Henry Gas Solubility Optimization Research Articles

Pattern classification of bearing faults in PMSM based on time domain feature ensembles

This research aims to identify an effective feature-based pattern classification technique that uses vibration and current data to identify bearing conditions. The authors attempted non-conventional time-domain features to detect the bearing conditions in permanent magnet synchronous motors (PMSM). This work employs two case studies utilizing eight datasets from Paderborn University to identify the bearing conditions of three and twelve classes. In this work, support vector machine, k-nearest neighbor, random forest, decision tree, and naive Bayes classifiers with 10% holdout validation are applied to study 31 feature combinations. This study also examines the Henry gas solubility optimization technique for feature selection to identify the most discriminating features. The results indicate that four feature ensembles consisting of 2 to 5 features performed better with the support vector machine, k-nearest neighbor, and random forest classifiers. In contrast to previous relevant studies, the proposed features are useful in identifying PMSM-bearing conditions with the highest accuracy of 99.8% and 99% using current signals for 3 and 12 classes respectively for combined current operating conditions.

HYBRID OPTIMIZATION-ENABLED FUNCTIONAL CONNECTIVITY-BASED PIVOTAL REGION EXTRACTION AND TRANSFER LEARNING FOR AUTISM SPECTRUM DISORDER DETECTION

Autism Spectrum Disorder (ASD) is a neuro development-based disability caused by variations in the brain which occur during the early stages of human life. This may cause impact on social skills and communication of an individual. Autism is a highly challenging issue to diagnose at the early stages. ASD is one of the important problems to diagnose because it starts manifesting at low ages. However, diagnosing ASD is difficult due to its complex symptoms as well as an inadequate number of neurobiological indications. This paper aims to detect ASD based on pivotal region extraction and Feedback-Henry Jellyfish Optimization-Convolution Neural Network with Transfer Learning (FHJO_CNN with TL) is developed in this work. Here, an autistic brain image is acquired from the data sample and it is fed to the pre-processing stage. By utilizing the Kalman filter and Region of Interest (RoI), selection pre-processing is done. After that, functional connectivity-based pivotal region extraction is performed based on the proposed FHJO, where the FHJO is an incorporation of Feedback-Henry Gas Optimization (FHGO) and Jelly Fish (JS) algorithms, where, FHGO is the combination of Henry Gas Solubility Optimization (HGSO), and Feedback Artificial Tree (FAT) algorithm. Thereafter in feature extraction, features like Local Binary Pattern (LBP), and Speeded Up Robust Features (SURF), statistical features are excerpted. Finally, ASD classification is done utilizing the proposed FHJO_CNN with TL. The performance of the proposed method is evaluated using the ACERTA-ABIDE dataset and the evaluation reveals that FHJO_CNN with TL attained values of accuracy, sensitivity and specificity which are 94.08%, 95.09%, and 93.59% respectively.

Prediction of daily global solar radiation in different climatic conditions using metaheuristic search algorithms: a case study from Türkiye

