Network Optimization Algorithm Research Articles

Trust Based Multi Objective-Pelican Optimization Algorithm for Mobile Ad Hoc Networks

Trust Based Multi Objective-Pelican Optimization Algorithm for Mobile Ad Hoc Networks

A long-term prediction method for PM2.5 concentration based on spatiotemporal graph attention recurrent neural network and grey wolf optimization algorithm

With the rapid advancement of global industrialization and urbanization, the problem of air pollution, primarily attributed to PM2.5, as the main component has become increasingly serious, directly affecting people's health. Therefore, it is necessary to forecast the long-term PM2.5 levels. However, most existing prediction methods for PM2.5 concentration lack sufficient extraction of urban spatial characteristics. Therefore, this paper proposes a novel method based on spatiotemporal graph attention recurrent neural network and grey wolf optimization (GWO-GART). Firstly, a long-term prediction model of PM2.5, the Graph Attention Cycle Network (GART), is constructed by using Graph Attention network (GAT), Graph Neural Network (GNN) and Rated Recurrent Unit (GRU) networks. In the GART model, a novel multi-GAT is proposed for the first time, which can extract the spatial features of cities circularly and obtain more implicit associations between cities. Secondly, we utilize grey wolf optimization (GWO) to select the best set of hyperparameters for the model. This automated approach aids in designing the network structure of GART. We compared GWO-GART with three single models and two hybrid models using a dataset collected from the real world. The results show that, compared with the state-of-the-art (SOTA) models, GWO-GART had an average reduction of 2.13% in RMSE, 2.47% in MAE, and an average increase of 1.55% in CSI on all datasets. This demonstrates that GWO-GART provides an efficient approach to precisely forecast long-term PM2.5 levels in urban regions.

Retracted: Cognitive Feature Extraction of Puns Code-Switching Based on Neural Network Optimization Algorithm

Retracted: Cognitive Feature Extraction of Puns Code-Switching Based on Neural Network Optimization Algorithm

Decoding Optimization Algorithms for Convolutional Neural Networks in Time Series Regression Tasks

Optimization algorithms play a vital role in training deep learning models effectively. This research paper presents a comprehensive comparative analysis of various optimization algorithms for Convolutional Neural Networks (CNNs) in the context of time series regression. The study focuses on the specific application of maximum temperature prediction, utilizing a dataset of historical temperature records. The primary objective is to investigate the performance of different optimizers and evaluate their impact on the accuracy and convergence properties of the CNN model. Experiments were conducted using different optimizers, including Stochastic Gradient Descent (SGD), RMSprop, Adagrad, Adadelta, Adam, and Adamax, while keeping other factors constant. Their performance was evaluated and compared based on metrics such as mean squared error (MSE), mean absolute error (MAE), root mean squared error (RMSE), R-squared (R²), mean absolute percentage error (MAPE), and explained variance score (EVS) to measure the predictive accuracy and generalization capability of the models. Additionally, learning curves are analyzed to observe the convergence behavior of each optimizer. The experimental results, indicating significant variations in convergence speed, accuracy, and robustness among the optimizers, underscore the research value of this work. By comprehensively evaluating and comparing various optimization algorithms, we aimed to provide valuable insights into their performance characteristics in the context of time series regression using CNN models. This work contributes to the understanding of optimizer selection and its impact on model performance, assisting researchers and practitioners in choosing the most suitable optimization algorithm for time series regression tasks.

DRL-Based Service Function Chain Edge-to-Edge and Edge-to-Cloud Joint Offloading in Edge-Cloud Network

In this paper, we study service function chain (SFC) offloading in the edge-cloud network. Two offloading options are available for fully-loaded edge nodes: edge to edge (E2E) offloading and edge to cloud (E2C) offloading. Both E2E offloading and E2C offloading have been optimized in existing research, and Deep Reinforcement Learning (DRL) methods were adopted to achieve excellent performances. However, DRL-based SFC E2E and E2C joint offloading is still a research gap. In this paper, we propose a DRL-based SFC E2E and E2C joint offloading optimization algorithm for the edge-cloud network to maximize the utilization efficiency of edge resources. Twin Delayed Deep Deterministic policy gradient (TD3) algorithm is applied to the optimization problem. The simulation results indicate that the proposed algorithm has excellent convergence performance and improves the utilization efficiency of edge resources in edge-cloud network scenarios of diverse scales.

