Proposed Hybrid Algorithm Research Articles

A hybrid metaheuristic approach to solve grid centric cleaner economic energy management of microgrid systems

This paper percolates a detailed and descriptive techno economic analysis for clean and efficient operation of two different grid-connected low voltage (LV) microgrid (MG) systems. The various fitness functions that were evaluated includes economic dispatch considering diverse grid pricing and grid participation strategies, emission dispatch including and excluding the use of renewables and environmental constrained economic dispatch. An advanced economic strategy termed as demand side management (DSM) was also introduced with aforementioned fitness functions and results were compared. A novel hybrid optimization algorithm mathematically modelled by amalgamating swift and popular crow search algorithm (CSA) and recently developed arithmetic optimization algorithm (AOA) was employed for the study. The results were compared with those from the recently published literature. Numerical results show that both the cost and emission were reduced significantly using the proposed algorithm compared to a long-sighted list of algorithms published in literature. Involvement of DSM improved the results to further extent. Additionally, it was also found that the proposed hybrid algorithm outperformed the likes of many in consistently yielding superior quality solutions.

Numerical Simulation and Mathematical Analysis of Meta Heuristic MPPT System for Solar Photovoltaic Applications Under Non-Linear Operational Conditions

As a sustainable energy source, the use of solar photovoltaic (PV) systems has significantly increased. Environmental elements, especially partial shadowing circumstances, have a considerable impact on how well solar PV systems function. In such cases, the various local maxima in the power-voltage curve make it difficult for conventional Maximum Power Point Tracking (MPPT) methods to maintain peak efficiency. Heuristic Maximum Power Point Tracking (MPPT) system for solar photovoltaic (PV) applications, employing the cuckoo search algorithm. The primary objective of the research is to enhance the efficiency and reliability of solar PV systems by optimizing the MPPT process, which is crucial for maximizing energy extraction under varying environmental conditions. The model is used to simulate the performance of the system under various environmental conditions, such as changes in temperature and irradiance levels. The simulation results are then statistically analyzed to evaluate the effectiveness of the cuckoo search algorithm in tracking the maximum power point accurately and rapidly. The algorithm demonstrates a robust ability to converge to the maximum power point efficiently, thereby enhancing the overall energy yield of the solar PV system.. The performance of the hybrid PSO-CSA MPPT algorithm in contrast to traditional MPPT techniques is assessed through simulations and tests under various shading circumstances. The findings show that the hybrid strategy regularly outperforms conventional methods by enhancing the total energy production of partially shadowed solar PV installations through faster convergence, less oscillations, and greater tracking accuracy. The proposed hybrid algorithm also demonstrates stability and flexibility in real-world settings, making it a potential option for boosting the dependability and efficiency of solar PV systems when shade is present. This study advances the field of renewable energy and prepares the path for the use of sophisticated optimization methods to the problems of solar PV power generation under changing environmental circumstances.

Adaptive mutation sparrow search algorithm-Elman-AdaBoost model for predicting the deformation of subway tunnels

A novel coupled model integrating Elman-AdaBoost with adaptive mutation sparrow search algorithm (AM-SSA), called AMSSA-Elman-AdaBoost, is proposed for predicting the existing metro tunnel deformation induced by adjacent deep excavations in soft ground. The novelty is that the modified SSA proposes adaptive adjustment strategy to create a balance between the capacity of exploitation and exploration. In AM-SSA, firstly, the population is initialized by cat mapping chaotic sequences to improve the ergodicity and randomness of the individual sparrow, enhancing the global search ability. Then the individuals are adjusted by Tent chaotic disturbance and Cauchy mutation to avoid the population being too concentrated or scattered, expanding the local search ability. Finally, the adaptive producer-scrounger number adjustment formula is introduced to balance the ability to seek the global and local optimal. In addition, it leads to the improved algorithm achieving a better accuracy level and convergence speed compared with the original SSA. To demonstrate the effectiveness and reliability of AM-SSA, 23 classical benchmark functions and 25 IEEE Congress on Evolutionary Computation benchmark test functions (CEC2005), are employed as the numerical examples and investigated in comparison with some well-known optimization algorithms. The statistical results indicate the promising performance of AM-SSA in a variety of optimization with constrained and unknown search spaces. By utilizing the AdaBoost algorithm, multiple sets of weak AMSSA-Elman predictor functions are restructured into one strong predictor by successive iterations for the tunnel deformation prediction output. Additionally, the on-site monitoring data acquired from a deep excavation project in Ningbo, China, were selected as the training and testing sample. Meanwhile, the predictive outcomes are compared with those of other different optimization and machine learning techniques. In the end, the obtained results in this real-world geotechnical engineering field reveal the feasibility of the proposed hybrid algorithm model, illustrating its power and superiority in terms of computational efficiency, accuracy, stability, and robustness. More critically, by observing data in real time on daily basis, the structural safety associated with metro tunnels could be supervised, which enables decision-makers to take concrete control and protection measures.