Today’s many giant sectors including energy, industry, tourism, and agriculture should closely track the variation trends of solar radiation to take more benefit from the sun. However, the scarcity of solar radiation measuring stations represents a significant obstacle. This has prompted research into the estimation of global solar radiation (GSR) for various regions using existing climatic and atmospheric parameters. While prediction methods cannot supplant the precision of direct measurements, they are invaluable for studying and utilizing solar energy on a global scale. From this point of view, this paper has focused on predicting daily GSR data in three provinces (Afyonkarahisar, Rize, and Ağrı) which exhibit disparate solar radiation distributions in Türkiye. In this context, Gradient-Based Optimizer (GBO), Harris Hawks Optimization (HHO), Barnacles Mating Optimizer (BMO), Sine Cosine Algorithm (SCA), and Henry Gas Solubility Optimization (HGSO) have been employed to model the daily GSR data. The algorithms were calibrated with daily historical data of five input variables including sunshine duration, actual pressure, moisture, wind speed, and ambient temperature between 2010 and 2017 years. Then, they were tested with daily data for the 2018 year. In the study, a series of statistical metrics (R2, MABE, RMSE, and MBE) were employed to elucidate the algorithm that predicts solar radiation data with higher accuracy. The prediction results demonstrated that all algorithms achieved the highest R2 value in Rize province. It has been found that SCA (MABE of 0.7023 MJ/m2, RMSE of 0.9121 MJ/m2, and MBE of 0.2430 MJ/m2) for Afyonkarahisar province and GBO (RMSE of 0.8432 MJ/m2, MABE of 0.6703 MJ/m2, and R2 of 0.8810) for Ağrı province are the most effective algorithms for estimating GSR data. The findings indicate that each of the metaheuristic algorithms tested in this paper has the potential to predict daily GSR data within a satisfactory error range. However, the GBO and SCA algorithms provided the most accurate predictions of daily GSR data.

Hybrid Optimized Gated Recurrent Unit with Ridge Classifier for Crop Recommendation for Precise Agriculture Using Fused Feature Selection Concept

Agriculture is considered the leading field around the world, which is also the backbone of India. Agriculture is in a flawed state because the temperature changes, along with their uncertainty, cause huge damage to the crops during the manufacturing process. So, the appropriate prediction of crop expansion plays a vital role in the management of crop growth. This prediction can enhance the federated industries to make their sustainability toward the occupation. Recently, the farmers have not selected suitable crops for their cultivation based on soil factors. This makes a negative impact on crop yield, and thus, the Indian farmers can suffer from severe losses besides the monetary front. Hence, the optimal crop recommendation model has to consider different parameters of the soil for forecasting the best crop for cultivation, which increases crop growth and crop production. Thus, this research work explores a new crop recommendation model for precision agriculture intending to promote crop yield and alleviate the loss to farmers. Initially, this research work gathers the standard data regarding the agricultural parameters of some areas. Then, the deep features using an autoencoder, and statistical features are gathered along with the Principal Component Analysis (PCA)-based features. Next, all three sets of features are fused and fed to the developed Adaptive Henry Gas Solubility Optimization (AHGSO) for selecting the optimal features. Finally, the chosen optimal features are fed to the recommendation stage, where a Gated Recurrent Unit with Ridge Classifier (GRU-RC) is suggested for getting the precise outcome regarding the recommended crop suitable to that agricultural parameter. Here, the optimal solutions are attained by tuning the parameters of GRU and ridge classifier with the same I-HGSO. At last, the results obtained from the hybrid method can be considered more efficient.

Application of Time Series Analysis and Regression Algorithm in Assessing the Effectiveness of Monetary Policy in the Era of Digital Economy

Monetary policy is the series of actions to manage country's money supply and to attain economic growth. An objection to using interest rates in monetary policy is that the use of interest rates has the problem of time uncertainty. Therefore, this research introduces Conditional Autoregressive Value-at-Risk and Henry Gas Solubility Optimization (Caviar-HGSO) with Deep Long Short Term Memory (DeepLSTM) to monetary policy forecasting to demonstrate its effectiveness. Here, Caviar-HGSO is a hybrid of Caviar and HGSO used to tune DeepLSTM weights. The HGSO algorithm follows the wind state to balance exploration and exploitation in search space while avoiding local optimization. A Caviar model estimates the parameter over time by autoregressive process and determine a parameter by regression analysis. The advantages of these two optimization algorithms lead to better monetary policy forecasts. The experimental results show that Caviar-HGSO_DeepLSTM performs better regarding MAE, MSE, and RMSE, namely 0.438, 0.103, and 0.351.

