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QIBMRMN: Design of a Q-Learning based Iterative sleep-scheduling & hybrid Bioinspired Multipath Routing model for Multimedia Networks

Doorwar Minaxi, Malathi P

International Journal of Electronics and Telecommunications

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2023

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Vol. 69, No. 4

776--784

EN

Multimedia networks utilize low-power scalar nodes to modify wakeup cycles of high-performance multimedia nodes, which assists in optimizing the power-toperformance ratios. A wide variety of machine learning models are proposed by researchers to perform this task, and most of them are either highly complex, or showcase low-levels of efficiency when applied to large-scale networks. To overcome these issues, this text proposes design of a Q-learning based iterative sleep-scheduling and fuses these schedules with an efficient hybrid bioinspired multipath routing model for largescale multimedia network sets. The proposed model initially uses an iterative Q-Learning technique that analyzes energy consumption patterns of nodes, and incrementally modifies their sleep schedules. These sleep schedules are used by scalar nodes to efficiently wakeup multimedia nodes during adhoc communication requests. These communication requests are processed by a combination of Grey Wolf Optimizer (GWO) & Genetic Algorithm (GA) models, which assist in the identification of optimal paths. These paths are estimated via combined analysis of temporal throughput & packet delivery performance, with node-to-node distance & residual energy metrics. The GWO Model uses instantaneous node & network parameters, while the GA Model analyzes temporal metrics in order to identify optimal routing paths. Both these path sets are fused together via the Q-Learning mechanism, which assists in Iterative Adhoc Path Correction (IAPC), thereby improving the energy efficiency, while reducing communication delay via multipath analysis. Due to a fusion of these models, the proposed Q-Learning based Iterative sleep-scheduling & hybrid Bioinspired Multipath Routing model for Multimedia Networks (QIBMRMN) is able to reduce communication delay by 2.6%, reduce energy consumed during these communications by 14.0%, while improving throughput by 19.6% & packet delivery performance by 8.3% when compared with standard multimedia routing techniques.

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Feature optimization using a two-tier hybrid optimizer in an Internet of Things network

Agrawal Akhileshwar Prasad, Singh Nanhay

International Journal of Applied Mathematics and Computer Science

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2023

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Vol. 33, no. 2

313--326

EN

The growing use of the Internet of Things (IoT) in smart applications necessitates improved security monitoring of IoT components. The security of such components is monitored using intrusion detection systems which run machine learning (ML) algorithms to classify access attempts as anomalous or normal. However, in this case, one of the issues is the large length of the data feature vector that any ML or deep learning technique implemented on resource-constrained intelligent nodes must handle. In this paper, the problem of selecting an optimal-feature set is investigated to reduce the curse of data dimensionality. A two-layered approach is proposed: the first tier makes use of a random forest while the second tier uses a hybrid of gray wolf optimizer (GWO) and the particle swarm optimizer (PSO) with the k-nearest neighbor as the wrapper method. Further, differential weight distribution is made to the local-best and global-best positions in the velocity equation of PSO. A new metric, i.e., the reduced feature to accuracy ratio (RFAR), is introduced for comparing various works. Three data sets, namely, NSLKDD, DS2OS and BoTIoT, are used to evaluate and validate the proposed work. Experiments demonstrate improvements in accuracy up to 99.44%, 99.44% and 99.98% with the length of the optimal-feature vector equal to 9, 4 and 8 for the NSLKDD, DS2OS and BoTIoT data sets, respectively. Furthermore, classification improves for many of the individual classes of attacks: denial-of-service (DoS) (99.75%) and normal (99.52%) for NSLKDD, malicious control (100%) and DoS (68.69%) for DS2OS, and theft (95.65%) for BoTIoT.