An enhanced and efficient approach for feature selection for chronic human disease prediction: A breast cancer study

Computer-aided diagnosis (CAD) systems play a vital role in modern research by effectively minimizing both time and costs. These systems support healthcare professionals like radiologists in their decision-making process by efficiently detecting abnormalities as well as offering accurate and dependable information. These systems heavily depend on the efficient selection of features to accurately categorize high-dimensional biological data. These features can subsequently assist in the diagnosis of related medical conditions. The task of identifying patterns in biomedical data can be quite challenging due to the presence of numerous irrelevant or redundant features. Therefore, it is crucial to propose and then utilize a feature selection (FS) process in order to eliminate these features. The primary goal of FS approaches is to improve the accuracy of classification by eliminating features that are irrelevant or less informative. The FS phase plays a critical role in attaining optimal results in machine learning (ML)-driven CAD systems. The effectiveness of ML models can be significantly enhanced by incorporating efficient features during the training phase. This empirical study presents a methodology for the classification of biomedical data using the FS technique. The proposed approach incorporates three soft computing-based optimization algorithms, namely Teaching Learning-Based Optimization (TLBO), Elephant Herding Optimization (EHO), and a proposed hybrid algorithm of these two. These algorithms were previously employed; however, their effectiveness in addressing FS issues in predicting human diseases has not been investigated. The following evaluation focuses on the categorization of benign and malignant tumours using the publicly available Wisconsin Diagnostic Breast Cancer (WDBC) benchmark dataset. The five-fold cross-validation technique is employed to mitigate the risk of over-fitting. The evaluation of the proposed approach's proficiency is determined based on several metrics, including sensitivity, specificity, precision, accuracy, area under the receiver-operating characteristic curve (AUC), and F1-score. The best value of accuracy computed through the suggested approach is 97.96%. The proposed clinical decision support system demonstrates a highly favourable classification performance outcome, making it a valuable tool for medical practitioners to utilize as a secondary opinion and reducing the overburden of expert medical practitioners.

Research on optimization method for flyby observation mission adapted to satellite on-board computation

In this paper, the optimization problem of orbital transfer strategy for orbital flyby observation missions is studied. A hybrid optimization method is proposed, which is improved to make it more suitable for satellite on-board computing. This new algorithm is designed to solve the initial value sensitivity problem of the sequential quadratic programming algorithm (SQP). It is consisted of the depth-first search algorithm (DFS) and the SQP algorithm and thus has the characteristics of fast convergence, high reliability, and good robustness. With this method, the DFS with a large step size is calculated first, and then the optimal value in the calculation result is used as the initial value of the SQP algorithm for further optimization. This method can obtain the approximate optimal solution available in engineering. The numerical simulation of an orbital transfer optimization problem is set to verify the effectiveness of the new hybrid algorithm. The simulation results compared with the genetic algorithm (GA) show that the proposed hybrid algorithm can effectively reduce the on-board resource occupation when getting similar results and thus can meet the needs of satellite on-board computing.

Optimizing multi-objective task scheduling in fog computing with GA-PSO algorithm for big data application.

