Adaptive Grasshopper Optimization Algorithm Research Articles

Modeling and Performance Analysis of a Grid-Connected Photovoltaic System with Advanced Controller considering Varying Environmental Conditions

This paper presents a mathematical model of 255 kW grid-connected solar photovoltaic (SPV) system. To study the performance characteristics of the grid-connected SPV system, a new hybrid adaptive grasshopper optimization algorithm with the recurrent neural network (AGO-RNN) control technique was implemented. Furthermore, the power quality at the point of common coupling (PCC) has been studied using the conventional (PSO) and proposed AGO-RNN controllers. The characteristics of the PV system were analyzed under varying environmental (variable irradiance and temperature) conditions considering 3 different cases such as (i) standard test conditions (STC), (ii) variable radiation with constant temperature, and (iii) variable radiation with variable temperature. For each case, the total harmonic distortion (THD) has been calculated using the proposed AGO-RNN control technique, and the results were compared with particle swarm optimization (PSO) technique. The 255 kW PV model is initially developed and connected to a three-level NPC inverter, an MPPT-based perturbation and observation algorithm. Later, the PV model is controlled by an AGO-RNN pulse width modulation (PWM) controller and is then integrated to the main grid at PCC. The main advantage of this technique is exploiting the separate DC-DC converter between the SPV module and the inverter. Finally, the proposed grid-connected SPV system was simulated on MATLAB for analyzing the performance of the system based on its I-V and P-V characteristics, inverter voltage, grid power, gird voltage, grid current, power factor, and THD under different environmental conditions. The simulation results demonstrate that the current magnitude and THD of the SPVGC system are improved with the cutting-edge AGO-RNN controller compared to PSO in all three different scenarios, and this value is less than 1.6%, which is within the permitted limits of IEC 61727 standards.

Bearing failure of reciprocating compressor sub-health recognition based on CAGOA-VMD and GRCMDE

The bearing vibration signal of reciprocating compressor has complex, non-stationary, nonlinear, and feature coupling characteristics. A method for sub-health recognition of sliding bearings based on curve adaptive grasshopper optimization algorithm optimize the parameters of variational mode decomposition (CAGOA-VMD) and generalized refine composite multiscale dispersion entropy (GRCMDE) is used. First, the CAGOA was used to search the best influence parameter combination of the VMD algorithm, and determine the bandwidth parameters and the number of decompositions that need to be set by the VMD algorithm, decompose the bearing fault signal to obtain a series of IMF. Then, the kurtosis and correlation coefficient criteria are used to select a group of components that contain the most information, and the fault signal is reconstructed on this component, and then the reconstructed signal is analyzed by GRCMDE to form a fault eigenvector. Finally, KPCA is used for dimensionality reduction to select input features and input into KELM for classification and recognition. The experimental results show that this method can effectively extract the bearing fault features of reciprocating compressors, and the eigenvectors have good separability, and realize the sub-health recognition of bearing fault features of reciprocating compressors.

Optimal Sizing and Location of Distributed Generators for Power Flow Analysis in Smart Grid Using IAS-MVPA Strategy

In this paper, the optimal distribution generation (DG) size and location for power flow analysis at the smart grid by hybrid method are proposed. The proposed hybrid method is the Interactive Autodidactic School (IAS) and the Most Valuable Player Algorithm (MVPA) and commonly named as IAS-MVPA method. The main aim of this work is to reduce line loss and total harmonic distortion (THD), similarly, to recover the voltage profile of system through the optimal location and size of the distributed generators and optimal rearrangement of network. Here, IAS-MVPA method is utilized as a rectification tool to get the maximum DG size and the maximal reconfiguration of network at environmental load variation. In case of failure, the IAS method is utilized for maximizing the DG location. The IAS chooses the line of maximal power loss as optimal location to place the DG based on the objective function. The fault violates the equality and inequality restrictions of the safe limit system. From the control parameters, the low voltage drift is improved using the MVPA method. The low-voltage deviation has been exploited for obtaining the maximum capacity of the DG. After that, the maximum capacity is used at maximum location that improves the power flow of the system. The proposed system is performed on MATLAB/Simulink platform, and the effectiveness is assessed by comparing it with various existing processes such as generic algorithm (GA), Cuttle fish algorithm (CFA), adaptive grasshopper optimization algorithm (AGOA) and artificial neural network (ANN).

