Optimization Algorithm Research Articles

Groundwater level prediction using modified recurrent neural network combined with meta-heuristic optimization algorithm

Groundwater level prediction using modified recurrent neural network combined with meta-heuristic optimization algorithm

Predicting carbon dioxide emissions using deep learning and Ninja metaheuristic optimization algorithm

This paper provides a novel approach to estimating CO₂ emissions with high precision using machine learning based on DPRNNs with NiOA. The data preparation used in the present methodology involves sophisticated stages such as Principal Component Analysis (PCA) as well as Blind Source Separation (BSS) to reduce noise as well as to improve feature selection. This purified input dataset is used in the DPRNNs model, where both short and long-term temporal dependencies in the data are captured well. NiOA is utilized to tune those parameters; as a result, the prediction accuracy is quite spectacular. Experimental results also demonstrate that the proposed NiOA-DPRNNs framework gets the highest value of R2 (0.9736), lowest error rates and fitness values than other existing models and optimization methods. From the Wilcoxon and ANOVA analyses, one can approve the specificity and consistency of the findings. Liebert and Ruple firmly rethink this rather simple output as a robust theoretic and empirical framework for evaluating and projecting CO2 emissions; they also view it as a helpful guide for policymakers fighting global warming. Further study can build up this theory to include other greenhouse gases and create methods enabling instantaneous tracking for sophisticated and responsive approaches.

Tradeoffs Between Convergence Rate and Noise Amplification for Momentum-Based Accelerated Optimization Algorithms

Tradeoffs Between Convergence Rate and Noise Amplification for Momentum-Based Accelerated Optimization Algorithms

Multi-objective optimization of grinding process parameters for complicated worm space surface based on the grey wolf optimization algorithm

Multi-objective optimization of grinding process parameters for complicated worm space surface based on the grey wolf optimization algorithm

Seasonal auto-regressive integrated moving average with bidirectional long short-term memory for coconut yield prediction

Crop yield prediction helps farmers make informed decisions regarding the optimal timing for crop cultivation, taking into account environmental factors to enhance predictive accuracy and maximize yields. The existing methods require a massive amount of data, which is complex to acquire. To overcome this issue, this paper proposed a seasonal auto-regressive integrated moving average-bidirectional long short-term memory (SARIMA-BiLSTM) for coconut yield prediction. The collected dataset is preprocessed through a label encoder and min-max normalization is employed to change non-numeric features into numerical features and enhance model performance. The preprocessed features are selected through an adaptive strategy-based whale optimization algorithm (AS-WOA) to avoid local optima issues. Then, the selected features are given to the SARIMA-BiLSTM to predict the coconut yields. The proposed SARIMA-BiLSTM is adaptable to handling a widespread of various seasonal patterns and captures spatial features. The SARIMA-BiLSTM performance is estimated through the coefficient of determination (R2), mean absolute error (MAE), mean squared error (MSE), and root mean square error (RMSE). SARIMA-BiLSTM attains 0.84 of R2, 0.056 of MAE, 0.081 of MSE, and 0.907 of RMSE which is better when compared to existing techniques like multilayer stacked ensemble, convolutional neural network and deep neural network (CNN-DNN) and autoregressive moving average (ARIMA).

Research on building energy-saving based on GA-BP coupled improved multi-objective whale optimization algorithm

Research on building energy-saving based on GA-BP coupled improved multi-objective whale optimization algorithm

Golden eagle optimization algorithm embedded in gated Kolmogorov-Arnold network for transient stability preventive control of power systems

Golden eagle optimization algorithm embedded in gated Kolmogorov-Arnold network for transient stability preventive control of power systems

Two-dimensional bin-packing problem with conflicts and load balancing: A hybrid chaotic and evolutionary particle swarm optimization algorithm

Two-dimensional bin-packing problem with conflicts and load balancing: A hybrid chaotic and evolutionary particle swarm optimization algorithm

A gradient-based approach to fast and accurate head motion compensation in cone-beam CT.

Cone-beam computed tomography (CBCT) systems, with their flexibility, present a promising avenue for direct point-of-care medical imaging, particularly in critical scenarios such as acute stroke assessment. However, the integration of CBCT into clinical workflows faces challenges, primarily linked to long scan duration resulting in patient motion during scanning and leading to image quality degradation in the reconstructed volumes. This paper introduces a novel approach to CBCT motion estimation using a gradient-based optimization algorithm, which leverages generalized derivatives of the backprojection operator for cone-beam CT geometries. Building on that, a fully differentiable target function is formulated which grades the quality of the current motion estimate in reconstruction space. We drastically accelerate motion estimation yielding a 19-fold speed-up compared to existing methods. Additionally, we investigate the architecture of networks used for quality metric regression and propose predicting voxel-wise quality maps, favoring autoencoder-like architectures over contracting ones. This modification improves gradient flow, leading to more accurate motion estimation. The presented method is evaluated through realistic experiments on head anatomy. It achieves a reduction in reprojection error from an initial average of 3 mm to 0.61 mm after motion compensation and consistently demonstrates superior performance compared to existing approaches. The analytic Jacobian for the backprojection operation, which is at the core of the proposed method, is made publicly available. In summary, this paper contributes to the advancement of CBCT integration into clinical workflows by proposing a robust motion estimation approach that enhances efficiency and accuracy, addressing critical challenges in time-sensitive scenarios.

Particulate matter source localization in dynamic indoor environments: Bridging simulation-experimentation gaps with a 3D multi-robot system.

