Elephant Herding Optimization Research Articles

Adaptive neuro fuzzy inference system with elephant herding optimization based energy management scheme

AbstractIt's not uncommon for renewable energy sources to be regarded a good investment because of the current state of the global economy. It may be difficult to locate such resources if microgrid technologies do not meet the requirements. An elephant herding optimization (EHO) was offered as a solution to the challenges of heuristic methods. Adaptive neuro‐fuzzy inference system is used to train the EHO in this technique. The primary contribution of this essay is to optimize battery use in order to maximize battery consumption. The goal of this project is to lower operating expenses while also improving the accuracy of forecasts. Given a number of programming uncertainties depending on parameters. The proposed method is tested in MATLAB/Simulink, and the results are compared to the theoretical results. In order to ensure compatibility and competency, the new method is compared to the present cuttlefish algorithm and the whale optimization algorithm.

Loss of load probability minimization for stand-alone photovoltaic system using elephant herding optimization

Stand-alone photovoltaic power systems are commonly deployed for rural electrification. Notwithstanding this, non-optimal sizing has been a constant issue in implementing such systems as non-optimal system sizing reduces the system’s reliability in supplying the required power to the loads. Hence, an Elephant Herding Optimization (EHO) is presented for optimal system sizing by searching for the optimal photovoltaic module, solar battery, charge controller and inverter so that the loss of load probability is minimized. Before developing EHO for sizing, an iterative-based approach was formulated to size the system by testing all possible design options. The optimal sizing results obtained from this iterative approach was then used for the validation of optimal sizing solution obtained from EHO-based sizing algorithm. EHO was found to produce same loss of load probability as presented by the iterative approach but with approximately 2.86 times lower in computation time. In addition, it was discovered to be better than evolutionary programming as it had lower loss of load probability with slightly faster computation.

Bio-inspired ensemble feature selection (biefs) and kernel extreme learning machine classifier for breast cancer diagnosis

Breast cancer is a major disease identified in women, affecting 2.1 million women every year, and is the reason for most cancer-related mortality in women, as per the World Health Organization (WHO). For cancer researchers, accurately forecasting the life expectancy of breast cancer patients is a serious challenge. Machine Learning (ML) has acknowledged much interest in the hope of providing correct results, but due to irrelevant features, its modelling methodologies and prediction performance are still a difficulty. To solve this issue, Feature Selection (FS) was also done to verify whether comparable accuracy can be achieved even with lesser number of features or not. Bio-Inspired Ensemble Feature Selection (BIEFS) algorithm is introduced aimed at selecting a subset of features that increase the prediction performance of subsequent classification models while also simplifying their interpretability. BIEFS algorithm uses three feature selection methods such as Adaptive Mutation Enhanced Elephant Herding Optimization (AMEHO), Adaptive Mutation Butterfly Optimization Algorithm (AMBOA), and Adaptive Salp Swarm Algorithm (ASSA) and integrates their normalized outputs for getting quantitative ensemble importance. BIEFS algorithm depends upon the aggregation of multiple FS techniques by Pearson Correlation Coefficient (PCC).This BIEFS algorithm can improve the accuracy of analysis (benign and malignant).

A hybrid swarm intelligence approach for resolving reactive power dispatch issues in power system: Optimal placement and sizing of UPFC

The most critical feature of current energy management systems is ORPD, which is used to maintain safe and reliable working conditions for power networks. Currently, due to their economic and technological advantages, HVDC transmission systems are commonly used in modern electrical power systems. The incorporation of DC link introduces more complexity in ORPD computation. Therefore, an attempt is made to optimally place and size the UPFC. In the transmission network, the UPFC is an efficient multi-purpose FACTS controller for handling the reactive as well as the physical power individually in a rapid manner. This paper plans to develop a new hybrid approach referred as Backtraping Assisted Elephant Herding Optimization (BAEHO), to address the RPD issues in a power system under unbalanced conditions. The introduced hybridized approach further determines the optimal position and size for placing the UPFC based on the constraints like the LSI, VDI, ATC, and CUPFC. Moreover, the proposed algorithm is proved for its performance in maintaining a better trade-off among multiple objectives, even under overloading conditions.