Detecting anonymous attacks in wireless communication medium using adaptive grasshopper optimization algorithm

Intrusion Detection Systems (IDSs) is a system that monitors network traffic for suspicious activity and issues alert when such activity is revealed. Moreover, the existing IDSs-based methods are based on outdated attacks that unable to identify modern attacks or malicious trends. For this reason, in this study we developed a new multi-swarm adaptive grasshopper optimization algorithm to utilize adaptation mechanism in a group of swarms based on fuzzy logic to protect against sophisticated attacks. The proposed (MSAGOA) technique has the capability of global optimization and rapid convergence that are used to attain optimal feature subsets to identify attack types on IDS datasets. In the MSAGOA technique, learning engine as Extreme learning Machine, Naive Bayes, Random Forest and Decision Tree is applied as a fitness function to select the highly discriminating features and to maximize classification performance. Afterward, select the best classifier which works as a fitness function in our approach to measure the performance in terms of accuracy, detection rate, and false alarm rate. The simulations are performed on three IDS datasets such as NSL-KDD, AWID-ATK-R, and NGIDS-DS. The experimental results demonstrated that MSAGOA method has performed better and obtained high detection rate of 99.86%, accuracy of 99.89% in NSL-KDD and high detection rate of 98.73%, accuracy of 99.67% in AWID-ATK-R and detection rate of 89.50%, accuracy of 90.23% in NGIDS-DS. In addition, the performance is compared with several other existing techniques to show the efficacy of the proposed approach.

Application of Grasshopper Optimization Algorithm for Selective Harmonics Elimination in Low-Frequency Voltage Source Inverter

In this paper, an application of the recently developed Grasshopper Optimization Algorithm (GOA) for calculation of switching angles for Selective Harmonic Elimination (SHE) PWM in low-frequency voltage source inverter is proposed. The algorithm is based on insect behavior in the food foraging swarm of grasshoppers. The characteristic feature of GOA is the movement of agents is based on the position of all agents in the swarm. This method represents a higher probability of convergence than Particle Swarm Optimization (PSO) Modifications of GOA have been examined regarding their effect on the algorithm’s convergence. The proposed modifications were based on the following techniques: Grey Wolf Optimizer (GWO), Natural Selection (NS), Adaptive Grasshopper Optimization Algorithm (AGOA), and Opposite Based Learning (OBL). The performance of GOA and its modifications were compared with well-known PSO. Areas, where GOA is superior to PSO in terms of probability of convergence, have been shown. The efficiency of the GOA algorithm applied for solving the SHE problem was confirmed by measurements in the laboratory.

IOT and context‐aware learning‐based optimal neural network model for real‐time health monitoring

AbstractRecently, numerous context‐aware approaches are established to provide physiological information regarding the wellness and healthcare of each individual. While monitoring the health condition of the patient there occur delays in transferring data to the cloud. So to overcome such types of delays, numerous IoT sensors are developed to monitor, track, and sense the activities of the elder persons. This paper proposes four‐module architecture comprises of IoT module (IoT‐M), data pre‐processing module (DP‐M), context‐aware module (CA‐M) as well as decision‐making module (DM‐M) for storing and processing numerous cumulative sensor data. Here, an IoT comprises a concrete or substantial hardware ecological unit, that is, sensors and actuators. On the other hand, the context‐aware computational approach comprises of insubstantial software ecological unit in understanding and processing the context directly into an accomplishment through various IoT devices. The initial module or the IoT‐M comprises of sensors whereas in the DP‐M phase includes data collection phase, data storage phase, and data redundancy phase. The third phase or the CA‐M comprises two different types of layers namely fog layer, cloud layer. In addition to this, a context‐aware learning phase is also enumerated. In the final phase or the DM‐P phase, the feature extraction and classification is done by Back‐Propagation Neural Network along with the Adaptive grasshopper optimization algorithm so as to obtain a best optimal solution. Thus, an alarm or a notification is sent to the medical practitioner regarding the health condition of the patient with very less response time, high accuracy, and a high scalability rate. The evaluation results and discussions are made by comparing our proposed approach with several other approaches and the evaluation results reveal that the proposed framework provides better results with high accuracy, scalability, network latency, and low response time.

