Search Optimization Research Articles

Thermal Runaway Warning of Lithium Battery Based on Electronic Nose and Machine Learning Algorithms

Characteristic gas detection can be an efficient way to predict the degree of thermal runaway of a lithium battery. In this work, a sensor array consisting of three commercial MOS sensors was employed to discriminate between three target gases, CO, H2 and a mixture of the two, which are characteristic gases released during the thermal runaway of lithium batteries. In this work, an integrated model that makes the classification stage results one of the feature inputs for the concentration regression stage was employed, successfully reducing the RMSE of the concentration regression results. In addition, we also explored the influence of the selection of the response time length on the classification and regression tasks, achieving the best results in a short time through the optimum algorithm. To assess the impact of time duration sensor data on the results, we selected four time windows of different length and extracted the corresponding sensor response data for subsequent processing. Initially, principal component analysis (PCA) was used to visualise the clustering of the three target gas samples at room temperature, providing a preliminary data analysis. For the classification phase, we chose three classification algorithms—MLP (Multilayer Perceptron), ELM (Extreme Learning Machine), and SVM (Support Vector Machine)—and performed a comprehensive comparison of their classification and generalisation abilities using grid search for hyperparameter optimisation and five-fold cross-validation. The results demonstrated that MLP achieved 99.23% classification accuracy during the 20 s response period. In the concentration regression phase, we combined the classification results with the raw features to create a new feature set, which was then input into a multi-output MLP regression model. The root mean square error (RMSE) employing the new feature set was used to measure the prediction error. Ultimately, the findings showed that the input of combined features significantly reduced the regression error for the mixed gas.

Device optimization of all-perovskite triple-junction solar cells for realistic irradiation conditions

All-perovskite two-terminal tandem solar cells, comprising two or more junctions, offer high power conversion efficiencies (PCEs) that exceed the limits of single-junction photovoltaics. Realizing high-efficiency SCs requires carefully optimizing the photoactive layer, front electrodes, and functional layers. Here, we first aim to determine the optimal device architecture, i.e., the perovskites bandgaps and optimum layer thicknesses, for standard test conditions (STCs). We then optimize the energy yield (EY) under realistic outdoor conditions (ROCs), i.e., the overall electrical energy to be expected in one year at a specific location. In the first step, we reference our simulation with two terminal all-perovskite triple-junction SCs (2T3J-PSCs) to previously experimentally realized PSC with a PCE of 20.1% as a benchmark to derive the underlying diode parameters and use our in-house energy yield code combined with a hybrid particle swarm optimization and gravitational search algorithm (PSOGSA) to find the optimal bandgap combination and perovskite layer thicknesses. The optimized SCs with the optimal bandgap combination offer a PCE of 25.1% with a current density of 10.1 mA/cm2. Furthermore, the effect of the other functional layers, such as transparent conductive oxide (TCO), hole transport layer (HTL), and recombination junctions (RJs), is also investigated to enhance the cell performance further. The SCs with optimized layers exhibit improved parameters: PCE of 27.1%, with a relative improvement of 34.8% in the PCE compared with the fabricated cell, and a high current matching a of 10.8 mA/cm2. The numerical results showed that the reported cell can potentially achieve a PCE of 36% at an eVoc/EG ratio of 0.72. However, the optimal parameters may vary in real-world operating conditions due to variations in temperature, humidity, and light exposure. As a result, the optimal energy yield (EY) parameters may differ. Therefore, the energy yield optimizing process is also carried out by considering ROCs in Phoenix, AZ, USA, to find the best parameters under these conditions. We found that the optimum top layer bandgap under ROCs is lower than under STCs due to a bluer, more shifted spectrum in ROCs. After that, the optimized cell under ROCs is tested in several locations exhibiting different climatic conditions (Seattle, Honolulu, Los Angeles, Miami, and Milwaukee). The numerical results show that the optimized cell under ROCs offers an increase in energy yield in several locations compared to conventional single-junction crystalline Si-SCs (PCE = 23.6%) with a high energy yield of 648.2 kWh/m2 in Phoenix, with an improvement of 39.9% in the EY compared to the Si-SC counterpart. Our results provide design guidelines for fabricating a highly efficient triple-junction perovskite SC in the lab and outdoor applications and improve the efficiency of 2T3J-PSCs beyond the 30% limit.