Multilevel-thermoplastic waste segregation using Archimedes Henry gas solubility trained deep quantum network

The management of plastic waste is complex process worldwide. The manual process to sort garbage is termed to be complex and expensive task and hence the scientists studied automatic sorting technique which can elevate the efficacy of recycling task. Moreover, the waste segregation models are adapted to several groups of materials to sort the waste. The usage of deep models is considered to be effective in classifying the thermoplastic waste segregation. The images are attained and delivered to pre-processing with median filter followed by segmentation. PSI-Net considering Archimedes Henry Gas Solubility Optimization (AHGSO) is adapted to segment pre-processed images. Thereafter, texture features along with statistical features are obtained for undergoing first level classification. The first level categorization is executed using Deep Quantum Neural Network (DQNN) wherein the model is trained using AHGSO and it classifies into three classes, namely “no plastic,”“Resin,” and “plastic.” In addition, the second level classification is done in case of plastic or resin using Deep Maxout Network (DMN)-AHGSO wherein DMN undergoes training by means of AHGSO. The DMN-AHGSO gained premium accuracy of 92.9%, F-measure of 92.6%, and precision of 92.9%.

Multilevel thermoplastic waste segregation and classification with AHGSO using federated learning framework

PurposeAn abundance of techniques has been presented so forth for waste classification but, they deliver inefficient results with low accuracy. Their achievement on various repositories is different and also, there is insufficiency of high-scale databases for training. The purpose of the study is to provide high security.Design/methodology/approachIn this research, optimization-assisted federated learning (FL) is introduced for thermoplastic waste segregation and classification. The deep learning (DL) network trained by Archimedes Henry gas solubility optimization (AHGSO) is used for the classification of plastic and resin types. The deep quantum neural networks (DQNN) is used for first-level classification and the deep max-out network (DMN) is employed for second-level classification. This developed AHGSO is obtained by blending the features of Archimedes optimization algorithm (AOA) and Henry gas solubility optimization (HGSO). The entities included in this approach are nodes and servers. Local training is carried out depending on local data and updations to the server are performed. Then, the model is aggregated at the server. Thereafter, each node downloads the global model and the update training is executed depending on the downloaded global and the local model till it achieves the satisfied condition. Finally, local update and aggregation at the server is altered based on the average method. The Data tag suite (DATS_2022) dataset is used for multilevel thermoplastic waste segregation and classification.FindingsBy using the DQNN in first-level classification the designed optimization-assisted FL has gained an accuracy of 0.930, mean average precision (MAP) of 0.933, false positive rate (FPR) of 0.213, loss function of 0.211, mean square error (MSE) of 0.328 and root mean square error (RMSE) of 0.572. In the second level classification, by using DMN the accuracy, MAP, FPR, loss function, MSE and RMSE are 0.932, 0.935, 0.093, 0.068, 0.303 and 0.551.Originality/valueThe multilevel thermoplastic waste segregation and classification using the proposed model is accurate and improves the effectiveness of the classification.

Tree Seed Algorithm-based Feature Selection with Optimal Deep Learning Model for Supply Chain Management

In recent days, supply chain and logistic industries have been going through a transformational wave of automation and digitization. Supply chain management (SCM) can involve machine learning (ML) abilities and prediction models to ensure that the demands are satisfied at a minimum cost. Intelligent models can be developed to determine whether adequate inventory is accessible to encounter the predictable rise in demands, and if not, the system spontaneously begins to adjust the orders with suppliers to source the raw materials for resolving the predicted future high demand. The conventional ways of SCM can be replaced by the design of recent artificial intelligence (AI) and deep learning (DL) techniques. By this motivation, this research presents a tree seed algorithm-based feature selection with optimum DL technique for supply chain management (TSA-ODLSCM). The proposed TSA-ODLSCM model involves the design of a feature subset selection approach using a tree search algorithm (TSA) algorithm. Besides, a new convolutional neural network with fuzzy cognitive maps (CNN-FCM) technique is designed for the classification process. Moreover, optimal parameter tuning of the CNN-FCM model was performed using the Henry gas solubility optimization (HGSO) technique. To exhibit the improved performance of the TSA-ODLSCM approach, a huge range of simulations were executed and outcomes were examined below several aspects. The experimental validation reported an enhanced 96.52% outcome of the TSA-ODLSCM approach over other methods.