QSAR model for predicting neuraminidase inhibitors of influenza A viruses (H1N1) based on adaptive grasshopper optimization algorithm

ABSTRACT High-dimensionality is one of the major problems which affect the quality of the quantitative structure–activity relationship (QSAR) modelling. Obtaining a reliable QSAR model with few descriptors is an essential procedure in chemometrics. The binary grasshopper optimization algorithm (BGOA) is a new meta-heuristic optimization algorithm, which has been used successfully to perform feature selection. In this paper, four new transfer functions were adapted to improve the exploration and exploitation capability of the BGOA in QSAR modelling of influenza A viruses (H1N1). The QSAR model with these new quadratic transfer functions was internally and externally validated based on MSEtrain, Y-randomization test, MSEtest, and the applicability domain (AD). The validation results indicate that the model is robust and not due to chance correlation. In addition, the results indicate that the descriptor selection and prediction performance of the QSAR model for training dataset outperform the other S-shaped and V-shaped transfer functions. QSAR model using quadratic transfer function shows the lowest MSEtrain. For the test dataset, proposed QSAR model shows lower value of MSEtest compared with the other methods, indicating its higher predictive ability. In conclusion, the results reveal that the proposed QSAR model is an efficient approach for modelling high-dimensional QSAR models and it is useful for the estimation of IC50 values of neuraminidase inhibitors that have not been experimentally tested.

An effect of chaos grasshopper optimization algorithm for protection of network infrastructure

Due to the proliferation of sophisticated cyber extortion with exponentially critical effects, intrusion detection system is being evolved systematically their revealing, understanding, attribution and mitigation capabilities. Unfortunately, most of the modern Intrusion Detection System (IDS) technique does not provide sufficient defense services in the wireless environment while maintaining operational continuity and the stability of the defense objective in the presence of intruders and modern attacks. To resolve this problem, we propose a new feature selection technique by combining Ensemble of Feature Selection (EFS) and Chaotic Adaptive Grasshopper Optimization Algorithm (CAGOA) method, called ECAGOA. The proposed method has the capability of preventing stagnation issue and is particularly credited to the following three aspects. Firstly, EFS method is applied for selecting the high ranked subset of attributes. Then, we have employed chaos concept in Grasshopper Optimization Algorithm (GOA) which generates a uniformly distributed population to enhance the quality of the initial populations and has the capability to manage two different issues such as the ability to search for new space termed as exploration and the ability to use existing space termed as exploitation in the optimization process. In order to avoid local optima and premature convergence, lastly, an adaptive grasshopper optimization algorithm is developed by using organized parameter adaptation method. Furthermore, the adaptive behavior of GOA is applied to decide whether a record signifies an anomaly or not, differing from some approaches acquainted in the literature. Support vector machine (SVM) is used as a fitness function in the proposed method to choose the relevant features that can help classify the attacks accurately. In addition, it is also applied to optimize the penalty factor (C), kernel parameter (σ), and tube size (ϵ) of SVM method. The proposed algorithm is evaluated using three popular datasets: ISCX 2012, NSL-KDD and CIC-IDS2017. The evaluation results show that the proposed method outperformed several feature selection techniques from state-of-the-art methods in terms of detection rate, accuracy, and false alarm rate.

Optimal Power Flow Solution With an Embedded Center-Node Unified Power Flow Controller Using an Adaptive Grasshopper Optimization Algorithm

This paper proposes an adaptive grasshopper optimization algorithm (AGOA) for solving the optimal power flow (OPF) problem with the optimal incorporation of a center-node unified power flow controller (C-UPFC). The C-UPFC which is an advanced flexible AC transmission system (FACTS) device is inserted in series with a transmission line (TL) at its midpoint for providing the power flow control together with independent voltage control. The proposed AGOA is based on applying the Levy flight distribution and spiral path orientation of search agents to the traditional grasshopper optimization algorithm (GOA) to diminish the stagnation problem of the basic GOA at local optima and enhance its searching ability. Therefore, this AGOA technique is implemented for optimal sizing and siting of the C-UPFC on standard IEEE 30-bus and 57-bus systems as well as 26-bus system, and then compared with other well-known techniques to verify its effectiveness. To assess the installation of the C-UPFC in a power system, the optimal capacities and locations of the C-UPFC are determined for different objective functions, such as the fuel cost, fuel cost with a valve point loading effect (VPLE), piecewise cost and emission. Simulation results reveal that the proposed algorithm is more efficient and superior for OPF solution compared with the other algorithms reported in the literature. Furthermore, the optimal integration of the C-UPFC in the power system is considerably minimizing the power loss and improving the voltage profile.

