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

A novel algorithm of MGWO-based PI controller for a single-stage grid-connected flyback inverter with ZVS

An effective approach on zero-voltage switching scheme for a single-stage grid-connected flyback inverter along with the introduction of Modified Grey Wolf Optimizer technique based on the proportional integral controller is proposed. A focus on soft-switching is attained by means of permitting the grid-side negative current along the bidirectional switches held in the transformer’s secondary side. Consequently, there is a discharge of metal–oxide–semiconductor field-effect transistor’s output capacitor. This function led the primary switch to turn ON at the condition of zero voltage. Therefore, it is essential to optimize the reactive current level for attaining zero-voltage switching. Generally, the basic Grey Wolf Optimization has some more disadvantages of accuracy-solving and less capability of finding the fitness solutions. Hence, to overcome this, optimizer can be modified for further enhancement in the optimization process. Modified Grey Wolf Optimizer based on the proportional integral controller with pulse width modulation technique is used for controlling the switches; thereby zero-voltage switching triggering takes place which results in decreased total harmonic distortion. Finally, the simulations can be carried out based on the total harmonic distortion which helps to illustrate the effectiveness of the suggested algorithm. A 24-V, 325-W prototype has been carried out to verify the proposed system

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

LTE NETWORKS BTS LOCATION OPTIMATION WITH MODIFIED GREY WOLF OPTIMIZER

Base station is the main network element in cellular networks deployment include LTE that nowadays widely used for data communication (e.g. internet access). The vast area of placement, position variation and number of users as well as environmental factors spawned a vastly searching solution space, so that this BTS placement is an NP-hard problem. Planning process is started by defining through a dimensioning exercise that captures few constraints, for instance capacity and coverage. Network planning optimization frequently use meta-heuristic algorithm to find the optimum solution. Grey Wolf Optimizer (GWO), one of meta-heuristic algorithm that inspired from food searching process of grey wolf pack. This algorithm has been implemented in many engineering cases, including the placement of base stations on LTE networks. The advantage of this algorithm lies in the number of parameters and the simplicity of the process. GWO allocates exploration phase and exploitation phase in the same portion, so it still faces the issue of diversity in the solution process. This thesis proposes modified GWO to optimize base station location in LTE network in order to achieve better performance of the network capacity and coverage whilst consider network and environment constraint to increase network capacity and coverage. GWO has two vector coefficients, A and C, useful for local optima avoidance and manage exploration and exploitation phases. The proposed algorithm modifies GWO by changing the value of “a”. In the original GWO algorithm, the value “a” is changed linearly along the iteration,while in the proposed algorithm, the value of “a” changes based on the double steps equation that differentiate between exploration phase and exploitation phase. The simulation covered variation of areas, user number and user density. The work evaluated the number and locations of BTS deployed, coverage area and number of users that can be handled by applying meta heuristic approach : modified grey wolf optimizer using double step equation and compare it to grey wolf optimizer. This new proposed GWO named double step GWO obtained better result due to its longer exploration phase

МОДИФІКАЦІЯ АЛГОРИТМУ ВОВЧОЇ ЗГРАЇ ДЛЯ ЗАДАЧІ ДИНАМІЧНОГО РОЗПОДІЛУ НАВАНТАЖЕННЯ

Проведено аналіз існуючі модифікації алгоритму вовчої зграї. Розроблено алгоритм вовчої зграї та модифікований алгоритм вовчої зграї для знаходження ефективного розв’язку задачі динамітного розподілу навантаження для замкнутої енергетичної системи, що складається із електростанцій та споживачів. Основною особливістю задачі є зміна попиту на електроенергію протягом дня. Описано перехід від задачі із обмеженнями, що характеризують фізичні та експлуатаційні характеристики електростанцій, до задачі без обмежень із використанням функцій штрафів. Проведено ряд експериментів із використанням розроблених алгоритмів та виконано аналіз отриманих результатів.Ключові слова: динамічний розподіл навантаження, алгоритм вовчої зграї, модифікація алгоритму вовчої зграї, ройовий інтелект, електроенергетика.Бабич С. А. Модификации алгоритма волчьей стаи для задачи динамического распределения нагрузки / Национальный технический университет Украины «Киевский политехнический институт имени Игоря Сикорского»Проведен анализ существующих модификации алгоритма волчьей стаи. Разработан алгоритм волчьей стаи и модифицированный алгоритм волчьей стаи для нахождения эффективного решения задачи динамитного распределения нагрузки для замкнутой энергетической системы, состоящей из электростанций и потребителей. Основной особенностью задачи является изменение спроса на электроэнергию в течение дня. Описаны переход от задачи с ограничениями, характеризующие физические и эксплуатационные характеристики электростанций, в задачи без ограничений с использованием функций штрафов. Проведен ряд экспериментов с использованием разработанных алгоритмов и выполнен анализ полученных результатов.Ключевые слова: динамическое распределение нагрузки, алгоритм волчьей стаи, метод роя частиц, роевой интеллект, электроэнергетика.S. Babych Improved Grey Wolf Optimization for Economic Load Dispatch Problem / National Technical University of Ukraine “Igor Sikorsky Kyiv Polytechnic Institute”The analysis of existing modifications of the grey wolf optimizer algorithms is carried out. The grey wolf optimizer and improved Grey Wolf Optimization is developed to solving the dynamic load dispatch problem for an isolated power system consisting of power stations and consumers are considered. The distinction of this task is moving during day power demand. The transition from a problem with constraints characterizing the physical and operational properties of power generators to a problem without ones but with the use of fines functions is described. A set of experiments are conducted on the use of developed algorithms and analysis of the obtained results is given.Keywords: dynamic load dispatch, grey wolf optimizer, improved grey wolf optimizer, swarm intelligence, electric power industry

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