As the volume and velocity of Big Data continue to grow, traditional cloud computing approaches struggle to meet the demands of real-time processing and low latency. Fog computing, with its distributed network of edge devices, emerges as a compelling solution. However, efficient task scheduling in fog computing remains a challenge due to its inherently multi-objective nature, balancing factors like execution time, response time, and resource utilization. This paper proposes a hybrid Genetic Algorithm (GA)-Particle Swarm Optimization (PSO) algorithm to optimize multi-objective task scheduling in fog computing environments. The hybrid approach combines the strengths of GA and PSO, achieving effective exploration and exploitation of the search space, leading to improved performance compared to traditional single-algorithm approaches. The proposed hybrid algorithm results improved the execution time by 85.68% when compared with GA algorithm, by 84% when compared with Hybrid PWOA and by 51.03% when compared with PSO algorithm as well as it improved the response time by 67.28% when compared with GA algorithm, by 54.24% when compared with Hybrid PWOA and by 75.40% when compared with PSO algorithm as well as it improved the completion time by 68.69% when compared with GA algorithm, by 98.91% when compared with Hybrid PWOA and by 75.90% when compared with PSO algorithm when various tasks inputs are given. The proposed hybrid algorithm results also improved the execution time by 84.87% when compared with GA algorithm, by 88.64% when compared with Hybrid PWOA and by 85.07% when compared with PSO algorithm it improved the response time by 65.92% when compared with GA algorithm, by 80.51% when compared with Hybrid PWOA and by 85.26% when compared with PSO algorithm as well as it improved the completion time by 67.60% when compared with GA algorithm, by 81.34% when compared with Hybrid PWOA and by 85.23% when compared with PSO algorithm when various fog nodes are given.

Computational analysis of a class of singular nonlinear fractional multi-order heat conduction model of the human head

The subject of the article is devoted to the development of a matrix collocation technique based upon the combination of the fractional-order shifted Vieta–Lucas functions (FSVLFs) and the quasilinearization method (QLM) for the numerical evaluation of the fractional multi-order heat conduction model related to the human head with singularity and nonlinearity. The fractional operators are adopted in accordance with the Liouville–Caputo derivative. The quasilinearization method (QLM) is first utilized in order to defeat the inherent nonlinearity of the problem, which is converted to a family of linearized subequations. Afterward, we use the FSVLFs along with a set of collocation nodes as the zeros of these functions to reach a linear algebraic system of equations at each iteration. In the weighted L2\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$L_2$$\\end{document} norm, the convergence analysis of the FSVLFs series solution is established. We especially assert that the expansion series form of FSVLFs is convergent in the infinity norm with order O(1K3)\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$${\\mathcal {O}(\\frac{1}{K^3})}$$\\end{document}, where K represents the number of FSVLFs used in approximating the unknown solution. Diverse computational experiments by running the presented combined QLM-FSVLFs are conducted using various fractional orders and nonlinearity parameters. The outcomes indicate that the QLM-FSVLFs produces efficient approximate solutions to the underlying model with high-order accuracy, especially near the singular point. Furthermore, the methodology of residual error functions is employed to measure the accuracy of the proposed hybrid algorithm. Comparisons with existing numerical models show the superiority of QLM-FSVLFs, which also is straightforward in implementation.

Prediction of thermal behavior of Earthen-Straw composite material with 3D numerical modeling of a random geometry without overlapping

Nowadays, stabilizing raw soil with straw fibers represents a sustainable eco-building practice. Moreover, the rebirth of the earth’s construction is primarily driven by the material’s thermal properties. In the present work, a 3D study of the earthen-straw composite material’s thermal behavior has been carried out to promote its use in construction and ensure satisfactory living conditions for the building. To consider the most possible influencing factors, the effect of fibers concentration on thermal conductivity was investigated for the straw contents of 5%, 10% and 15% by volume of earthen. Thus, a 3D model with a random distribution of straw fibers considered as cylinders without overlapping was designed under Catia V5. The distribution uses datasets from a hybrid algorithm developed for this purpose, which is then imported to Ansys Software to carry out the simulation allowing the computation of the thermal conductivity of the homogeneous material. It can be concluded that the proposed hybrid algorithm was efficient since the 3D numerical results based on the geometry generated by the algorithm’s output were satisfactory in comparison with those deducted from the 2D study and the existing theoretical models. On the other hand, adding straw fibers in the raw earth matrix with thermal conductivity equal to 0.75 W/(m.K) has enhanced its thermal insulation. Indeed, for 15% of straw fiber’s contents, we have found 0.633 W/(m.K) for the thermal conductivity of the earthen-straw composite material, which is equivalent to a decrease of 15.6% in this intrinsic parameter.