Autoregressive-Elephant Herding Optimization based Generative Adversarial Network for copy-move forgery detection with Interval type-2 fuzzy clustering

A copy move forgery is considered as the general form of digital image forgery, where a portion of the image is copied as well as pasted in some other position in the same image. To act upon forgery is simple; however detecting the forgery is more complex due to the copied portions’ features similar to other parts of images. Therefore, an effectual forgery object detection approach is exploited by exploiting the adopted Autoregressive Elephant Herding Optimization based Generative Adversarial Network (A-EHO based GAN). The proposed A-EHO approach is derived by incorporating Conditional Autoregressive Value at Risk by Regression Quantiles (CAViaR) with Elephant Herding Optimization (EHO). First, features like Local optimal oriented pattern (LOOP) and Convolutional Neural Network (CNN) features are extracted for each foreground object. Then, the features are employed to the GAN for the computation of the forgery score. Here, the RideNN classifier detects the forgery image based on the feature vector and the forgery score. As a result, the adopted approach achieved higher performance regarding detection rate, ROC, TNR, as well as TPR with the values of 96.687%, 98.35%, 97.809%, and 96.971%, respectively.

Classification of Glaucoma Based on Elephant-Herding Optimization Algorithm and Deep Belief Network

This study proposes a novel glaucoma identification system from fundus images through the deep belief network (DBN) optimized by the elephant-herding optimization (EHO) algorithm. Initially, the input image undergoes the preprocessing steps of noise removal and enhancement processes, followed by optical disc (OD) and optical cup (OC) segmentation and extraction of structural, intensity, and textural features. Most discriminative features are then selected using the ReliefF algorithm and passed to the DBN for classification into glaucomatous or normal. To enhance the classification rate of the DBN, the DBN parameters are fine-tuned by the EHO algorithm. The model has experimented on public and private datasets with 7280 images, which attained a maximum classification rate of 99.4%, 100% specificity, and 99.89% sensitivity. The 10-fold cross validation reduced the misclassification and attained 98.5% accuracy. Investigations proved the efficacy of the proposed method in avoiding bias, dataset variability, and reducing false positives compared to similar works of glaucoma classification. The proposed system can be tested on diverse datasets, aiding in the improved glaucoma diagnosis.

A two-stage deep convolutional model for demand response energy management system in IoT-enabled smart grid

Home Energy Management System (HEMS) plays an integral role in SG that optimally schedules the appliances to achieve energy savings, cost reduction, Peak–Average Ratio (PAR) minimization via the Demand Side Management (DSM) system. Previous studies are developed to perform electricity demand scheduling for the efficient management of electricity in all the sectors. However, effective monitoring and energy management solutions are essential for the residential loads to control the operation of the appliances depending upon the consumption of power during high peak hours. For this purpose, we proposes an optimal load scheduling technique named Two Stage Deep Dilated Multi-Kernel Convolutional network (DDMKC)-Modified Elephant Herd optimization algorithm (MEHOA) approach to manages, shifts the load of the consumer and thereby lowers the electricity bill. In this model, the forecaster scheme utilizes the DR pricing information that accurately predict the future pricing signal to make optimal decision and achieve minimum degree of discomfort. Based on the forecasted future prices, the MEHOA algorithm schedule the power consumption pattern to the appliances to solve the problem residential load management issues of the consumers and thereby enhances the user comfort (UC), alleviate PAR, and lowers the payment of electricity bill. Experimental analysis is conducted in terms of several metrics like energy consumption, user comfort, and PAR measures and compared with other scheduling based optimization models. The proposed model achieves the percentage greater of 60%, 62%, and 65% for the DAP, RTP, and ToU DR pricing schemes respectively. The result shows that the proposed model effectively and efficiently.