A smart and intelligence based controlling algorithms for maximum power extraction and effective battery management in EV systems

AbstractAn economically appealing alternative to the use of fossil fuels in many nations is the electric vehicle (EV). Commercial EVs have a battery that powers the motor while also being charged by solar photovoltaic (PV). The original contribution of this research work is to implement a smart and advanced methodologies for accomplishing the objectives of MPPT control and battery management in EVs. Here, a novel spotted hyena optimized (SHO)‐MPPT controlling algorithm is applied to get the most out of the electricity generated by PV panels to meet EV energy requirements. A modified high gain super‐lift Luo converter has been implemented to improve and regulate the output voltage of PV for optimal battery charging and motor control. Then, an adaptive dynamic encoded network controller is designed to improve the voltage regulation and boosting performance of the DC‐DC converter. Consequently, a Henry gas solubility optimization is deployed to perform an effective battery management in EVs by optimally estimating the state of charge, which helps to increase the charging efficiency of EVs. Moreover, a three‐phase voltage source inverter has also been used to lower harmonic levels and deliver high‐quality output power to the EV motor. The results of the proposed MPPT controlling, converter controlling, and battery management strategies are verified in this study using a complete simulation and comparison analysis, where the parameters such as IV‐PV characteristics, motor torque, speed, total harmonic distortions (THDs), and so forth are taken into account for performance validation. By using the proposed controlling methodology, the THD is reduced to 1.09% with the improved vehicle speed of 3000 rpm, and overall performance efficiency of 99%.

Hybrid Henry gas solubility optimization and the equilibrium optimizer for feature selection: real cases with Twitter spam detection

Hybrid Henry gas solubility optimization and the equilibrium optimizer for feature selection: real cases with Twitter spam detection

Squirrel Henry Gas Solubility Optimization driven Deep Maxout Network with multi‐texture feature descriptors for digital image forgery detection

SummaryThe ability to detect image forgeries is a crucial component in solving many problems, especially social ones that arise in legal proceedings, on Facebook, and so forth. Generally, forgery detection attempts to predict the artifacts through evaluating the differences in texture properties of an image. One division of an image is copied in a comparable image, typically at a different position, in a process known as copy move forgery. The two main types of copy move forgery prediction are keypoint‐based and block‐based techniques. While the block‐based approach takes more time, the keypoint‐based model performs poorly. The goal of this research was to develop a method for detecting image counterfeiting called the Squirrel Henry Gas Solubility Optimization algorithm‐based Deep Maxout Network (SHGSO‐based DMN). Here, Steerable Pyramid Transform (SPT) decomposition is done in order to obtain quantity of multi‐scale and multi‐oriented sub bands. For the purpose of detecting forgeries, the Deep Maxout Network which was constructed using the SHGSO model is deployed. With accuracy of 0.9507, True Negative Rate (TNR) of 0.9568, and True Positive Rate (TPR) of 0.9517, the new method outperformed other current forgery detection techniques.

Application of optimization‐based regression analysis for evaluation of frost durability of recycled aggregate concrete

AbstractConcrete constructed using recycled aggregates in place of natural aggregates is an efficient approach to increase the construction sector's sustainability. To improve recycled aggregate concrete () technologies in permafrost, it is essential to certify the stability in frost‐induced conditions. The main goal of this study was to use support vector regression () methods to forecast the frost durability () of on the basis of durability agent value in cold climates. Herein, three optimization methods called Ant lion optimization (), Grey wolf optimization (), and Henry Gas Solubility Optimization () were employed for indicating optimal values of key parameters. The results depicted that all developed models have capability in predicting the of in cold regions. The values of as a comprehensive index depicted that the model has the higher value at 0.0571 as the weakest model, then at 0.0312 recognized as the second‐highest model, and finally the system at 0.0224 mentioned as outperformed model. and approaches were likewise capable of accurately forecasting the of in cold regions, but the created method outperformed them all when taking into account the explanations and justifications.