A Hybrid Approach to Improve the Video Anomaly Detection Performance of Pixel- and Frame-Based Techniques Using Machine Learning Algorithms

With the rapid development in technology in recent years, the use of cameras and the production of video and image data have similarly increased. Therefore, there is a great need to develop and improve video surveillance techniques to their maximum extent, particularly in terms of their speed, performance, and resource utilization. It is challenging to accurately detect anomalies and increase the performance by minimizing false positives, especially in crowded and dynamic areas. Therefore, this study proposes a hybrid video anomaly detection model combining multiple machine learning algorithms with pixel-based video anomaly detection (PBVAD) and frame-based video anomaly detection (FBVAD) models. In the PBVAD model, the motion influence map (MIM) algorithm based on spatio–temporal (ST) factors is used, while in the FBVAD model, the k-nearest neighbors (kNN) and support vector machine (SVM) machine learning algorithms are used in a hybrid manner. An important result of our study is the high-performance anomaly detection achieved using the proposed hybrid algorithms on the UCF-Crime data set, which contains 128 h of original real-world video data and has not been extensively studied before. The AUC performance metrics obtained using our FBVAD-kNN algorithm in experiments were averaged to 98.0%. Meanwhile, the success rates obtained using our PBVAD-MIM algorithm in the experiments were averaged to 80.7%. Our study contributes significantly to the prevention of possible harm by detecting anomalies in video data in a near real-time manner.

A Hybrid Deep Learning Algorithm for Tropospheric Zenith Wet Delay Modeling with the Spatiotemporal Variation Considered

The tropospheric Zenith Wet Delay (ZWD) is one of the primary sources of error in Global Navigation Satellite Systems (GNSS). Precise ZWD modeling is essential for GNSS positioning and Precipitable Water Vapor (PWV) retrieval. However, the ZWD modeling is challenged due to the high spatiotemporal variability of water vapor, especially in low latitudes and specific climatic regions. Traditional ZWD models make it difficult to accurately fit the nonlinear variations in ZWD in these areas. A hybrid deep learning algorithm is developed for high-precision ZWD modeling, which considers the spatiotemporal characteristics and influencing factors of ZWD. The Convolutional Neural Network (CNN) and Long Short-Term Memory (LSTM) are combined in the proposed algorithm to make a novel architecture, namely, the hybrid CNN-LSTM (CL) algorithm, combining CNN for local spatial feature extracting and LSTM for complex sequence dependency training. Data from 46 radiosonde sites in South America spanning from 2015 to 2021 are used to develop models of ZWD under three strategies, i.e., model CL-A without surface parameters, model CL-B with surface temperature, and model CL-C introducing surface temperature and water vapor pressure. The modeling accuracy of the proposed models is validated using the data from 46 radiosonde sites in 2022. The results indicate that CL-A demonstrates slightly better accuracy compared to the Global Pressure and Temperature 3 (GPT3) model; CL-B shows a precision increase of 14% compared to the Saastamoinen model, and CL-C exhibits accuracy improvements of 30% and 12% compared to the Saastamoinen and Askne and Nordius (AN) model, respectively. Evaluating the models’ generalization capabilities at non-modeled sites in South America, data from six sites in 2022 were used. CL-A shows overall better performance compared to the GPT3 model; CL-B’s accuracy is 19% better than the Saastamoinen model, and CL-C’s accuracy is enhanced by 33% and 10% compared to the Saastamoinen and AN model, respectively. Additionally, the proposed hybrid algorithm demonstrates a certain degree of improvement in both modeling accuracy and generalization accuracy for the South American region compared to individual CNN and LSTM algorithm.