Cluster Based Routing using Energy and Distance Aware Multi-Objective Golden Eagle Optimization in Wireless Sensor Network

In recent years, WSNs have attracted significant attention due to the improvements in various sectors such as communication, electronics, and information technologies. When the clustering algorithm incorporates both Euclidean distance and energy, it automatically decreases the energy consumption. So, the major goal of this research is to reduce energy consumption for prolong the lifetime of the network. In order to achieve an energy-efficient process, Energy and Distance Aware Multi-Objective Golden Eagle Optimization (ED-MOGEO) is proposed in this research. In addition, this proposed solution reduces retransmissions and delays to improve the performance metrics. And so, this research carried out two major fitness functions (Euclidean distance and energy) for creating an energy-efficient WSN. Furthermore, energy consideration is used to reduce the nodes unavailability which results in packet loss during the transmission. For generating the routing path between the source and the Base Station (BS), the ED-MOGEO algorithm is used. From the simulation results, it shows that Proposed ED-MOGEO achieves better performances in terms of residual energy (14.36 J), end-to-end delay (12.9 ms), packet delivery ratio (0.994), normalized routing overhead (0.11), and throughput (1.131 Mbps) when compared to existing Cluster-Based Data Aggregation (CBDA) and Elephant Herding Optimization (EHO)-Greedy methods.

Impact of self adaptive-elephant herding optimization towards neural network for facial emotion recognition

FACIAL expression is one of the most efficient, universal and fundamental indicators to identify their emotions and intentions in humans. Various experiments have already been performed on automatic Facial Emotion Recognition (FER) owing to useful significance in medical diagnosis, stress monitoring for drivers, sociable robots, and other human-computer interface devices. Here, this proposed framework consists of two processes namely; “(i) proposed feature extraction and (ii) classification”. Here, a major novelty relies in the initial phase (i.e. feature extraction phase), where the Proposed Local Vector Pattern (Proposed- LVP) based features are extracted. In addition to the proposed-LVP, the other Discrete Wavelet Transform (DWT) and Gray Level Co-occurrence Matrix (GLCM) based features are also extracted. Besides, the Principal Component Analysis (PCA) method is used for reducing the dimension of the features. Further, they are subjected to classification process, where Optimized Neural Network (NN) is used. More particularly, a new Improved Elephant Herding Optimization (EHO) model termed as Self Adaptive-EHO (SA-EHO) is used to train the NN model via selecting the optimal weights. At last, the proposed work performance is computed over the other traditional systems with respect to the positive measures like “accuracy, sensitivity, specificity and precision”; negative measures like “False Positive Rate (FPR), False Negative Rate (FNR) and False Discovery Rate (FDR)”; other measures like “Negative Predictive Value (NPV), F1-score and Matthew’s Correlation Coefficient (MCC)”, respectively.

Adaptive Speed Prediction for Direct Torque-controlled Permanent Magnet Synchronous Motor Drive Using Elephant Herding Optimization Algorithm

Adaptive Speed Prediction for Direct Torque-controlled Permanent Magnet Synchronous Motor Drive Using Elephant Herding Optimization Algorithm

Dynamic scale factor based elephant herding optimization with weighted support vector machine for COVID-19 prediction

The rapid spread of Corona virus disease 2019 (COVID-19) makes its detection among the greatest challenges worldwide. A recent report indicates that COVID-19 has been detected in over 1.6 million individuals across several countries. To predict the risk of COVID-19, predictive diagnostic models have been developed to assist clinicians with RT-PCR results. However, the models do not provide the level of accuracy that is possible. To solve this problem, the proposed system designed a Dynamic Scale factor based Elephant Herding Optimization (DSEHO) with Weighted Support Vector Machine (WSVM) approach for covid-19 prediction. The initial analysis includes a dataset of COVID-19 patients. The data should be normalized using a min-max method. Independent Component Analysis (ICA) is introduced to decrease the dimensions. To select the optimal features, a Dynamic Scale factor based Elephant Herding Optimization (DSEHO) is implemented. For the prediction of COVID-19, Weighed Support Vector Machines (WSVM) is used. The proposed system provides improved accuracy, precision, recall, and F-measure over the previous system.