Nonsingular Indirect Boundary Element Method and Multistrategy Particle Swarm Optimization Algorithm Applied to 3D Subsurface Cavity Inversion

ABSTRACTThe inversion of subsurface geological structures is a crucial approach for gaining insights into the internal composition of the earth. In this paper, we propose a novel inversion method combining the nonsingular indirect boundary element method (IBEM) with the multistrategy particle swarm optimization (MSPSO) algorithm, tailored for accurately inverting 3D subsurface cavities. Leveraging the semi‐analytical nature of IBEM offers advantages such as dimensionality reduction, automatic fulfillment of radiation conditions at infinity, and high computational accuracy. Furthermore, to augment global optimization and local search capabilities, an MSPSO algorithm is introduced. Employing multiple optimization strategies enhances particle diversity, accelerates algorithm convergence, and mitigates the risk of local optima. Through the consideration of subsurface cavities with varying parameters, this method quickly identifies the approximate location of the cavity within a wide search range. The final results demonstrate that the proposed method can simultaneously and accurately invert the 3D spatial position, size, and orientation of the cavity.

Solving blockage problem of the 5G mmWaves Wireless Communications using Jellyfish Search Optimizer

The 5G technologies are aimed to support a variety of vertically connected applications with heterogeneous devices and machines with significant improvements in terms of higher quality of service, increased network capacity and improved system throughput. However, 5G systems still face a number of challenges mentioned by researchers and organizations, and among these challenges, we highlight that millimeter wave links are very susceptible to blockage. If the line-of-sight communication path is blocked, reflexive path communication can help maintain communication. The shortest route was to take an alternate route when the first route was blocked. Among the proposed solutions to the blocking problem is an algorithm to distribute the power between different mmWave communication paths in order to overcome the masses of links under randomly distributed obstacles. The problem has been resolved with an exact method. In this article, we have optimized all the results of this exact method by applying a powerful metaheuristic technique called jellyfish search optimizer to maximize the overall capacity of the path between two nodes. The results showed that our approach procures an improvements exceed 100% over the results of the exact method.

Research on the A* Algorithm for Automatic Guided Vehicles in Large-Scale Maps

The traditional A* algorithm faces the challenges of low search efficiency and large node extension range in the field of path planning. These directly restrict the overall performance of the algorithm. In this study, we aimed to improve the search efficiency and path planning quality of the A* algorithm in complex and large-scale environments through a series of optimisation measures, including the innovation of weight design, flexible adjustment of the search neighbourhood, improvement of the heuristic function, and optimisation of the node selection strategy. Specifically, this study innovatively introduces the local obstacle rate as the core index of weight design, and it dynamically adjusts the weights according to the change of the obstacle rate during the node movement process, which effectively reduces the search space and significantly improves the search speed. At the same time, according to the real-time change of the local obstacle rate, this study dynamically adjusts the range of the search neighbourhood, so that the algorithm can choose the optimal search strategy according to different environmental information. In terms of the improvement of the heuristic function, this study adopted the diagonal distance as the benchmark for cost estimation, and it innovatively introduces the angle coefficient to reflect the complexity of path turning, thus providing the algorithm with a more accurate guidance for the search direction. In addition, this study optimises the node selection method by drawing on the idea of simulated annealing, which eliminates the need to calculate and compare all possible surrogate values during the node selection process, thus significantly reducing the running time of the algorithm. The results of the simulation experiments fully verify the effectiveness and practicality of the improved algorithm. Compared with the traditional A* algorithm, the improved algorithm achieved significant optimisation in terms of the average running time, the number of expansion nodes, and the path length, with the average running time shortened by 84%, the number of expansion nodes reduced by 94%, and the path length also shortened by 2.3%.