Passenger Flow Prediction Method based on Hybrid Algorithm: Intelligent Transportation System

Forecasting passenger flow at metro transit stations is a useful method for optimizing the organization of passenger transportation and enhancing operational safety and transportation efficiency. Aiming at the problem that the traditional ARIMA model has poor performance in predicting passenger flow, a hybrid prediction method based on ARIMA-Kalman filtering is proposed. In this regard, ARIMA model training experimental samples are integrated with Kalman filter to create a prediction recursion equation, which is then utilized to estimate passenger flow. The simulation experiment results based on the inbound passenger flow data of Nanjing metro station show that compared with the single ARIMA model, the root mean square error of the prediction results of the proposed ARIMA-Kalman filter hybrid algorithm is reduced by 257.106, and the mean absolute error decreased by 145. 675, the mean absolute percentage error dropped by 5. 655%, proving that the proposed hybrid algorithm has higher prediction accuracy. The experiment results based on the passenger flow data of Nanjing metro station show that compared to a single ARIMA model, the proposed ARIMA Kalman filtering hybrid algorithm reduces the root mean square error of the prediction results by 257.106, the average absolute error by 145.675, and the average absolute percentage error by 5.655%. It has been proven that the proposed hybrid algorithm has higher prediction accuracy.

Leveraging AI-Driven ChatGPT for Automated Data Preprocessing in Data Science

Autism is one of the most complex and different types of disorders, and it necessitates precise diagnosis based on characteristics including societal engagement, limited verbal communication, and repetitive behaviour.A timely and correct diagnosis of Autism Spectrum Disorder can ensure that you receive the appropriate medical treatment and control to help you recover. In this publication, Artificial Neural Networks are used to classify anxiety-related arousal in children with Autism Spectrum Dis-order(ASD), which is one of the most important fields of data mining research.The main objective of this study is to apply the Artificial Neural Network Algorithm (ANN), Convolutional Neural Network Algorithm called as (CNN), in ASD using the Adaptive Kalman Filter Gaussian Mixture Model (AKFGMD). In this paper we have developed a proposed hybrid algorithm which was implemented in ASD dataset and the result relevant to the proposed algorithm is better result when compared with other classifications method with respect to accuracy, sensitivity and specificity.

Energy Efficient Cluster Head Selection Using Hybrid RL-PSO Approach

Wireless Sensor Networks (WSNs) are crucial in several applications, highlighting the need of effective clustering and fault detection systems. This paper introduces a novel approach that uses Reinforcement Learning (RL) and Particle Swarm Optimization (PSO) to optimize cluster head selection and enhance fault detection capabilities within WSNs. The proposed hybrid algorithm operates in two phases, combining the explorative capabilities of RL with the optimization process of PSO to select cluster heads based on residual energy and connectivity considerations. By continuously monitoring the network's residual energy state and the number of active nodes, the proposed method ensures prolonged network lifetime and improved overall performance. Our experimental results demonstrate the superior performance of the hybrid RL-PSO approach compared to traditional clustering algorithms, showcasing significant improvements in optimizer accuracy, residual energy preservation, and fault detection efficiency.

Hybrid spectrum sensing enhancement for cognitive radio in 6G radio system

This article highlights the potential advantages of the proposed hybrid algorithm for 6G. The algorithm's adaptability addresses the dynamic spectral characteristics of 6G environments, enabling seamless transition between different spectral conditions. The hybrid approach holds promise for enhancing spectrum utilization, reducing interference, and optimizing overall communication system performance. As 6G technology evolves, the integration of Energy Detection and Cyclostationary Spectrum Sensing through the hybrid algorithm offers a glimpse into innovative techniques that can shape the future of wireless communication, unlocking the potential for enhanced connectivity, efficiency, and user experience. Several parameters such as Probability of detection (Pd), Probability of false alarm (pfa), Biter error rate (BER) and Power spectral density (PSD) are compared and analysed with the conventional spectrum sensing algorithms. It is seen that the proposed hybrid algorithms obtained a substantial detection performance at the low Signal to noise ratio (SNR).