Energy efficient data transmission using multiobjective improved remora optimization algorithm for wireless sensor network with mobile sink

<span lang="EN-US">A wireless sensor network (WSN) is a collection of nodes fitted with small sensors and transceiver elements. Energy consumption, data loss, and transmission delays are the major drawback of creating mobile sinks. For instance, battery life and data latency might result in node isolation, which breaks the link between nodes in the network. These issues have been avoided by means of mobile data sinks, which move between nodes with connection issues. Therefore, energy aware multiobjective improved remora optimization algorithm and multiobjective ant colony optimization (EA-MIROA-MACO) is proposed in this research to improve the WSN’s energy efficiency by eliminating node isolation issue. MIRO is utilized to pick the optimal cluster heads (CHs), while multiobjective ant colony optimization (MACO) is employed to find the path through the CHs. The EA-MIROA-MACO aims to optimize energy consumption in nodes and enhance data transmission within a WSN. The analysis of EA-MIROA-MACO’s performance is conducted by considering the number of alive along with dead nodes, average residual energy, and network lifespan. The EA-MIROA-MACO is compared with traditional approaches such as mobile sink and fuzzy based relay node routing (MSFBRR) protocol as well as hybrid neural network (HNN). The EA-MIROA-MACO demonstrates a higher number of alive nodes, specifically 192, over the MSFBRR and HNN for 2,000 rounds.</span>

Tuning of PID Controller for Speed Control of DC-Motor by using Generalized Regression Neural Network and Invasive Weed Optimization

Tuning of PID Controller for Speed Control of DC-Motor by using Generalized Regression Neural Network and Invasive Weed Optimization

A brief study on rice diseases recognition and image classification: fusion deep belief network and S-particle swarm optimization algorithm

In the regions of southern Andhra Pradesh, rice brown spot, rice blast, and rice sheath blight have emerged as the most prevalent diseases. The goal of this research is to increase the precision and effectiveness of disease diagnosis by proposing a framework for the automated recognition and classification of rice diseases. Therefore, this work proposes a hybrid approach with multiple stages. Initially, the region of interest (ROI) is extracted from the dataset and test images. Then, the multiple features are extracted, such as color-moment-based features, grey-level cooccurrence matrix (GLCM)-based texture, and shape features. Then, the S-particle swarm optimization (SPSO) model selects the best features from the extracted features. Moreover, the deep belief network (DBN) model trained by SPSO is based on optimal features, which classify the different types of rice diseases. The SPSO algorithm also optimized the losses generated in the DBN model. The suggested model achieves a hit rate of 94.85% and an accuracy of 97.48% with the 10-fold cross-validation approach. The traditional machine learning (ML) model is significantly less accurate than the area under the receiver operating characteristic curve (AUC), which has an accuracy of 97.48%.

Overview on Efficient Resource Provisioning Approach Using Cloud Computing Environment

Abstract: The goal of this study is to examine the importance of effective resource management across different fields and the critical role that cloud computing apps have in accomplishing this effectiveness. To show how these various approaches and strategies can be used in real-world scenarios, the article looks at case studies and application usage. Optimizing resource utilization and performance in cloud computing requires effective scheduling, resource allocation, and other algorithms Using Cloud Simulation (cloud sim) Tool and other cloud environment tools. By adjusting to changing workloads, these algorithms improve fault tolerance, cost effectiveness, and load balancing. Furthermore, maintaining cloud service integrity depends heavily on algorithms for data processing, network optimization, and fault tolerance. Sufficient research in this area is essential to fully utilize cloud computing.