Multi-Step Ahead Water Level Forecasting Using Deep Neural Networks

Stream gauge height (water level) is a significant indicator for forecasting future floods. Flooding occurs when the water level exceeds the flood stage. Predicting imminent floods can save lives, protect infrastructure, and improve road traffic management and transportation. Deep neural networks have been increasingly used in this domain due to their predictive capabilities in capturing complex features and interdependencies. This study employs four distinct models—Multi-Layer Perceptron (MLP), Long Short-Term Memory (LSTM), transformer, and LSTNet—with MLP serving as the baseline model to forecast water levels. The models are trained using data from 20 distinct river gages across the state of Missouri to ensure consistent performance. Random search optimization is employed for hyperparameter tuning. The prediction intervals are set at 4, 6, 8, and 10 (each interval equivalent to 30 min) to ensure that performance results are robust and not due to random weight initialization or suboptimal hyperparameters and are consistent throughout different prediction intervals. The findings of this study indicate that the LSTNet model leads to a better performance than the other models, with a median RMSE of 0.00724, 0.00959, 0.01204, and 0.01230 for the 4, 6, 8, and 10 intervals, respectively. As climate change leads to localized storms driven by atmospheric shifts, water level fluctuations are becoming increasingly extreme, further exacerbating data drift in real-world datasets. The LSTNet model demonstrates superior performance in terms of RMSE, MAE, and the correlation coefficient across all prediction intervals when forecasting water levels under data drift conditions.

AI-optimization operation of biomass-based distributed generator for efficient radial distribution system

This research aims to optimize the size and location of biomass-based distributed generator (BMDG) units to enhance the voltage profile, reduce electrical losses, maximize cost savings, and decrease emissions from power distribution systems. Biomass-based distributed generator (BMDG) systems offer numerous advantages to enhance the efficiency of power distribution systems. However, achieving these benefits relies on determining the optimal size and position of the BMDGs. To achieve these objectives, the metaheuristic technique called particle swarm optimization (PSO) is employed to find the optimal placement and size of BMDGs. The proposed model was validated on MATLAB's IEEE-33 bus radial distribution system (RDS), confirming the aforementioned benefits. Comparative analysis between the PSO-based technique and other algorithms from previous research revealed better results with the proposed method. The results indicate that optimal placement and sizing of BMDG units have led to a reduction of more than 67.68% in active power losses and 65.90% in reactive power losses compared to the base case. Additionally, the reduction in active power loss was 40.44%, 11.39%, 42.85%, 1.81%, 0.85%, 29.83%, 5.82% and 28.38% more than artificial bee colony, backtracking search optimization algorithm, moth-flame optimization, Coordinate control, artificial Hummingbird algorithm, variable constants PSO (VCPSO), artificial gorilla troops optimizer (AGTO), and a jellyfish search optimizer respectively. Furthermore, the reactive power losses were reduced by 38.33% and 15.68% compared to VCPSO and AGTO respectively. Furthermore, this study revealed a cost reduction of 6.38% when compared to the AGTO and 1.30% when compared to the AHA. Moreover, the voltage profile of the power distribution system was improved by 7.28%. The presented methodology has demonstrated promising results for BMDGs in RDS across various applications.

Rapid deep learning prediction model using satellite imagery for radiation accident Announcement system in Serbia

Radioactivity environmental monitoring with the help of the Radiation Accident Announcement System (RAAS) established in the Republic of Serbia is of vital importance for rapid response in the event of intervention dose values being reached such as Operational Intervention levels (OILs). There are cases of impossibility of using a ground-based model in order to predict the transport and spread of radiation during a nuclear accident such as the one in the Fukushima Daiichi nuclear power plant 2011. Modern technology has made it possible to use machine learning models with remote sensing data in addition (or an alternative) to atmospheric models with ground data collection methods. Deep learning (DL) model was developed and trained on a satellite-based cloud fraction dataset to forecast diurnal cloud drift. By forecasting the movement of clouds that are potential carriers of radioactive materials, decision-makers can provide an adequate response with an Action Plan in the case of nuclear and radiation accidents and incidents. Designed Application Programming Interface (API) has been developed and integrated between the DL model and the RAAS. In this way, the monitoring, data sharing and exchange processes were automated in order to trigger the DL model when an OILs value of 1 μSv/h is reached. The hyperparameter optimization process of the DL model was done with grid search and Particle Swarm Optimization (PSO) to achieve maximum performance on the data in a reasonable amount of time. The evolution metrics to monitor and measure the performance of the DL model were cosine similarity (CS) and structural similarity (SS) with the best score of 0.78282 and 0.27063 for grid search, respectively, while 0.27065 and 0.78282 for PSO, respectively. It can be concluded that remote sensing imagery with DL model is an alternative approach against a ground-based forecasting system, and is able to predict in near real-time independently of ground network system and data.