Quantum OPTICS and deep self-learning on swarm intelligence algorithms for Covid-19 emergency transportation.

In this paper, the quantum technology is exploited to empower the OPTICS unsupervised learning algorithm, which is a density-based clustering algorithm with numerous applications in the real world. We design an algorithm called Quantum Ordering Points To Identify the Clustering Structure (QOPTICS) and demonstrate that its computational complexity outperforms that of its classical counterpart. On the other hand, we propose a Deep self-learning approach for modeling the improvement of two Swarm Intelligence Algorithms, namely Artificial Orca Algorithm (AOA) and Elephant Herding Optimization (EHO) in order to improve their effectiveness. The deep self-learning approach is based on two well-known dynamic mutation operators, namely Cauchy mutation operator and Gaussian mutation operator. And in order to improve the efficiency of these algorithms, they are hybridized with QOPTICS and executed on just one cluster it yields. This way, both effectiveness and efficiency are handled. To evaluate the proposed approaches, an intelligent application is developed to manage the dispatching of emergency vehicles in a large geographic region and in the context of Covid-19 crisis in order to avoid an important loss in human lives. A theoretical model is designed to describe the issue mathematically. Extensive experiments are then performed to validate the mathematical model and evaluate the performance of the proposed deep self-learning algorithms. Comparison with a state-of-the-art technique shows a significant positive impact of hybridizing Quantum Machine Learning (QML) with Deep Self Learning (DSL) on solving the Covid-19 EMS transportation.

Application Of Artificial Intelligence For Temperature Profile Prediction In Additive Manufacturing Process

Additive manufacturing (AM) is the computer-aided design for the successive addition of layers by layer material. It is widely used because of the fast prototyping using laser metal deposition, which is difficult to implement using conventional techniques. Understanding the temperature profile prediction is necessary in AM processes, such as Bed Fusion process (PBF) technology to produce the right quality parts. Thus, the temperature profile prediction using Artificial intelligence techniques, like data-driven models and real-time iterative models using complex geometries, require real time control systems by considering the in-situ data. Besides, enhancing the accuracy of prediction is the hectic challenge faced by the existing systems. Hence, the proposed temperature profile prediction is developed based on an Artificial Intelligence method named Global herding algorithm-based neural network (global herding-based NN) to overcome the challenges associated with the existing methods. The proposed global herding optimization is developed by hybridizing the herding characteristics associated with the standard Elephant herding optimization (EHO) and Rhino Herd (RH) optimization to boost the solution's global optimal convergence. Moreover, the integration of the proposed global herding optimization with the NN model ensures the optimal selection of the hyper-parameters of the NN classifier, which renders effective performance of the temperature profile prediction. The effectiveness of the proposed model is revealed based on the performance metrics, such as MAE of 11.778, MAPE of 3.432, and MSE of 11.778.

OPTIMAL ENSEMBLE FEATURE SELECTION (OEFS) METHOD AND KERNEL WEIGHT CONVOLUTIONAL NEURAL NETWORK (KWCNN) CLASSIFIER FOR MEDICAL DATASETS