Internet of things‐enabled intelligent systems for remote chronic disease monitoring

AbstractThe possibility of remotely monitoring and managing chronic diseases is made feasible by the Internet of Things (IoT), which enables the collection and analysis of vast amounts of data from various sources such as sensors, devices, and wearables. However, conventional machine learning approaches often encounter difficulties when dealing with IoT data due to its noise, heterogeneity, and high dimensions. In this study, we introduce a novel technique for developing human‐centric intelligent systems for remote monitoring of chronic diseases using deep learning in an IoT context. This technique is capable of handling complex and diverse IoT data, providing accurate and comprehensible predictions and recommendations. We outline a standardized architecture for these systems, comprising of three distinct phases: data acquisition, data processing, and data visualization. To demonstrate the effectiveness of our methodology, we apply it to a case study involving the development and evaluation of a human‐centered intelligent system for remote monitoring of hypertension and diabetic patients, involving real users. Our findings reveal a substantial enhancement in prediction accuracy when employing our proposed hybrid algorithm. When compared to traditional algorithms, our proposed hybrid algorithm exhibits superior performance, achieving an accuracy of 93.5%, surpassing the Restricted Boltzmann Machine (RBM) with an accuracy of 88.2%, Long Short‐Term Memory (LSTM) networks at 90.4%, and Convolutional Neural Networks (CNN) at 91.6%. This improvement in accuracy is critical for ensuring reliable disease monitoring and management, highlighting the effectiveness of our approach. In terms of precision, scalability, and user satisfaction, we demonstrate the effectiveness and efficiency of our method. Furthermore, we address the social and ethical implications inherent in our methodology and suggest avenues for future research in this domain.

Minimization of the weight of the blade of the aerial installation by an adaptive hybrid optimization method

In various fields of engineering practice, such as turbine building, power engineering, elements of thin-walled structures are widely used, which operate under increased loads when interacting with air or water. These include blades of impellers of radial-axial and rotary-blade hydraulic turbines, blades of wind power plants. Designing highly efficient machines and structures with the required level of reliability requires determining their optimal characteristics. The article proposes an adaptive method for finding the minimum of an arbitrary smooth multivariable function. The method was used to solve the benchmark optimization problem of a function in the form of a valley. The essence of the proposed algorithm lies in the sequential approach to the bottom of the valley and the subsequent movement in the direction of decreasing the objective function. Comparison of the results of calculating the minimum point of the function is performed using both non-gradient and gradient methods, namely: Powell, Hook-Jeeves, the steepest descent method and the developed method. It was found that the effectiveness of the proposed method is greater than the usual search algorithms, but it is not without its drawbacks. The method is proposed that represents a number of hybrid methods, which form a hybrid coalition Proposed hybrid algorithm does not provide a satisfactory result in the "single" search. The search algorithm reaches a point where all the values of the function at the surrounding points are greater than the values at the obtained point, and the algorithm cannot overcome the barrier. To solve the problem, it is necessary to take the obtained point as a new starting point and repeat the algorithm for finding the minimum of the function, that is, use the multistart method. The proposed method was used to solve the problem of optimizing the blade of an air installation, which was reduced to the problem of unconditional optimization using the method of penalty functions, but the goal function had a significantly valley structure. The optimal values of section thicknesses were obtained, which made it possible to build a blade with improved characteristics.

Enhancing the accuracy of wind speed estimation model using an efficient hybrid deep learning algorithm

Estimation of wind speed holds great significance for efficient operation of wind farms and grid stability. Randomness of wind speed poses a great challenge for accurate estimation of wind speed. Hence for addressing this problem, a convolutional neural network (CNN) and long short-term memory (LSTM) based novel hybrid deep learning (DL) short-term is proposed. The prediction model is built using pre-processed wind speed dataset from Jaisalmer district, Rajasthan. For designing the proposed model, the samples of the dataset are bifurcated into four seasons such as autumn, summer, winter and spring and the prediction performance are evaluated for each season separately. The proposed hybrid algorithm (CNN-LSTM) is implemented and a comparison is made with different DL algorithms. The predictive performance are calculated by observing the statistical metrics such as root mean squared error (RMSE), mean absolute error (MAE), relative RMSE, normalized RMSE and coefficient of determination (R2). It is observed that the value of statistical metrics such as MAE, RMSE, relative RMSE and normalized RMSE of the proposed algorithm are lower than the other DL models for all the four seasons. The higher value of R2 obtained by the CNN-LSTM indicates the model is best fitted with the dataset. Conclusively, the proposed model demonstrates improved adaptability and higher prediction accuracy in comparison to the predictive skills of models utilizing the same dataset.