Robust optimization of geoenergy production using data-driven deep recurrent auto-encoder and fully-connected neural network proxy

Robust and efficient optimization of post-history well production schedule under history-matched geomodel known as closed-loop production management is crucial to achieve reasonable and reliable decision-making for subsurface geoenergy resources (e.g., oil/gas resources, geothermal, natural gas hydrate, etc.) recovery in petroleum industry. The procedure generally consists of a cycling implementation of geomodel history matching and production schedule optimization through repeatedly simulating high-fidelity reservoir models and therefore needs almost infeasible computation cost particularly for field-scale oil production scenario. Through hybridizing deep recurrent neural network and particle-swarm optimization algorithm, we present a novel data-driven computation framework for robust post-history production optimization without explicitly performing history matching. In the proposed framework, we develop a hybrid neural network proxy model, which combines recurrent autoencoder (RAE) and fully-connected neural network (FCNN), referred to as RAE-FCNN. The RAE structure can extract latent information from time-series history data, and the FCNN structure can map the combination of extracted latent variables by RAE and post-history well-control sequence to net present value (NPV). The trained RAE-FCNN proxy model can predict NPV values corresponding to the history measurements and user-specific post-history well controls. In the robust optimization framework, the averaged NPV over an ensemble of noisy history measurements are regarded as the objective function while the post-history well-controls are chosen as the decision variables. The proposed surrogate-based optimization framework has been demonstrated on two waterflooding reservoir production cases, e.g., a 2D synthetic Gaussian model and a 3D benchmaRk channelized EGG model.The decision variables considered in optimization are the injection rates with linear bound constraints at the injectors. In comparison with the optimization results by high-fidelity reservoir models, solely 1000 high-fidelity reservoir models for both cases are sufficient to train accurate proxy model and achieve satisfactory post-history production optimization results. Our proposed deep-learning-based optimization method can effectively make full use of history data to predict post-history oil well production without the need of computationally expensive history matching step, which enables us to achieve quick decision-making and risk evaluation for close-loop waterflooding reservoir production management and optimization.

Performance optimization of solid oxide electrolysis cell for syngas production by high temperature co-electrolysis via differential evolution algorithm with practical constraints

Co-electrolysis of water and carbon dioxide using solid oxide electrolysis cell combined with the catalytic reactor to produce renewable synthesis fuel is considered as a promising strategy for carbon dioxide utilization. However, in order to achieve a target product with a remarkable system-level efficiency, a certain proportion of carbon monoxide and hydrogen from solid oxide electrolysis cell must be sent to the catalytic reactor directly, which imposes practical constraints on the solid oxide electrolysis cell performance. Take this constraint into consideration, we proposed a performance design methodology that enables a rapid and effective optimization of the solid oxide electrolysis cell operating conditions. Firstly, a physical model is developed by integrating chemical equilibrium, electrochemistry, and energy conservation involved in the solid oxide electrolysis cell. Secondly, a physical model is utilized to generate a dataset for the development of neural network model and optimization algorithm. This approach allows for the efficient determination of optimal conditions while considering practical constraints. In the case study, under the constraint that the desired fuel is a long carbon chain fuel with a carbon dioxide conversion rate ranging from 80 % to 90 %, the highest efficiency achieved by the solid oxide electrolysis cell is optimized to 66.75 %. Moreover, this approach is extended to other two target fuels (dimethyl ether and methane) under different carbon dioxide conversion rate, and the optimal performance is designed. According to the results, there is a trade-off between high efficiency and high carbon dioxide conversion rate, as the low carbon dioxide conversion rate often leads to higher efficiency. This method allows for flexible design of operating conditions to meet different fuel requirements and guide practical applications.

Multi–materials topology optimization using deep neural network for coupled thermo–mechanical problems

In this paper, a density-based topology optimization method using neural networks is used to design domains composed of multi-materials under combined thermo–mechanical loading. The neural network produces the topology density field by minimizing a loss function defined for the problem. Length scale control is conducted using a Fourier space projection. JAX, a high-performance automatic differentiation (AD) Python library is used to build an end-to-end differentiable neural network. The model can handle the sensitivity analysis automatically using the backpropagation process (automatic differentiation of loss function) and the need for solving adjoint equations manual sensitivity analysis is removed. The optimization problem is solved by minimizing the loss function of the network and other optimization algorithms such as the Method of Moving Asymptotes (MMA) are not required. The performance of the method is demonstrated through several examples and compared with those obtained from the popular Solid Isotropic Material with Penalization (SIMP) method. The network is able to handle high-resolution re-sampling, resulting in a more refined and smooth variation of optimal topologies.