Disease detection software that works automatically in healthcare domain refers to the proactive or reactive use of computerised data systems for diagnosis of diseases. Medical knowledge base, data processing, and data analytics are the three major components of the system. The procedures of data processing and data analytics are crucial. Data mining (DM) techniques were used to process these processes. DM is a tool for finding patterns in massive amounts of data and retrieving knowledge. Clinical and diagnostic evidence has created a slew of reliable timely detection services and other health-related technology in the DM and healthcare industries. Artificial Intelligence (AI) in Machine Learning (ML) includes classification and predictive analytics. Identifying key characteristics and developing a classification model to determine whether the cases are disease or not is a difficult task. Feature selection (FS) refers to the process of reducing the quantity of input features when developing a predictive model. Reducing the number of input features is desirable because it cuts the computational cost of modelling while also improving the model’s performance in some cases. Instead of using a single feature selection, Optimal Ensemble Feature Selection (OEFS) solves a feature selection problem by integrating numerous feature selections. The OEFS method works by integrating the outputs of different single feature selection models like Divergence Weight Elephant Herding Optimization (DWEHO), Divergence Weight ButterFly Optimization Algorithm (DWBFO), and Differential Evolution (DE). By merging different subsets of features, Weighted Majority Voting (WMV) is used in finding the optimal feature subset. Classification model using Kernel Weight Convolutional Neural Network (KWCNN) classification is proposed. The convolution operation is a mathematical linear action across matrices that gives it its name. In terms of medical disease diagnosis, the proposed KWCNN classification performs quite well. To determine the performance of all classification algorithms, evaluation criteria such as sensitivity, specificity, f-measure, and accuracy were measured using a confusion matrix.

Practical Application of Fractional-Order PID Controller based on Evolutionary Optimization Approach for a Magnetic Levitation System

This article proposes a state-of-the-art design procedure of integer-order PID (IOPID) and fractional-order PID (FOPID) controller applied to a well-established and diversified engineering application of the Magnetic Levitation System (Maglev). Controller design and implementation for the Maglev system are quite complicated and difficult since the system dynamics exhibits non-linearity with a wide variation of operating points. Also, the system is highly unstable which rules out the possibility to accomplish conventional tuning techniques. Thus in this work, the controller tuning methodology is framed as a complex optimization problem by incorporating a new transient specification-based objective function. For designing and tuning of proposed controller parameters, modern meta-heuristic and evolutionary optimization algorithms are deployed; those are Bird Swarm Algorithm, Elephant Herding Optimization, Equilibrium optimizer and Grey Wolf Optimization. The software and hardware results demonstrate that FOPID controllers yield better time-domain and frequency-domain performance specifications and exhibit excellent reference tracking capability than IOPID controllers. The performance robustness of the proposed controllers is greatly enhanced subjected to a vast range of parametric uncertainties along with a significant minimization of the control effort.

Application of IEHO–BP neural network in forecasting building cooling and heating load

Countering the issue of low optimization accuracy and poor stability of the Elephant Herding Optimization (EHO) algorithm when solving multi-dimensional nonlinear complex problems, putting forward an improved Elephant Herding Optimization (IEHO) algorithm. The algorithm improves the accuracy of EHO algorithm optimization by chaosing the initial solution, adding dynamic influence factors, Levy flight operators and boundary mutation operators in the position update process. Standard functions are used for test experiments, and the results indicate that the introduction of improved strategies can effectively improve the accuracy and stability of the EHO algorithm when solving optimization problems. In view of the performance of the IEHO algorithm in function optimization, combining it with the BP neural network, proposing the IEHO–BP neural network algorithm, and using new algorithm to forecasting the building cooling and heating load. The experimental results show that compared with other group intelligence optimization algorithms, the output results of the cooling and heating load forecasting model based on the IEHO–BP neural network algorithm are more accurate and less oscillating.

An Optimized Neuro-Fuzzy Network for Software Project Effort Estimation

Estimating software effort is an essential project management practice. The software effort estimation is the process of predicting the time and efforts required in the development of software projects. Estimating software cost is a critical challenge during the planning stage. Due to the lack of availability of quality data and the dynamic nature of software development, accurate estimation is difficult. This paper proposes an efficient software cost estimation method for the Genetic Elephant Herding Optimization-based Neuro-Fuzzy Network (GEHO-based NFN). In this method, a neuro-fuzzy network (NFN) is used to predict the software efforts in terms of cost. The training of this NFN is optimized using the genetic elephant herding optimization (GEHO) method, which combines the features of the genetic algorithm (GA) and the elephant herding optimization (EHO) techniques. The performance of the developed method was evaluated using five historical & benchmark datasets from the industrial projects. These are based on the four widely used performance evaluation metrics, such as Mean magnitude of relative Error (MMRE), Median magnitude of relative error (MdMRE), Root Mean Square Error (RMSE), and Prediction Accuracy (PRED). The Comparative analysis and experimental results of the proposed method conclude that the performance of the GEHO-based NFN method is better than other popular soft computing methods like Linear regression, Support Vector Regression (SVR), Wavelet ANN (WNN), and Decision Tree (DT) based Software project effort estimation (SPEE) methods.