Advances in Henry Gas Solubility Optimization: A Physics-Inspired Metaheuristic Algorithm With Its Variants and Applications

Advances in Henry Gas Solubility Optimization: A Physics-Inspired Metaheuristic Algorithm With Its Variants and Applications

Exploring Optimization Algorithms: A Review of Methods and Applications

This article review focuses on feature selection as the main parameter that plays a major role in tuning machine learning models. Several algorithms of optimization such as MFO (Moth-Flame Optimization), the GA-GSA algorithm’s hybrid type, SOA (Seagull Optimization Algorithm), WOA (Whale Optimization Algorithm), GOA (Grasshopper Optimization Algorithm), HGSO (Henry Gas Solubility Optimization), and SafeOpt are widely used in engineering design, power systems scheduling, The paper stresses the importance of optimization in improving efficiency, lessening mistakes and increasing understandability of machine learning models. The literature addresses the widest directions in the usage of optimization for the following fields of science such as structural engineering, additive manufacturing, and landslide susceptibility mapping. A comprehensive summary table is generated, which shows an overview of each study, algorithm, focus, and methodology and has a stoke of key findings. The conclusions reveal the adaptiveness, competitiveness and compossibility of the optimization algorithms applied to a wide range of domains. The summary shows how optimization has the potential to change decision-making processes and activities by being a decisive factor that determines the future of branches of various industries. The main objective of this work is to direct researchers and practitioners by providing them with some innovative ideas and approaches and offering insights on the existing cutting-edge approaches while laying the groundwork for future innovations in optimization.

Rainfall modeling using two different neural networks improved by metaheuristic algorithms

Rainfall is crucial for the development and management of water resources. Six hybrid soft computing models, including multilayer perceptron (MLP)–Henry gas solubility optimization (HGSO), MLP–bat algorithm (MLP–BA), MLP–particle swarm optimization (MLP–PSO), radial basis neural network function (RBFNN)–HGSO, RBFNN–PSO, and RBFGNN–BA, were used in this study to forecast monthly rainfall at two stations in Malaysia (Sara and Banding). Different statistical measures (mean absolute error (MAE) and Nash–Sutcliffe efficiency (NSE) and percentage of BIAS (PBIAS)) and a Taylor diagram were used to assess the models’ performance. The results indicated that the MLP–HGSO performed better than the other models in forecasting rainfall at both stations. In addition, transition matrices were computed for each station and year based on the conditional probability of rainfall or absence of rainfall on a given month. The values of MAE for testing processes for the MLP–HGSO, MLP–PSO, MLP–BA, RBFNN–HGSO, RBFNN–BA, and RBFNN–PSO at the first station were 0.712, 0.755, 0.765, 0.717, 0.865, and 0.891, while the corresponding NSE and PBIAS values were 0.90–0.23, 0.83–0.29, 0.85–0.25, 0.87–0.27, 0.81–0.31, and 0.80–0.35, respectively. For the second station, the values of MAE were found 0.711, 0.743, 0.742, 0.719, 0.863 and 0.890 for the MLP–HGSO, MLP–PSO, MLP–BA, RBFNN–HGSO, RBFNN–BA, and RBFNN–PSO during testing processes and the corresponding NSE–PBIAS values were 0.92–0.22, 0.85–0.28, 0.89–0.26, 0.91–0.25, 0.83–0.31, 0.82–0.32, respectively. Based on the outputs of the MLP–HGSO, the highest rainfall was recorded in 2012 with a probability of 0.72, while the lowest rainfall was recorded in 2006 with a probability of 0.52 at the Sara Station. In addition, the results indicated that the MLP–HGSO performed better than the other models within the Banding Station. According to the findings, the hybrid MLP–HGSO was selected as an effective rainfall prediction model.