Accelerated fuzzy min–max neural network and arithmetic optimization algorithm for optimizing hyper-boxes and feature selection

Accelerated fuzzy min–max neural network and arithmetic optimization algorithm for optimizing hyper-boxes and feature selection

Online surface temperature prediction and abnormal diagnosis of lithium-ion batteries based on hybrid neural network and fault threshold optimization

Online diagnosis of abnormal temperature is vital to ensure the reliability and operation safety of lithium-ion batteries, and this study develops a hybrid neural network and fault threshold optimization algorithm for their online surface temperature prediction and abnormal diagnosis. To be specific, a hybrid neural network incorporating convolutional neural network and long short-term memory neural network is firstly employed to predict the battery temperature, and a residual monitor is designed to track the deviation between the measure and the prediction. Then, the acquired residual is compared with the fault threshold to diagnose whether the battery temperature is abnormal. Moreover, to improve the correctness and reliability of fault diagnosis, a fault threshold optimization algorithm based on the receiver operating characteristic curve is defined to automatically find the optimal fault threshold. The accuracy and reliability of temperature prediction is verified under various aged state and temperatures, as well as different battery types. The abnormal experiment validation on three datasets reveals that the proposed method can diagnose and unwind temperature fault warning timely and reliably. Additionally, the average execution time of each prediction and diagnosis is less than 3.5 ms, manifesting the real-time application capability of the proposed method.

Automatic Detection of Covid-19 in CT Images Using an Optimized Deep Neural Network Classifier

An exact and quick finding of Covid-19 patients plays a significant part in the initial period of medicinal treatment and prevention. Automatic recognition of COVID-19 cases using CT images may help lessen the effect of this infection on human civilization. In recent years, computer vision is the major solution for diagnosing the covid-19 disease by using CT images. Besides, many researchers had presented efficient artificial intelligence techniques for identifying Covid-19 disease. Nevertheless, the accuracy and time consumption of the model further to be improved. Thus, in this work, we propose a Covid-19 disease classification using a hybrid deep neural network with handcrafted features. The proposed approach consists of three stages namely, pre-processing, handcrafted feature extraction, and classification. Initially, the images are given to the pre-processing stage to remove the noise present in the input images. Then, we extract the hand-crafted features (Gray-Level Co-Occurrence Matrix (GLCM)) from each image. After that, the pre-processed image is given to the input of the optimized deep neural network classifier to classify an image as normal or abnormal. The proposed optimized deep neural network is a combination of a convolution neural network (CNN) and adaptive artificial jelly optimization (A2JO) algorithm. To enhance the performance of classifier accuracy, the extracted handcrafted features are fused with the hybrid deep neural network. The performance of the proposed approach was analyzed based on different metrics and performance compared with other techniques.

Two-layer neural network on infinite-dimensional data: global optimization guarantee in the mean-field regime *

The analysis of neural network optimization in the mean-field regime is important as the setting allows for feature learning. The existing theory has been developed mainly for neural networks in finite dimensions, i.e. each neuron has a finite-dimensional parameter. However, the setting of infinite-dimensional input naturally arises in machine learning problems such as nonparametric functional data analysis and graph classification. In this paper, we develop a new mean-field analysis of a two-layer neural network in an infinite-dimensional parameter space. We first give a generalization error bound, which shows that the regularized empirical risk minimizer properly generalizes when the data size is sufficiently large, despite the neurons being infinite-dimensional. Next, we present two gradient-based optimization algorithms for infinite-dimensional mean-field networks, by extending the recently developed particle optimization framework to the infinite-dimensional setting. We show that the proposed algorithms converge to the (regularized) global optimal solution, and moreover, their rates of convergence are of polynomial order in the online setting and exponential order in the finite sample setting, respectively. To the best of our knowledge, this is the first quantitative global optimization guarantee of a neural network on infinite-dimensional input and in the presence of feature learning.