AFD filter and E2N2 classifier for improving visualization of crop image and crop classification in remote sensing image

ABSTRACT Due to increasing global population, the need of focusing on land cover and land uses by human beings is felt. Nowadays, most of the researchers and scientists are found showing much attention in agricultural landscapes because the effectual land-cover (LC) mapping provides the correct information regarding the land uses and land cover. Crop classification is one of the essential issues in the agriculture field because it affects the government and private agencies economically. The number of methodologies exist to classify the crop images from the remotely sensed image, but the traditional methodologies do not perform well due to some factors, like blurring effect, noise, etc. We have proposed an Adaptive Fuzzy-Centred Denoising Filter (AFDF) and Extended Elman Neural Network (E2N2) to enhance visualization of crop image to crop classification in order to overcome traditional techniques RSI. The remote-sensing crop images have been considered in the proposed work, and the AFDF preprocesses the image to eradicate the noise. After that, the Jaccard Coefficient-based Shared Nearest Neighbour (JCSNN) segments the object as preprocessed images. The LTrP, DWT, GLCM, and shape features are extracted as the optimal features chosen by Enhanced Elephant Herding Optimization (E2HO). Lastly, the crop is classified using the E2N2 classifier. The high-resolution images were utilized for the experiments to validate the proposed work. At last, various techniques have been compared against the proposed system, and outcomes have shown significant enhancement in accuracy and reliability. The proposed system obtains 88.01% F-measure, 95.2% accuracy with lesser FPR values that are greater when analogized to all prevalent methods. The advantage of using proposed effective protocols, like Adaptive Fuzzy-centred Denoising Filter and also E2N2 classifier, boost-up the crop images’ visualization and also crop categorization in remote-sensing images.

Support vector machine optimization via an improved elephant herding algorithm for motor energy efficiency rating.

Accurate evaluation of motor energy efficiency under off-condition operation can provide an important basis for an energy-saving upgrade of the motor and the elimination of backward motors. By considering the power quality, motor characteristics and load characteristics, a motor energy efficiency evaluation system with seven indexes and 10 grades was constructed. An improved elephant herding optimization method combined with a support vector machine rating model is proposed, it achieved an accuracy higher than 98%. Considering the slow convergence speed and low convergence precision of the standard elephant herding optimization (EHO) method, it is easy to fall into the local optimum problem. To improve population initialization, chaotic mapping and adversarial learning were used to achieve EHO with population diversity and global search capability. Group learning and elite retention have been added to improve the local development ability of the algorithm. The improved EHO has been compared with other intelligent optimization algorithms by using 12 benchmark functions, and the results show that the improved algorithm has better optimization performance.

Detection of Osteoarthritis Based on EHO Thresholding

Knee Osteoarthritis (OA) is a joint disease that is commonly observed in people around the world. Osteoarthritis commonly affects patients who are obese and those above the age of 60. A valid knee image was generated by Computed Tomography (CT). In this work, efficient segmentation of CT images using Elephant Herding Optimization (EHO) optimization is implemented. The initial stage employs, the CT image normalization and the normalized image is incited to image enhancement through histogram correlation. Consequently, the enhanced image is segmented by utilizing Niblack and Bernsen algorithm. The (EHO) optimized outcome is evaluated in two steps. The initial step includes image enhancement with the measure of Mean square error (MSE), Peak signal to noise ratio (PSNR) and Structural similarity index (SSIM). The following step includes the segmentation which includes the measure of Accuracy, Sensitivity and Specificity. The comparative analysis of EHO provides 95% of accuracy, 94% of specificity and 93% of sensitivity than that of Active contour and Otsu threshold.