Fuzzy-Based Parameter Optimization of Adaptive Unscented Kalman Filter: Methodology and Experimental Validation

This study introduces a fuzzy based optimal state estimation approach. The new method is based on two principles: Adaptive Unscented Kalman filter, and Fuzzy Adaptive Grasshopper Optimization Algorithm. The approach is designed for the optimization of an adaptive Unscented Kalman Filter. To find the optimal parameters for the filter, a fuzzy based evolutionary algorithm, named Fuzzy Adaptive Grasshopper Optimization Algorithm, is developed where its efficiency is verified by application to different benchmark functions. The proposed optimal adaptive unscented Kalman filter is applied to two nonlinear systems: a robotic manipulator, and a servo-hydraulic system. Different simulation tests are conducted to verify the performance of the filter. The results of simulations are presented and compared with a previous version of the unscented Kalman filter. For a realistic test, the proposed filter is applied on the practical servo-hydraulic system. Practical results are discussed, and presented results approve the capability of the presented method for practical applications.

High-Performance Converters for Optimal Utilization of Interconnected Renewable Energy Resources: A Proposed AGORNN Controller

In most of the industrial applications, an interleaved buck converter is used due to its limited requirements of filter and the enhanced converter dynamic. However, it suffers from an extremely small duty ratio when the high voltage ratio is required. To overcome the above drawbacks, a series capacitor buck converter was introduced. Based on the series capacitor converter model, a high-performance buck converter is proposed in this paper. In this paper, a high-performance converter is designed for the optimal utilization of interconnected renewable energy sources with the proposed control technique. The model and design of high-performance converter is revealed with the consideration of enhanced converter efficiency, effective utilization of renewable energy sources and reduced switching loss. The proposed control technique is the organized execution of both the adaptive grasshopper optimization algorithm and recurrent neural network (AGORNN). Here, the searching behavior of the grasshopper is modified by using the search functions like crossover and mutation. In the proposed technique, the adaptive grasshopper optimization algorithm (AGOA) is utilized to generate the optimal gain dataset based on the objective of minimum error function. The accomplished dataset is used to train the recurrent neural network (RNN) and it leads to predict the exact gain parameter for the PI controller. Batteries are used as an energy source, to balance out and allow the renewable power system units to continue running at a steady and stable output power. The proposed model is executed in MATLAB/Simulink working platform, and the execution is assessed with the existing techniques.

Implementation of adaptive scheme in evolutionary technique for anomaly-based intrusion detection

Intrusion detection has become important to network security because of the increasing connectivity between computers and internet. Various Intrusion Detection Systems have been investigated to protect web or networks using several evolutionary methods and classification techniques. In this study, we propose a new technique by combining Ensemble of Feature Selection (EFS) and Adaptive Grasshopper Optimization Algorithm (AGOA) methods, called EFSAGOA which can help to identify the types of attack. In the proposed approach, initially, EFS method is applied to rank the attribute for selecting the high ranked subset of attributes. Then, AGOA is employed to determine important attributes from the reduced datasets that can contribute to predict the networks traffic behavior. Furthermore, adaptive behavior of GOA uses to decide whether a record represents an anomaly or not, differing from some approaches acquainted in the literature. AGOA uses the Support Vector Machine (SVM) as a fitness function to choose the extremely efficient features and to maximize the classification performance. In addition, it is also applied to optimize the penalty factor (C), kernel parameter $$(\sigma )$$, and tube size $$(\epsilon )$$ of SVM classifier. The performance of EFSAGOA has been evaluated on modern intrusion data as ISCX 2012. The experimental results demonstrate that the proposed method performs better and obtain high detection rate, accuracy, and low false alarm rate compared to other state-of-art techniques in ISCX 2012 data.

Day‐ahead optimal scheduling of microgrid with adaptive grasshopper optimization algorithm