ICEEMDAN-IWOA-BiLSTM Hybrid Algorithm-based Model for Short-term Load Forecasting

Given the uncertain, random, and volatile characteristics of power load, accurate forecasting of power load becomes challenging. To address this issue, this paper proposes an optimized prediction model by using a modified Adaptive White Noise Complete Integrated Empirical Modal Decomposition Algorithm (ICEEMDAN) and the improved Whale Optimization Algorithm (IWOA) for bidirectional short-term and long-term memory networks (BiLSTM). First of all, Australian power load data is preprocessed in this study. Then, the load data is decomposed into a series of sequences by using ICEEMDAN. After that, the improved whale algorithm is employed to analyze the BiLSTM super parameter optimization. Finally, each decomposed component is input into the IWOA-BiLSTM model for load forecasting. The results demonstrate that the proposed algorithm achieves higher accuracy in power load forecasting, compared to individual benchmark models and most portfolio models, indicating its applicability and practical value.

Effective Routing Using Multi-Objective Levy flight- Artificial Rabbit Optimization Algorithm for Wireless Mesh Networks

Effective Routing Using Multi-Objective Levy flight- Artificial Rabbit Optimization Algorithm for Wireless Mesh Networks

Deep reinforcement learning-based resilience enhancement strategy of unmanned weapon system-of-systems under inevitable interferences

Unmanned weapon system-of-systems (UWSoS) is a collection of unmanned weapon systems providing multiple interdependent capabilities to support the mission completion. The soaring number of interconnected systems makes UWSoS vulnerable in the face of inevitable uncertain interferences. Thus, devising an effective resilience enhancement strategy is critical to handling inevitable disruption events. However, preventive and protective methods are not all-inclusive because of the high uncertainty operation environment and the lack of autonomy. Hence, we studied the resilience enhancement problem of UWSoS from the recovery perspective based on deep reinforcement learning (DRL). First, a DRL-based resilience enhancement strategy framework is proposed, combining the graph convolution network and proximal policy optimization algorithm to extract the entities’ representation features and autonomously learn the resilience enhancement strategy to handle various interferences scenarios better. Subsequently, a collaboration action resilience contribution index-guided proximal policy optimization algorithm is proposed to improve training efficiency. Finally, extensive simulation experiments and comparisons with five similar algorithms demonstrate the effectiveness, adaptability, and superiority of the proposed strategy. This work could provide valuable scheduling schemes for decision-makers to guide the reliable operation of UWSoSs.

Early Warning of Red Tide of Phaeocystis globosa Based on Phycocyanin Concentration Retrieval in Qinzhou Bay, China

Phaeocystis globose (P. glo) are the most frequent harmful algae responsible for red tides in Qinzhou Bay, Guangxi. They pose a significant threat to the coastal marine ecosystem, making it essential to develop an efficient indicator method tailored to P. glo outbreaks. In remote sensing water quality monitoring, there is a strong correlation between P. glo and cyanobacteria, with phycocyanin (PC) serving as an indicator of cyanobacterial biomass. Consequently, existing research has predominantly focused on remote sensing monitoring of medium to high PC concentrations. However, it is still challenging to monitor low PC concentrations. This paper introduced the BP neural network (BPNN) and particle swarm optimization algorithm (PSO). It selects spectral bands and indices sensitive to PC concentrations and constructs a PC concentration retrieval model, in combination with meteorological factors, offering a comprehensive exploration of the indicative role of low PC concentrations in predicting P. glo red tide outbreaks in Qinzhou Bay. The results demonstrated that the PC concentration retrieval model, based on the backpropagation neural network optimized by the particle swarm optimization algorithm (PSO-BPNN), demonstrated better performance (MAE = 0.469, RMSE = 0.615). In Qinzhou Bay, PC concentrations were mainly concentrated around 2~5 μg/L. During the P. glo red tide event, the area with undetectable PC concentrations (PC < 0.04 μg/L) increased by 4.97 km2, with regions below 0.9 μg/L experiencing exponential growth. Considering the variations in PC concentrations along with meteorological factors, we proposed a straightforward early warning threshold for P. glo red tides: PC < 0.9 μg/L and T < 20 °C. This method, from a remote sensing perspective, analyzes the process of P. glo outbreaks, simplifies PC concentration monitoring, and provides a reasonably accurate prediction of the risk of P. glo red tide disasters.