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Optimization of fractional order PI controller using metaheuristics algorithms applied to multilevel inverter for grid connected PV

Boucheriette Wafa, Mechgoug Raihane, Benguesmia Hani

Diagnostyka

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2023

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Vol. 24, No. 3

art. no. 2023311

EN

Due to its multiple advantages in industrial and grid-connected applications, Multi-Level Inverters (MLIs) have increased in popularity in recent years. To improve the efficiency of a grid-connected PV system's integrated multi-level inverter fractional order PI (FOPI) controllers are used to describe the control process. The control system is made up of three control loops based on FOPI controllers: one for controlling the intermediate circuit voltage (Vdc) and the other two for controlling the direct and quadratic currents (Id, Iq) supplied by the multilevel inverter. The proposed controller parameters (Kp, KI, λ) must be selected in order to increase the efficiency of the multi-level inverter while decreasing the total harmonic distortion (THD) of the output current of the inverter as well as voltage. For this we used three meta-heuristic algorithms (PSO, ABC, GWO). The performance of the three controllers PSO-FOPI, ABC-FOPI and GWO-FOPI controller is compared. The findings showed that GWO-FOPI performs better than the other PSO-FOPI and ABC-FOPI in accuracy and total harmonic distortion THD term. The simulation will be conducted using Matlab/Simulink.

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Application of grey wolf optimizer to develop new global GMPE for estimating peak ground acceleration

Ghalehjough Babak Karimi, Agahian Saeid

Acta Geophysica

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2023

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Vol. 71, no. 5

2149--2161

EN

Ground motion prediction equations (GMPEs) are open challenge problems that have been developed since 1964. Parametric and nonparametric methods predict ground motion characteristics such as peak ground acceleration (PGA), velocity, displacements, and spectral accelerations. In the present study, the grey wolf optimization (GWO) algorithm was used to obtain a new and developed GMPE for predicting PGA. Data from recorded earthquakes from all over the world were collected, and after filtering of Mw and distance parameters, close to 2000 data were used for modelling. Three parameters of Mw (4–7.9), epicentral distance (0.25–115 km) and geological conditions (soft soil, stiff soil, rock) were used as input parameters for estimating PGA. Many previous studies classified geological conditions based on shear wave velocity at the top 30 m (Vs30), without taking into account the effect of Vs30 at each group. In this study, the effects of Vs30 were considered separately for each geological group too. Results showed that PGA decreased by increasing Vs30 and moving from soft soil toward rock. Finally, the relationship was compared with the other two relations suggested for the local region and global earthquakes, and despite the simplicity of the suggested relation gained by the GWO method, it estimated PGA in terms of accuracy to a good and acceptable level.

5

Three phase MLI implementation of V/F control for three phase induction motor based on FPGA and Gary wolf algorithm

Hussein Taha A., Mohammed Laith A., Hamdy Laith A.

Przegląd Elektrotechniczny

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2022

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R. 98, nr 8

41--46

EN

Due to their efficient characteristics multilevel inverters (MLI) find numerous applications in industry. In this work design and implementation of three phase 15 level inverter is used to control the speed of three phase induction motor with V/F strategy. The power circuit consist of 10 MOSFET switches per phase. Spartan 3E FPGA kit is used as a control circuit. The triggering angles for the thirty MOSFET power transistor are determined with optimum values based on gray wolf optimization algorithm (GWO). Results in the form of output voltage, current, speed, and torque are shown for different reference speeds. The torque is shown to be constant as expected for all speeds. The total harmonic distortion (THD) is reduced to a significant value compared with a traditional sinusoidal PWM technique.

PL

Ze względu na swoją wydajność, falowniki wielopoziomowe (MLI) znajdują liczne zastosowania w przemyśle. W pracy wykorzystano projekt i wykonanie trójfazowego falownika 15-stopniowego do sterowania prędkością trójfazowego silnika indukcyjnego ze strategią V/F. Obwód zasilający składa się z 10 przełączników MOSFET na fazę. Jako obwód sterujący zastosowano zestaw Spartan 3E FPGA. Kąty wyzwalania dla trzydziestu tranzystorów mocy MOSFET są określane z optymalnymi wartościami w oparciu o algorytm optymalizacji szarego wilka (GWO). Wyniki w postaci napięcia wyjściowego, prądu, prędkości i momentu obrotowego są wyświetlane dla różnych prędkości odniesienia. Pokazano, że moment obrotowy jest stały, zgodnie z oczekiwaniami dla wszystkich prędkości. Całkowite zniekształcenia harmoniczne (THD) są zredukowane do znaczącej wartości w porównaniu z tradycyjną techniką sinusoidalnego PWM.