Operational Scheduling of Household Appliances by Using Triple-Objective Optimization Algorithm Integrated with Multi-Criteria Decision Making

Load scheduling is a key factor in demand side management (DSM), which manages available generation capacity with regard to the required demand. In this paper, a triple-objective load scheduling optimization problem (LSOP) is formulated for achieving optimal cost and peak demand as well as minimum customer inconvenience. A Henry gas solubility optimization (HGSO) algorithm that is based on multi-objective is used for solving LSOP. The proposed HGSO offers a set of compromise solutions that represent the tradeoff between the three objectives of the formulated problem. A set of all compromise solutions from the dominant Pareto front is achieved first, and then ranked by using MCDM so as to optimally sort these solutions. An entropy weighting method (EWM) is then used for computing the weights of various criteria that dominate the LSOP and is provided as a technique for ordering preferences by similarity to achieve the ideal solution (TOPSIS) so as to rank the sorted solutions. Two types of end-users are considered so as to show the effectiveness of the proposed LSOP: non-cooperative and cooperative users. The results of the proposed load scheduling method show the significance of the proposed method for both the cooperative and non-cooperative end-users. The proposed method achieves a cost of energy of R50.62 as a total cost of energy consumed by four non-cooperative end-users. The cost of energy for the cooperative end-users is found to be R47.39. Thus, saving in the energy cost unit is found to be around 5.5% by using the proposed method; moreover, the peak demand value is reduced by 9.7% when non-cooperative end-users becomes cooperative.

Estimation of photovoltaic models using an enhanced Henry gas solubility optimization algorithm with first-order adaptive damping Berndt-Hall-Hall-Hausman method

A reliable methodology is essential for accurately estimating the parameters of PV models, enabling reliable performance evaluations, effective control studies, accurate analysis of partial shading effects, and optimal optimization of Photovoltaic (PV) systems. It ensures that the obtained parameters reflect the true characteristics of the PV system, leading to more accurate and reliable results in various applications. The existing literature extensively explores the utilization of powerful Metaheuristic Algorithms (MAs) to address the complex constrained optimization problem in PV systems and achieve optimal solutions. However, it is important to note that a significant portion of these MAs primarily concentrates on the development of methodologies, often overlooking the design of the objective function tailored for PV systems. This oversight has created a theoretical gap in this research domain, underscoring the necessity for additional exploration and investigation to address this limitation. To address the existing theoretical gap, this study focused on developing an objective function that accurately estimates the initial root parameters of Photovoltaic (PV) models. This objective function was designed by incorporating the first-order Berndt-Hall-Hall-Hausman (BHHH) numerical method, along with the non-linear damping parameter of the Levenberg-Marquardt technique (LM). By implementing this approach, the study aimed to significantly improve the precision and reliability of estimating the initial root parameters in PV models, effectively filling the theoretical void in this specific research area. Then in terms of methodology, the Enhanced Henry Gas Solubility Optimization (EHGSO) algorithm is combined with the Sine-Cosine mutualism phase of Symbiotic Organisms Search (SOS) for efficiently estimating the unknown parameters of PV models. The keystone of EHGSO in terms of methodology enhances exploration at the beginning of optimization and intensifies exploitation in later iterations. The proposed EHGSO methodology based on the adaptive damping BHHH technique (EHGSOAdBHHH) is tested on Single Diode (SD), and Double Diode (DD) PV models using actual experimental data. EHGSOAdBHHH exhibits outstanding accordance with attained experimental data compared with other algorithms, and its superiority is validated using several statistical criteria.