SummaryRecently, the microgrid (MG) structure day‐ahead scheduling is an important aspect and achieved an optimal operation by maximizing the utility function. In this paper, a day‐ahead scheduling of MG and their optimal operation are analyzed with the help of the proposed adaptive algorithm. For the optimal analysis of MG, adaptive grasshopper algorithm (AGOA) with cuckoo search (CS) is proposed. The CS algorithm is utilized to update the learning functions of the GOA, and the optimal performances are evaluated. Here, the photovoltaic (PV), wind turbine (WT), battery, and diesel generator (DG) are considered to analyze the optimal scheduling issues, and the main aim is to minimize their generating and operational cost functions. In addition, to maximize the profit of operations in MG, the load demand must be satisfied according to their constraints and objectives. The multiobjective function is defined as the cost functions of MG such as the fuel cost, generation cost, state of charge (SOC), direct cost, reserve cost, and penalty cost, respectively. The proposed method is implemented in MATLAB/Simulink platform and tested with the IEEE 57‐bus system and IEEE 118‐bus system. In order to verify the effectiveness of the proposed method, this is compared with the existing methods such as whale optimization algorithm (WOA) and cuttlefish algorithm (CFA), respectively. Before the comparative study, the real‐time data of PV and WT are analyzed for the 24 hours. The SOC of the proposed method is analyzed and is about 80%.

Optimal utilization of renewable energy sources in MG connected system with integrated converters: an AGONN Approach

This paper proposes an efficient converter for the usage of hybrid renewable energy sources and reduced switching loss in the Micro Grid associated frameworks. The work resulted in a DC–DC converter for module integration and maximum power point tracking with an efficient adaptive control scheme. The proposed control scheme is a combined execution of both the adaptive grasshopper optimization algorithm (AGOA) and artificial neural network (ANN) named as AGONN strategy. In the proposed strategy, the AGOA plays out the evaluation technique to set up the correct control signals for the system and develops the control signals database for the offline way subject to the power exchange between source side and the load side. Moreover, to train the ANN system for the online way, the achieved dataset is utilized and it drives the control method in less execution time. Also, the objective function is characterized by the system data subject to equality and inequality constraints. The constraints are the accessibility of the renewable energy sources, power demand and the state of charge of storage elements. Batteries are used as an energy source, to balance out and allow the renewable power system units to continue running at a steady and stable output power. By then, the proposed show is executed in MATLAB/Simulink working platform and the execution is surveyed with the current methods.

Hybridizing Teaching-Learning-Based Optimization With Adaptive Grasshopper Optimization Algorithm for Abrupt Motion Tracking

Hybridizing Teaching-Learning-Based Optimization With Adaptive Grasshopper Optimization Algorithm for Abrupt Motion Tracking

An Efficient Optimal Key Based Chaos Function for Medical Image Security

With the rapid development in the field of medical image encryption, researchers analyzed a number of encryption algorithms based on chaotic systems. But there is a difficulty lies, i.e., small key space and weak security in 1-D chaotic cryptosystems. To overcome this difficulty, an alternative security model was proposed in this paper. This paper investigated highly secure medical images with a couple of subkeys, initially where a couple of subkeys are given by utilizing chaotic logistic and tent maps. As per the chaotic (C-function) process, the security was investigated like diffusion as well as confusion. Based on the initial conditions, different random numbers were generated for each map from chaotic maps. Adaptive grasshopper optimization algorithm with PSNR and correlation coefficient fitness function was proposed to choose the optimal secret and public key of the system among the random numbers. The reason behind choosing adaptive process is to enhance high-security investigation of the current proposed model compared to the existing methods. At last, the proposed strategy results were compared with existing security methods and literary works, but found to be high performing.

Distributed trajectory optimization for multiple solar-powered UAVs target tracking in urban environment by Adaptive Grasshopper Optimization Algorithm

Aiming at the trajectory optimization of the solar-powered UAVs (SUAVs) cooperative target tracking in urban environment, the distributed model predictive control (DMPC) method based on Adaptive Grasshopper Optimization Algorithm (AGOA) is proposed in this paper. Firstly, the cooperative target tracking problem in urban environment is modeled by formulating the SUAVs kinematic and target models, the urban environment constraints, solar power harvesting and consumption models for SUAV, and sight occlusions by constructions. Especially, the sight occlusions in urban environment for SUAV are taken into consideration for the first time in this paper. A judgment method of sight occlusions for SUAV is proposed to make the calculation of the energy index more precise. Second, based on the precise modeling, the DMPC method is adopted as the framework for trajectory optimization in real time. Third, AGOA, a novel intelligent algorithm to mimic the behaviors of grasshoppers, is proposed to be the DMPC solver. The proposed AGOA has a better searching ability than the traditional GOA and some other intelligent algorithms by introducing some improvement measures e.g. the natural selection strategy, the democratic decision-making mechanism, and the dynamic feedback mechanism based on the 1/5 Principle. Finally, the effectiveness of the proposed method is demonstrated by the simulations.