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Fuzzy synergetic control for dynamic car-like mobile robot

Bouhamatou Zoulikha, Abedssemed Foudil

Acta Mechanica et Automatica

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2022

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Vol. 16, no. 1

48--57

EN

This paper aims to present the dynamic control of a Car-like Mobile Robot (CLMR) using Synergetic Control (SC). The SC control is used to make the linear velocity and steering velocity converge to references. Lyapunov synthesis is adopted to assure controlled system stability. To find the optimised parameters of the SC, the grey wolf optimiser (GWO) algorithm is used. These parameters depend on the best-selected fitness function. Four fitness functions are selected for this purpose, which is based on the integral of the error square (ISE), the integral of the square of the time-weighted error (ITSE), the integral of the error absolute (IAE) and the integral of the absolute of the time-weighted error (TIAE) criterion. To go further in the investigation, fuzzy logic type 2 is used to get at each iteration the appropri-ate controller parameters that give the best performances and robustness. Simulations results are conducted to show the feasibility and efficiency of the proposed control methods.

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Modeling and scheduling home appliances using nature inspired algorithms for demand response purpose

Haroun Isra, Shareef Hussain, Ibrahim Ahmad Asrul, Khalid Saifulnizam, Idris Abdelrahman Omer

Przegląd Elektrotechniczny

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2021

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R. 97, nr 4

60--66

EN

Demand response (DR) refers to programs used in endeavors to reduce overall power consumption, manage consumption peak hour shifting, and reduce demand on service providers or utilities using different methods. This paper proposes a home appliance scheduler suitable for DR applications. In the proposed method, a controller controls thermal and shiftable loads, where thermal loads are empirical models that consider different factors. They produce the load profile of the home in consideration of different input parameters, e.g., setpoints and user tolerance ranges, and various factors, e.g., the room’s physical structure and the external environment. A scheduler uses the controller to implement load shifting using the whale optimization algorithm, particle swarm optimization, and gray wolf optimization (GWO) algorithms for three different occupancy and price schemes. Acceptable results were obtained by applying the models using various outer temperatures and user tolerance ranges. The results also demonstrate cost reduction of 38.59% with GWO for the first occupancy scheme.

PL

Demand Response (DR) oznacza programy do redukcji poboru mocy, doboru czasu pracy, odbiorników energii elektrycznej. W artykule zaproponowano program użycia urządzeń domowych spełniający wymagania DR z uwzględnieniem termicznych warunków pracy. . Zaproponowano algorytmy optymalizacji.

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Prediction of blast-induced ground vibration using GPR and blast-design parameters optimization based on novel grey-wolf optimization algorithm

Lawal A. I., Olajuyi S. I., Kwon S., Aladejare A. E., Edo T. M.

Acta Geophysica

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2021

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Vol. 69, no. 4

1313--1324

EN

Blasting is an intrinsic component of mining cycle of operation. However, it is usually associated with negative environmental efects such as blast-induced ground vibration (BIGV) which require accurate prediction and control. Therefore, in this study, Gaussian process regression (GPR) has been proposed for prediction of BIGV in terms of peak particle velocity (PPV), while grey-wolf optimization (GWO) algorithm has been used to optimize the blast-design parameters for the control of BIGV in Obajana limestone quarry, Nigeria. The blast-design parameters such as burden (B), spacing (S), hole depth (Hd), stemming length (T), and number of holes (nh) were obtained from the quarry. The distance from the blasting point to the measuring point (D) and the charge per delay (W) were measured and determined, respectively. The PPV was also measured for the number of blasting operations witnessed. These seven parameters were used as inputs to the proposed GPR model, while the PPV was the targeted output. The performance of the proposed model was evaluated using some statistical indices. The output of the GPR model was compared with ANN model and three empirical models, and the GPR model proved to be more accurate with the coefcient of determination (R2 ) of approximately 1 and variance accounted for VAF of about 100%, respectively. In addition, the GWO was also developed to select the optimum blasting parameters using the ANN model for the generation of objective function. The output of the GWO revealed that if the number of holes (nh) can be reduced by 45% and W by 8%, the PPV will be reduced by about 94%. Hence, the proposed models are both suitable for prediction of PPV and optimization of blast-design parameters.