Enhancing Asian Indigenous Language Processing through Deep Learning-Based Handwriting Recognition and Optimization Techniques

Asian indigenous language or autochthonous language is a language which is native to a region and spoken by indigenous people in Asia. This language is a linguistically different community created in the region. Recently, researchers in handwriting detection studies comparing with indigenous languages are attained important internet amongst research community. A new development of artificial intelligence (AI), natural language processing (NLP), cognitive analytics, and computational linguistics (CL) find it helpful in the analysis of regional low resource languages. It can be obvious in the obtainability of effectual machine detection methods and open access handwritten databases. Tamil is most ancient Indian language that is mostly exploited in Southern part of India, Sri Lanka, and Malaysia. Tamil handwritten Character Recognition (HCR) is a critical procedure in optical character detection. Therefore, this study designs a Henry Gas Solubility Optimization with Deep Learning based Handwriting Recognition Model (HGSODL-HRM) for Asian Indigenous Language Processing. The proposed HGSODL-HRM technique relies on computer vision and DL concepts for automated handwriting recognition in Tamil language, which is one of the popular indigenous languages in Asia. To accomplish this, the HGSODL-HRM technique employs capsule network (CapsNet) model for feature vector generation with HGSO algorithm as a hyperparameter optimizer. For the recognition of handwritten characters, wavelet neural network (WNN) model is exploited. Finally, the WNN parameters can be optimally chosen by sail fish optimizer (SFO) algorithm. To demonstrate the promising results of the HGSODL-HRM system, an extensive range of simulations can be implemented. The simulation outcomes stated the betterment of the HGSODL-HRM system compared to recent DL models.

MODELING THE COMPRESSIVE STRENGTH OF ULTRA-HIGH PERFORMANCE CONCRETE (UHPC) BY A MACHINE LEARNING TECHNIQUE OPTIMIZED BY NOVEL ALGORITHM

The Ultra-High Performance Concrete (UHPC) as an efficient material in constructional projects needs to be investigated in terms of ingredients and their magnitudes to compute the compressive strength of concrete. Empirical determination of relationships between constituents can demand more energy and cost. At the same time, the intelligent systems have enabled us to appraise the compressive strength based on ingredients' composition. Also, choosing eco-friendly materials in concrete as one of the widely-used items worldwide should be encouraged. This study has attempted to model the compressive strength of UHPC. Support Vector Regression (SVR), as a machine learning technique aligned with the Particle Swarm Optimization (PSO) and Henry's Gas Solubility Optimization (HGSO), have been used to simulate the compressive strength of concrete calculated based on different materials used in the present article. Eight constituents were tested to generate the compressive strength values. Various metrics were used to evaluate the modeling process. In this regard, the R2 of test phase modeling for SVR-HGSO was obtained 0.960 while for SVR-PSO, 0.925. In the training stage, the correlation rate was computed 0.902 for SVR-HGSO, which is 1.7 percent higher than SVR-PSO with R2 0.887.

Hybrid CHGSO Approach-Based EV Energy Regulation by Considering Incentive-Based Prosumer and Uncertainty

This paper proposes the hybrid approach for increasing the profit of prosumers and reducing the energy cost of the residents. The hybrid method is the combination of chaotic maps and the Henry gas solubility optimization algorithm (HGSO), hence it is called chaotic Henry gas solubility optimization (CHGSO). The proposed approach is the community-based microgrid that incorporates energy storage systems (ESS), electric vehicles (EVs) and photovoltaics (PV). Both ESS and EV are conserved as alternative sources of energy. The stored EV energy is used as a power source for homes and the grid by utilizing the Grid-to-Vehicle (G2V) and Vehicle-to-Grid (V2G) modes. The power flow of EV/ESS is controlled depending on the rate of charging and discharging. Here, the battery degradation cost is considered. The proposed approach solves the optimization problems, like battery degradation of EV/ESS, regulation of charge/discharge and provides optimal economic benefits. The proposed approach increases the prosumer profit through encouraging EV energy transactions through incentive-based pricing. Finally, the CHGSO method is performed in MATLAB and is compared with existing methods. From the simulation analysis, it can be concluded that the cost of CHGSO method is less and the prosumer participation has increased.