Improved Osprey Optimization Algorithm with Multi-Strategy Fusion

The osprey optimization algorithm (OOA) is an effective metaheuristic algorithm. Although the OOA has the characteristics of strong optimality-seeking ability and fast convergence speed, it also has the disadvantages of imbalance between global exploration and local exploitation ability, easily falling into local optima in the later stage, and reduced population diversity and convergence speed. Therefore, this paper proposes an improved osprey optimization algorithm (IOOA) with multi-strategy fusion. First, Fuch chaotic mapping is used to initialize the ospreys’ population and increase the population diversity. Then, an adaptive weighting factor is introduced in the exploration phase of the algorithm to help the algorithm improve the convergence accuracy. The Cauchy variation strategy is integrated in the algorithm’s exploitation stage to enhance the diversity of the ospreys’ population and avoid falling into local optima. Finally, a Warner mechanism for the sparrow search algorithm is introduced to coordinate the algorithm’s local optimization and global search capabilities. The IOOA with various optimization algorithms is tested in a simulation for 10 benchmark test functions and 15 CEC2017 test functions, and non-parametric tests are performed on the IOOA. Experimental results show that the IOOA achieves improved accuracy and stability. The application of the IOOA to the three-bar truss engineering design problem further verifies its superiority in dealing with practical optimization problems.

An Efficient DDoS Attack Detection and Prevention Model using fusion Heuristic Enhancement of Deep Learning Approach in FANET Sector

Problem OverviewA Flying Ad-hoc Network (FANET) is a decentralized communication network formed by Unmanned Aerial Vehicles (UAVs). However, it faces significant security challenges due to its decentralized nature and mobility. One of the most critical threats is the Distributed Denial-of-Service (DDoS) attack, which aims to overcome the network by flooding it with malicious actions, disrupting communication, and causing system failures. The primary challenge lies in detecting and mitigating such attacks in real-time, given the dynamic and complex nature of FANETs, where the rapid movement of UAVs complicates monitoring and defending mechanisms. MethodologyA novel detection and prevention model has been proposed to address the issue of DDoS attacks utilizing a hybrid heuristic-based machine learning approach tailored for FANET environments. The model begins by collecting necessary information using benchmark datasets to train and enhance the detection performance. The collected data is used for detecting the DDoS attack using Hybrid Deep Learning (HDL). The HDL model developed by combining Deep Temporal Convolutional Networks (DTCN) and Long Short-Term Memory (LSTM) networks, offers a powerful solution. DTCNs excel at detecting short-term, rapidly changing patterns in network traffic, which is essential for recognizing the instant effects of topology changes in FANETs. At the same time, LSTMs are designed to handle long-term dependencies and can used to the evolving patterns of UAV movement and traffic behavior over time. Here, the hyper-parameters are optimized by proposing the Hybrid of Water Strider and Cuckoo Search (HWSCS). Water Strider optimization (WSA) and Cuckoo Search Optimizer (CSO) are combined in the HWSCS algorithm. This algorithm is a versatile and powerful tool for optimizing multifaceted systems across different fields. Its combination of local and global search abilities allows it to find optimal solutions in complicated environments, making it invaluable for tasks ranging from machine learning to network security and beyond. In network security, HWSCS is effective in optimizing the parameters of intrusion detection systems, particularly in detecting complex cyber threats like DDoS attacks, ensuring more accuracy while reducing false positives. After detecting the DDoS attack, it is prevented from communication by establishing the routing process. This routing process involves rerouting the network traffic away from the affected areas and towards secure servers, effectively isolating the attack and minimizing its impact on the network. Additionally, it allows for greater flexibility and adaptability in responding to evolving threats in realtime. Here, the attack mitigation is accomplished by finding the Optimal Link State Routing (OLSR), estimated by the hybrid HWSCS algorithm. This algorithm determines the most efficient path for redirecting traffic away from the targeted servers, preventing overload, and maintaining network stability. The integration of HWSCS with OLSR not only improves network security but also proves the importance of innovative solutions in protecting malicious activities. Lastly, the model's performance is validated and measured with different metrics. ResultsThe proposed model demonstrated superior performance compared to traditional models. It achieved a recall of 93.87, significantly higher than other approaches like 89.22 for MobileNet, 89.40 for DTCN, 88.44 for LSTM, and 88.35 for DTCN-LSTM. This improved detection accuracy, combined with the effective routing mechanism, ensures better prevention and mitigation of DDoS attacks in FANETs. The results confirm the model's ability to not only detect attacks but also minimize network disruption, providing a robust solution for maintaining secure and stable communication in FANET environments.