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Global path planning for multiple AUVs using GWO

Panda Madhusmita, Das Bikramaditya, Pati Bibhuti

Archives of Control Sciences

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2020

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Vol. 30, No. 1

77--100

EN

In global path planning (GPP), an autonomous underwater vehicle (AUV) tracks a predefined path. The main objective of GPP is to generate a collision free sub-optimal path with minimum path cost. The path is defined as a set of segments, passing through selected nodes known as waypoints. For smooth planar motion, the path cost is a function of the path length, the threat cost and the cost of diving. Path length is the total distance travelled from start to end point, threat cost is the penalty of collision with the obstacle and cost of diving is the energy expanse for diving deeper in ocean. This paper addresses the GPP problem for multiple AUVs in formation. Here, Grey Wolf Optimization (GWO) algorithm is used to find the suboptimal path for multiple AUVs in formation. The results obtained are compared to the results of applying Genetic Algorithm (GA) to the same problem. GA concept is simple to understand, easy to implement and supports multi-objective optimization. It is robust to local minima and have wide applications in various fields of science, engineering and commerce. Hence, GA is used for this comparative study. The performance analysis is based on computational time, length of the path generated and the total path cost. The resultant path obtained using GWO is found to be better than GA in terms of path cost and processing time. Thus, GWO is used as the GPP algorithm for three AUVs in formation. The formation follows leader-follower topography. A sliding mode controller (SMC) is developed to minimize the tracking error based on local information while maintaining formation, as mild communication exists. The stability of the sliding surface is verified by Lyapunov stability analysis. With proper path planning, the path cost can be minimized as AUVs can reach their target in less time with less energy expanses. Thus, lower path cost leads to less expensive underwater missions.

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Zastosowanie algorytmu Grey Wolf Optimizer w optymalizacji nastaw regulatora stanu układu trójmasowego

Żychlewicz M.

Interdisciplinary Journal of Engineering Sciences

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2018

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Vol. 6, no. 1

12--20

PL

W artykule przedstawiono układ regulacji prędkości napędu złożonego napędu elektrycznego, uwzględniającego podwójne połączenie sprężyste. W nadrzędnej pętli sterowania zaimplementowano regulator stanu. Głównym elementem pracy jest optymalizacja parametrów zewnętrznej części układu za pomocą algorytmu metaheurystycznego GWO (Grey Wolf Optimizer). Zaprojektowana w ten sposób struktura sterowania została porównana z klasycznym rozwiązaniem projektowym, w którym zastosowano metodę rozłożenia biegunów równania charakterystycznego do wyznaczania nastaw regulatora. Uzyskano wysoką precyzję odtwarzania sygnału zadanego. Przeprowadzona została również analiza działania struktury sterowania w obecności zmian parametrów układu trójmasowego. Przedstawione rozważania teoretyczne zostały potwierdzone w testach obliczeniowych.

EN

This article presents control structure of complex drive that contains two elastic couplings. In the outer control loop the state space controller was implemented. The main point of described work is optimization of parameters used in this part of the drive using metaheuristic algorithm called GWO (Grey Wolf Optimizer). The control structure, designed using mentioned optimization method, was compared to classic solution, known from control theory. High precision of reference speed tracking was achieved. An analysis of the system in the presence of mechanical parameters changes was also prepared. Theoretical considerations were confirmed in numerical tests.

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Enhanced Grey Wolf Optimization Algorithm for Global Optimization

Joshi H., Arora S

Fundamenta Informaticae

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2017

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Vol. 153, nr 3

235--264

EN

Grey Wolf Optimizer (GWO) is a new meta-heuristic search algorithm inspired by the social behavior of leadership and the hunting mechanism of grey wolves. GWO algorithm is prominent in terms of finding the optimal solution without getting trapped in premature convergence. In the original GWO, half of the iterations are dedicated to exploration and the other half are devoted to exploitation, overlooking the impact of right balance between these two to guarantee an accurate approximation of global optimum. To overcome this shortcoming, an Enhanced Grey Wolf Optimization (EGWO) algorithm with a better hunting mechanism is proposed, which focuses on proper balance between exploration and exploitation that leads to an optimal performance of the algorithm and hence promising candidate solutions are generated. To verify the performance of our proposed EGWO algorithm, it is benchmarked on twenty-five benchmark functions with diverse complexities. It is then employed on range based node localization problem in wireless sensor network to demonstrate its applicability. The simulation results indicate that the proposed algorithm is able to provide superior results in comparison with some wellknown algorithms. The results of the node localization problem indicate the effectiveness of the proposed algorithm in solving real world problems with unknown search spaces.