Intelligent design of display space layout based on two-stage deep learning network

In an age of big data and information overload, recommendation systems have evolved rapidly. Throughout the traditional design of interior spaces, the specialised nature of the work and the high rate of human involvement has led to high costs. With the continuous development of artificial intelligence technology, it provides a favourable environment for reducing the development cost of the system. This study proposes a two-stage modelling scheme based on deep learning networks for the intelligent design of display space layouts, divided into two parts: matching and layout, which greatly improves design efficiency. The research results show that through comparison tests, its prediction accuracy reaches more than 80%, which can well meet the matching requirements of household products. The training number of Epochs is between 15 and 30, its training curve tends to saturate and the best accuracy can reach 100%, while the running time of the hybrid algorithm proposed in this study is only 20.716 s, which is significantly better compared with other algorithms. The proposed hybrid algorithm has a running time of only 20.716 s, which is significantly better than other algorithms. The approach innovatively combines deep learning technology with computer-aided design (CAD), enabling designers to automatically generate display space layouts with good visibility and usability based on complex design constraints. This study presents an innovative application of the research methodology by combining quantitative and qualitative methods to analyse the data. The application of both methods provides a more comprehensive understanding of the problem under study and provides insight into the key factors that influence the results. The findings of this study can provide useful insights for policy makers and practitioners.

A variegated GWO algorithm implementation in emerging power systems optimization problems

This paper proposes a novel hybrid algorithm which is mathematically modelled by amalgamating the superior features of recently developed Grey Wolf Optimizer (GWO), Sine Cosine Algorithm (SCA), and Crow Search Algorithm (CSA). Researchers have already implemented the aforementioned three algorithms and obtained superior quality results for solving diverse optimization problems. The novel hybrid Variegated GWO Algorithm (VGWO) developed in this proposed research work is initially realized and validated for solving IEEE CEC-C06 2019 benchmark functions. Thereafter, the proposed VGWO is utilized as an optimization tool to solve three emerging and complex power system optimization problems which includes energy management of microgrid systems operated in both islanded and grid-connected mode, dynamic economic emission dispatch and reactive power planning (RPP) problem. A comparative analysis of the proposed VGWO approach with other established metaheuristics is undertaken for each optimization problem. Numerical results show that the novel hybrid VGWO algorithm outperformed an ample number of optimization techniques in providing better quality solutions. The proposed hybrid algorithm yielded a 36.93% reduction in active power loss and 36.80% reduction in operating cost with respect to base case condition for RPP problem. Likewise while solving microgrid energy management problems 9–30% savings was realized in the generation cost compared to the ones mentioned in literature. The capability of handling many complex constraints within a minimum amount of computational time to provide consistently best solutions prioritize the proposed hybrid algorithm among its kinds. Statistical analysis validates the authenticity and viability of the proposed algorithm.

HyNTP: A Distributed Hybrid Algorithm for Time Synchronization

This paper presents a distributed hybrid algorithm that synchronizes the time and rate of a set of clocks connected over a network. Clock measurements of the nodes are given at aperiodic time instants and the controller at each node uses these measurements to achieve synchronization. Due to the continuous and impulsive nature of the clocks and the network, we introduce a hybrid system model to effectively capture the dynamics of the system and the proposed hybrid algorithm. Moreover, the hybrid algorithm allows each agent to estimate the skew of its internal clock in order to allow for synchronization to a common timer rate. We provide sufficient conditions guaranteeing synchronization of the timers, exponentially fast, with robustness. Numerical results illustrate the synchronization property induced by the proposed algorithm as well as its performance against comparable algorithms from the literature.