Multi-objective optimization and multi-attribute decision-making support for optimal operation of multi stakeholder integrated energy systems

To efficiently tackle the optimal operation problem of multi-stakeholder integrated energy systems (IESs), this paper develops a multi-objective optimization and multi-attribute decision-making support method. Mathematically, The optimal operation of IESs interconnected with distributed district heating and cooling units (DHCs) via the power grid and gas network, can be formulated as a multi-objective optimization problem considering both economic, reliability and environment-friendly objectives with numerous constraints of each energy stakeholder. Firstly, a multi-objective group search optimizer with probabilistic operator and chaotic local search (MPGSO) is proposed to balance global and local optimality during the random search iteration. The MPGSO utilizes a crowding probabilistic operator to select producers to explore areas with higher potential but less crowding and reduce the number of fitness function calculations. Moreover, a new parameter selection strategy based on chaotic sequences with limited computational complexity is adopted to escape the local optimal solutions. Consequently, a set of superior Pareto-optimal fronts could be obtained by the MPGSO. Subsequently, a multi-attribute decision-making support method based on the interval evidential reasoning (IER) approach is used to determine a final optimal solution from the Pareto-optimal solutions, taking multiple attributes of each stakeholder into consideration. To verify the effectiveness of the MPGSO, the DTLZ suite of benchmark problems are tested compared with the original GSOMP, NSGA-II and SPEA2. Additionally, simulation studies are conducted on a modified IEEE 30-bus system connected with distributed DHCs and a 15-node gas network to verify the proposed approch. The quality of the obtained Pareto-optimal solutions is assessed using a set of criteria, including hypervolume (HV), generational distance (GD), and Spacing index, among others. Simulation results show that the number of Pareto-optimal solutions (NPS) of MPGSO are higher by about 32.6 %-62.1%, computation time (CT) are lower by about 2.94 %-46.1 % compared with other algorithms. Besides, to further evaluate the performance of the proposed approach in addressing larger-scale issues, the study employs the modified IEEE 118-bus system of greater magnitude. The proposed MPGSO algorithm effectively handles multi-objective and non-convex optimization problems with Pareto sets in terms of better convergence and distributivity.

