Rugi Daya Dengan Menggunakan Metode Grey Wolf Optimizer (GWO)

Rekonfigurasi jaringan pada jaringan distribusi merupakan suatu proses atau usaha untuk mengubah status sakelar pada saluran yang terhubung (sectionalizing switch) dan yang tidak terhubung (tie switch) dengan tujuan untuk meminimalisasi rugi-rugi daya dan memperbaiki profil tegangan pada sistem. Rekonfigurasi dilakukan dengan mengganti jalur saluran baru yang terhubung tanpa menambah jumlah saluran. Namun, proses rekonfigurasi yang tidak tepat akan menyebabkan rugi-rugi daya menjadi lebih besar. Pada penelitian ini, metode Grey Wolf Optimizer (GWO) digunakan untuk melakukan rekonfigurasi yang optimal terhadap kasus sistem standar IEEE 33-bus dan sistem standar IEEE 69-bus. Hasil simulasi pada sistem standar IEEE 33-bus menunjukkan bahwa setelah dilakukan optimasi rekonfigurasi, rugi-rugi daya aktif pada sistem menjadi sebesar 139,5513 kW atau berkurang sebesar 31,146% dari sebelum rekonfigurasi, yaitu 202,6771 kW. Sedangkan pada sistem standar IEEE 69-bus, rugi-rugi daya aktif menjadi sebesar 98,6056 kW atau berkurang sebesar 56,1754% dari sebelum rekonfigurasi, yaitu 225,0007 kW. Dengan menggunakan metode GWO mampu mengurangi rugi-rugi daya aktif yang lebih baik dibandingkan dengan beberapa metode lain. Kata kunci — Rekonfigurasi, minimalisasi rugi-rugi, optimasi, Grey Wolf Optimizer (GWO

Alternative method for economic dispatch utilizing grey wolf optimizer

Power system is one of the largest and most complex engineering systems created by human. The systems are created in order to ensure the longevity and sustainability of the energy for civilization development. As been known, the nonstorage characteristics of electricity and constantly rising prices for labour, supplies and maintenance cost worldwide call for the need of economically power system operation. Economic Dispatch (ED) has the objective of dividing the power demand among the online generators economically while satisfying various constraints. Small Alternative method for economic dispatch utilizing grey wolf optimizer improvements in optimal output scheduling can contribute significantly in term of cost savings. Although several optimization methodologies have been developed for solving ED problems, the complexity of the task reveals the necessity for development of efficient algorithms to accurately locate the optimum solution. Thus, the objective of this research is to demonstrate an alternative approach for solving ED problems, aiming to provide a practical alternative for conventional methods. In this research, Grey Wolf Optimizer (GWO) is chosen because it has not been implemented in solving ED problem. Besides, the performance of the algorithm had been benchmarked on 29 well-known test functions and is able to give very competitive results compared to others well-known metaheuristic. In addition, the flexibility of this algorithm is a merit to solve different problems by only setting few parameters such as number of population and number of iteration without any special changes in the structure of the algorithm. Thus, in this research, GWO has been successful to solve higher-order nonlinearities and discontinuities characteristic of ED due to valve-point loading effects, ramp rate limits and prohibited zones. To show the feasibility and applicability of the proposed method, seven different test cases which consist of all types of practical constraints were applied and analyzed and the results were compared with recent research studies. From the simulation results, it shows that GWO is able to find the combination of scheduling generators in order to minimize the fuel cost. It has been observed that the GWO also has the ability to converge to a quality solution and possesses an alternative method for solving ED problems

Grey Wolf Optimizer and Cuckoo Search Algorithm for Electric Power System State Estimation with Load Uncertainty and False Data

State estimate serves a crucial purpose in the control centre of a modern power system. Voltage phasor of buses in such configurations is referred to as state variables that should be determined during operation. A precise estimation is needed to define the optimal operation of all components. So many mathematical and heuristic techniques can be used to achieve the aforementioned objective. An enhanced power system state estimator built on the cuck search algorithm is described in this work. Several scenarios, including the influence of load uncertainty and the likelihood of false data injection as significant challenges in electrical energy networks, are proposed to analyse the operation of estimators. The ability to identify and correct false data is also assessed in this regard. Additionally, the performance of the presented estimator is compared to that of the weighted least squares, Cuckoo Search algorithm and grey wolf Optimizer. The findings demonstrate that the grey wolf Optimizer overcomes the primary shortcomings of the conventional approaches, including accuracy and complexity, and is also better able to identify and rectify incorrect data. On IEEE 14-bus and 30-bus test systems, simulations are run to show how well the method works

GWO-based estimation of input-output parameters of thermal power plants

The fuel cost curve of thermal generators was very important in the calculation of economic dispatch and optimal power flow. Temperature and aging could make changes to fuel cost curve so curve estimation need to be done periodically. The accuracy of the curve parameters estimation strongly affected the calculation of the dispatch. This paper aims to estimate the fuel cost curve parameters by using the grey wolf optimizer method. The problem of curve parameter estimation was made as an optimization problem. The objective function to be minimized was the total number of absolute error or the difference between the actual value and the estimated value of the fuel cost function. The estimated values of parameter that produce the smallest total absolute error were the values of final solution. The simulation results showed that parameter estimation using gray wolf optimizer method further minimized the value of objective function. By using three models of fuel cost curve and given test data, parameter estimation using grey wolf optimizer method produced the better estimation results than those estimation results obtained using least square error, particle swarm optimization, genetic algorithm, artificial bee colony and cuckoo search methods