Indoor particulate matter (PM) threatens human health, compromises product quality and yield, and poses safety risks. Dynamic indoor environments introduce unpredictable airflow changes and complex PM behavior, posing significant challenges for accurate PM source localization by mobile robots. This study introduces our self-developed multi-robot system for three-dimensional (3D) concentration detection, bridging the gap between simulation and real-world applications. A total of 225 experiments across 15 cases were conducted in a dynamic ventilation environment, enabling a comprehensive analysis of airflow dynamics and PM behavior. The improved whale optimization algorithm (IWOA) was compared in 3D and 2D scenarios and further evaluated against the improved particle swarm optimization (IPSO) method under 3D conditions. The adaptability of IWOA_3D to variations in PM size, release rate, source location, and accuracy standard was also investigated. Using the success rate as a key evaluation criterion, IWOA_3D demonstrated strong adaptability to variations in source height, localizing PM2.5 at 1.05 m with a 73.3% success rate, despite limitations related to PM size and source location. These findings highlight the importance of selecting appropriate PM sensor readings and optimizing localization strategies in dynamic indoor environments, while demonstrating the practical effectiveness of the IWOA_3D method.

Design-manufacturing-evaluation integrated of microwave absorption metastructure based on large variation genetic-ant colony fusion optimization algorithm and additive manufacturing

Design-manufacturing-evaluation integrated of microwave absorption metastructure based on large variation genetic-ant colony fusion optimization algorithm and additive manufacturing

Event-triggered predefined-time distributed optimization algorithm based on zero-gradient-sum

Event-triggered predefined-time distributed optimization algorithm based on zero-gradient-sum

Best Subset Solution Path for Linear Dimension Reduction Models Using Continuous Optimization.

The selection of best variables is a challenging problem in supervised and unsupervised learning, especially in high-dimensional contexts where the number of variables is usually much larger than the number of observations. In this paper, we focus on two multivariate statistical methods: principal components analysis and partial least squares. Both approaches are popular linear dimension-reduction methods with numerous applications in several fields including in genomics, biology, environmental science, and engineering. In particular, these approaches build principal components, new variables that are combinations of all the original variables. A main drawback of principal components is the difficulty to interpret them when the number of variables is large. To define principal components from the most relevant variables, we propose to cast the best subset solution path method into principal component analysis and partial least square frameworks. We offer a new alternative by exploiting a continuous optimization algorithm for best subset solution path. Empirical studies show the efficacy of our approach for providing the best subset solution path. The usage of our algorithm is further exposed through the analysis of two real data sets. The first data set is analyzed using the principle component analysis while the analysis of the second data set is based on partial least squareframework.

Phase prediction in laser-clad high-entropy alloy coatings through metaheuristic optimization algorithms and interpretable machine learning

Phase prediction in laser-clad high-entropy alloy coatings through metaheuristic optimization algorithms and interpretable machine learning

A note on the computation of multi-revolution NRHO under the ephemeris model

A note on the computation of multi-revolution NRHO under the ephemeris model

A Direct Optimization Algorithm for Input-Constrained MPC

A Direct Optimization Algorithm for Input-Constrained MPC

A Novel Hybrid Multi-Objective Optimization Algorithm and Its Application to Designs of Electromagnetic Devices

A Novel Hybrid Multi-Objective Optimization Algorithm and Its Application to Designs of Electromagnetic Devices

PictureGuard: Enhancing Software-Defined Networking–Internet of Things Security with Novel Image-Based Authentication and Artificial Intelligence-Powered Two-Stage Intrusion Detection

Software-defined networking (SDN) represents a transformative approach to network management, enabling the centralized and programmable control of network infrastructure. This paradigm facilitates enhanced scalability, flexibility, and security in managing complex systems. When integrated with the Internet of Things (IoT), SDN addresses critical challenges such as security and efficient network management, positioning the SDN-IoT paradigm as an emerging and impactful technology in modern networking. The rapid proliferation of IoT applications has led to a significant increase in security threats, posing challenges to the safe operation of IoT systems. Consequently, SDN-IoT-based applications and services have been widely adopted to address these issues and challenges. However, this platform faces critical limitations in ensuring scalability, optimizing energy consumption, and addressing persistent security vulnerabilities. To overcome these issues, we proposed a secure SDN-IoT environment for intrusion detection and prevention using virtual blockchain (V-Block). Initially, IoT users are registered and authenticated to the shadow blockchain nodes using a picture-based authentication mechanism. After that, authenticated user flows validation was provided by considering effective metrics utilizing the Trading-based Evolutionary Game Theory (TEGT) approach. Then, we performed a local risk assessment based on evaluated malicious flows severity and then the attack graph was constructed using an Isomorphism-based Graph Neural Network (IGNN) model. Further, multi-controllers were placed optimally using fox optimization algorithm. The generated global paths were securely stored in the virtual blockchain Finally, the two agents in the multi-controllers were responsible for validating and classifying the incoming suspicious flow packets into normal and malicious packets by considering the operative metrics using the Dueling Deep Q Network (DDQN) algorithm. The presented work was conducted by Network Simulator-3.26 and the different performance matrices were used to itemize the suggested V-Block model based on its malicious traffic, attack detection rate, link failure rate, anomaly detection rate, and scalability.

Maximizing efficiency and performance of water distribution systems through the implementation of optimization algorithms: A comprehensive analysis of valve and chlorine booster placement and management

Maximizing efficiency and performance of water distribution systems through the implementation of optimization algorithms: A comprehensive analysis of valve and chlorine booster placement and management

Hybridization of wind farms with co-located PV and storage installations

Hybridization of wind farms with co-located PV and storage installations