Optimized Deep Learning Model for Medical Image Diagnosis

Deep learning models, particularly convolutional neural networks (CNNs), have excelled in pattern recognition tasks like face recognition, pedestrian detection, and medical diagnosis. CNNs, known for their end-to-end feature extraction and classification, are widely used in computer vision. Pre-trained CNN models such as AlexNet, VGG-19, ResNet, and Inception can serve as feature extractors for various problems, though they often produce a large number of features. To address this challenge and reduce the number of features, this research employs an efficient two-layer feature selection algorithm. The first layer, a global search optimizer, guides the swarm in the search space to identify the optimal feature subset. The second layer refines and fine-tunes the solution obtained from the global search. The main contributions of this work encompass several aspects: (i) the introduction of a lightweight classification model that utilizes a subset of features, (ii) the use of an efficient optimizer to perform simultaneous feature and instance selection, and (iii) the direct implications for the radiology field, where it could serve as a supportive tool for diagnosis. To evaluate the proposed approach, it was tested on skin lesion diagnosis using 1113 abnormal and 1099 normal lesions. The pre-trained VGG-19 model provided a 1-D feature vector of size 4096, with a linear SVM used for classification. Results showed the optimizer reduced the feature vector by up to 86% while achieving over 70% accuracy. The proposed optimizer outperformed the Reinforcement Learning-based Memetic Particle Swarm Optimization (RLMPSO).

A Novel Development of Soft Computing based Hybrid Power Point Tracking Controllers for Hydrogen Vehicle Application with New Wide Source DC-DC Converter

From the present power generation scenario, most of the power production companies are focusing on renewable energy systems because their merits are easy to install, more reliable power sources, free from atmospheric pollution, and better adaptable to all weather conditions. In this article, a Polymer Electrolyte Membrane Fuel Cell (PEMFC) module is selected in the first objective to produce constant and continuous power to the hydrogen-based electrical vehicle systems. This fuel module provides fast power production and gives good energy density. However, the demerits of this module are more current production and low-level voltage generation. A new single-switch DC-DC circuit is introduced in the second objective of this work for optimizing the current levels of the fuel module thereby reducing the source power loss of the proposed system. Here, another problem of the fuel module is nonlinear power production which is linearized by proposing the Cuckoo Search Optimization (CSO)-based Adaptive Network-based Fuzzy Inference System (ANFIS) in the third objective to extract the maximum voltage from the fuel module. The merits of this introduced Maximum Power Point Tracking (MPPT) Controller are better tracking speed, low-level disturbances in the consumer load power, high robustness, more sustainability, and easy operation. The entire proposed system is investigated by selecting the MATLAB Simulink tool. The introduced converter circuit is analyzed by selecting the programmed DC supply.

Sandpiper optimization with hybrid deep learning model for blockchain-assisted intrusion detection in iot environment

Intrusion detection in the Internet of Things (IoTs) is a vital unit of IoT safety. IoT devices face diverse kinds of attacks, and intrusion detection systems (IDSs) play a significant role in detecting and responding to these threats. A typical IDS solution can be utilized from the IoT networks for monitoring traffic, device behaviour, and system logs for signs of intrusion or abnormal movement. Deep learning (DL) approaches are exposed to promise in enhancing the accuracy and effectiveness of IDS for IoT devices. Blockchain (BC) aided intrusion detection from IoT platforms provides many benefits, including better data integrity, transparency, and resistance to tampering. This paper projects a novel sandpiper optimizer with hybrid deep learning-based intrusion detection (SPOHDL-ID) from the BC-assisted IoT platform. The key contribution of the SPOHDL-ID model is to accomplish security via the intrusion detection and classification process from the IoT platform. In this case, the BC technology can be used for a secure data-sharing process. In the presented SPOHDL-ID technique, the selection of features from the network traffic data takes place using the SPO model. Besides, the SPOHDL-ID technique employs the HDL model for intrusion detection, which involves the design of a convolutional neural network with a stacked autoencoder (CNN-SAE) model. The beetle search optimizer algorithm (BSOA) method is used for the hyperparameter tuning procedure to increase the recognition outcomes of the CNN-SAE technique. An extensive simulation outcome is created to exhibit a better solution to the SPOHDL-ID method. The experimental validation of the SPOHDL-ID method portrayed a superior accuracy value of 99.59 % and 99.54 % over recent techniques under the ToN-IoT and CICIDS-2017 datasets.

Optimized Data Replication in Cloud Using Hybrid Optimization Approach

ABSTRACTCloud computing (CC), in contrast to traditional high‐performance computing environments, is a group of imaginary and networked resources of computing that are controlled by one unified maximum‐performance computing power. Here, this work aims to develop a novel data replication method in the cloud. The data replication is carried out with a new multi‐objective technique that considers constraints like cost, the distance between data centers, trust, and risk. Moreover, for optimal data replication, a new hybrid algorithm termed poor rich strategy assisted grasshopper optimization (PRS‐GO) is introduced. To increase the accessibility of the system, the data used continuously should be duplicated in various areas. A minimal mean value of 0.66 is gained with the PRS‐GO scheme, whereas Particle Swarm Optimization‐Tabu Search (PSO + TS), Receding Horizon Control (RHC), Sun Flower Optimization (SFO), Cat Mouse‐Based Optimization (CMBO), Hunger Games Search Optimization (HGSO), Seagull Optimization (SGO), Poor And Rich Optimization (PRO), and Grasshopper Optimization Algorithm (GOA) have got a high mean value of 0.722, 0.71, 0.71, 0.71, 0.7, 0.7, 0.7, and 0.69.

The study of active support for energy storage system in system frequency regulation

Abstract As the penetration of new energy sources increases, the proportion of traditional energy units continues to decline, reducing the inertia and primary frequency regulation capability of the power system. Therefore, exploring the frequency support capabilities of energy storage systems with low inertia is crucial. Based on this, this paper first proposes an active frequency support method for energy storage system based on a Voltage Source Virtual Synchronous Generator (VSG), aiming to enhance system inertia and reduce response delay. Based on this issue, a parameter tuning optimization selection model based on the Transient Search Optimization (TSO) algorithm is proposed to determine the optimal power output and best frequency regulation parameters for the energy storage system. Finally, the model proposed in this paper is applied to the IEEE 14-bus system to verify its effectiveness.

Research on reactor power prediction of nuclear power plant based on multivariate optimization GRU model

In the operation of nuclear power plants, the accurate prediction of power change trends is crucial for ensuring safety and stability. In this work, a ML-GRU-RS method, based on model-agnostic meta-learning (MAML), gate recurrent unit (GRU), and random search optimization, is proposed for the long-term prediction of key parameters of nuclear power plants. This method combines the fast adaptability of MAML, the time series data processing capability of GRU, and the optimization efficiency of random search to achieve high-precision predictions under varying power conditions. The results demonstrate that this method can effectively predict the future trends of key parameters in nuclear power plants. The ability of operators to anticipate these trends has been significantly enhanced, contributing to the overall safety of the nuclear power plants.

Secure Routing Using Hybrid Sine Cosine Reptile Search and Remora Optimization Algorithm for WSN

Secure Routing Using Hybrid Sine Cosine Reptile Search and Remora Optimization Algorithm for WSN

Compressive strength prediction of high-performance concrete using MLP model

ABSTRACT The remarkable mechanical properties of high-performance concrete (HPC) make it indispensable in a wide range of engineering applications. Predicting HPC’s compressive strength with precision is essential to guaranteeing its structural stability and longevity. The present work introduces a novel method for predicting HPC compressive strength (CS) by utilizing the Multi-layer Perceptron (MLP) model in conjunction with three distinct optimizers, namely Northern Goshawk Optimization (NGO), Manta Ray Foraging Optimization (MRFO), and Atom Search Optimization (ASO). An effective method for capturing complex relationships between input and output variables is the multi-layer perceptron (MLP) in artificial neural networks. Developing a dependable, robust predictive model is the goal of training the MLP model on a variety of datasets of HPC mixtures and CS. As evidenced by the MLNG model’s R2 value of 0.994 and RMSE of 1.3572, the joint efforts of MRFO, NGO, and ASO during the optimization phase improve the MLP model and produce promising results. These results clarify how different optimizers play a crucial role in improving the accuracy of HPC compressive strength predictions made with the MLP model. This information helps make better decisions regarding design and construction methods for HPC.

Skin Cancer Classification Using Premature Convergence Strategy-based Artificial Jelly Search Optimization with Convolutional Neural Network

Skin Cancer Classification Using Premature Convergence Strategy-based Artificial Jelly Search Optimization with Convolutional Neural Network