Optimization Algorithm Research Articles

Robust Boundary Output-Feedback Control of a Reaction-Diffusion Equation with In-Domain Disturbances

Output feedback control design for a class of reaction-diffusion equations with Dirichlet anti-collocated sensing and actuation subject to in-domain disturbances is addressed. Within this setting, we design a finite-dimensional dynamic output feedback controller ensuring closed-loop exponential stability and input-output stability with an explicit estimate of the input-output gain. The approach is based on the spectral decomposition of the open-loop infinite-dimensional system and on the use of a suitable Lyapunov functional candidate. Sufficient conditions in the form of matrix inequalities are given to ensure closed-loop stability. These conditions are shown to be always feasible and are employed to devise an optimal controller design algorithm based on the solutions to some linear matrix inequalities.

Forecast Natural Gas Price by an Extreme Learning Machine Framework Based on Multi-Strategy Grey Wolf Optimizer and Signal Decomposition

Natural gas is one of the most important sources of energy in modern society. However, its strong volatility highlights the importance of accurately forecasting natural gas price trends and movements. The nonlinear nature of the natural gas price series makes it difficult to capture. Therefore, we propose a forecasting framework based on signal decomposition and intelligent optimization algorithms to predict natural gas prices. In this forecasting framework, we implement point, probability interval, and quantile interval forecasting. First, the natural gas price sequence is decomposed into multiple Intrinsic Mode Functions (IMFs) using the Ensemble Empirical Mode Decomposition (EEMD) technique. Each decomposed sequence is then predicted using an optimized Extreme Learning Machine (ELM), and the individual results are aggregated as the final result. To improve the efficiency of the intelligent algorithm, a Multi-Strategy Grey Wolf Optimizer (MSGWO) is developed to optimize the hidden layer matrices of the ELM. The experimental results prove that the proposed framework not only provides more reliable point forecasts with good nonlinear adaptability but also describes the uncertainty of natural gas price series more accurately and completely.

An accelerometer outdoor calibration strategy based on a Hybrid Boundary Dynamic Optimization algorithm

Abstract Inertial navigation technology stands as a prominent and widely adopted solution in the realm of vehicular applications that demand meticulous and reliable navigation. In these applications, accelerometers serve as a vital source of data for Inertial Navigation System (INS), supplying crucial information in acceleration. However, when the navigation system is used in an outdoor setting over an extended period of time, the complex dynamic environment causes the nonlinear error parameters of the accelerometers to change. This results in the output of data containing errors, which in turn leads to a reduction in navigation accuracy. The current calibration methods for accelerometers rely heavily on the measurement equipment, such as high-precision tri-axial rotary tables or centrifuges. This results in the existing calibration methods being limited to laboratory settings, with the application to outdoor scenarios being challenging. Swarm intelligent optimization algorithms offer a novel approach to this problem. This paper proposes an accelerometer calibration strategy based on a Hybrid Boundary Dynamics Optimization (HBDO) algorithm, with the aim of achieving high-precision calibration of accelerometers in the absence of accurate and expensive measurement equipment. The results of extensive experimentation demonstrate that the accelerometer calibration strategy based on the HBDO algorithm is equivalent to the system-level calibration method using precision measurement equipment in static navigation experiments, which achieve equivalent navigation accuracy.

A Hybrid Method of Corporate Financial Performance Prediction Based on Common Model and Convolutional Neural Network

Performance data are affected by market environment, business demand, policy changes, and other factors, resulting in an obvious nonlinear relationship between the influencing factors across the period. The convolutional neural network (CNN) method cannot deal with such nonlinear relationship data directly, and the oscillation caused by stochastic gradient descent leads to local optimization, resulting in poor prediction results. Therefore, the interperiod impact prediction method of corporate financial performance based on the Public model is proposed. Based on the enterprise’s financial data, the method involves several steps. First, it extracts the principal component data pertinent to the enterprise’s intellectual capital through principal component analysis. Subsequently, the public model is employed to measure the intellectual capital of the enterprise, utilizing the extracted principal component data related to intellectual capital, such as coefficients and the coefficient of intellectual capital efficiency, among other indicators. Last, the intellectual capital indicators of various enterprises are combined with those of the current enterprise, taking into account the coefficients of the current enterprise’s value-added intellectual capital. Using different enterprise intellectual capital indicators and current enterprise financial performance data as input, the method incorporates machine learning algorithms within a CNN model for iterative feature extraction operations. Additionally, the Adam optimization algorithm is introduced to adaptively adjust the learning rate of the CNN model, thereby enhancing its anti-interference ability. The output of this process is the prediction results detailing the interperiod impact on the enterprise’s financial performance. Experimental results demonstrate that this method can effectively extract the principal components from the enterprise’s financial data and accurately measure the current enterprise’s intellectual capital. Furthermore, it predicts the interperiod impact of the enterprise’s financial performance over the next 12 months, based on factors such as capital utilization efficiency and return on net assets, related to the enterprise’s intellectual capital. The application of this method yields superior results.

Enhancing network lifetime in WSNs through coot algorithm-based energy management model.

Enhancing network lifetime in WSNs through coot algorithm-based energy management model.

Sustainable water allocation under climate change: Deep learning approaches to predict drinking water shortages.

Sustainable water allocation under climate change: Deep learning approaches to predict drinking water shortages.

Optimizing energy constrained target localization and tracking with radial bias and seeker optimization algorithms in wireless sensor networks.

Optimizing energy constrained target localization and tracking with radial bias and seeker optimization algorithms in wireless sensor networks.

Developing an automatic gripping learning system for a robotic arm by integrating a convolutional neural network and optimization algorithms

Developing an automatic gripping learning system for a robotic arm by integrating a convolutional neural network and optimization algorithms

High-throughput design strategy for creep-resistance steel using machine learning and optimization algorithm

High-throughput design strategy for creep-resistance steel using machine learning and optimization algorithm

Prediction of barite scale formation and inhibition in hydrocarbon reservoirs using AI modeling: Focus on different optimization algorithms

Prediction of barite scale formation and inhibition in hydrocarbon reservoirs using AI modeling: Focus on different optimization algorithms

Traffic congestion predictor: A spatiotemporal graph neural network with congestion-conditional adaptive mechanism and optimization algorithm

Traffic congestion predictor: A spatiotemporal graph neural network with congestion-conditional adaptive mechanism and optimization algorithm

An enhanced tree-seed algorithm for global optimization and neural architecture search optimization in medical image segmentation

An enhanced tree-seed algorithm for global optimization and neural architecture search optimization in medical image segmentation

An efficient history-guided surrogate models-assisted niching evolutionary algorithm for expensive multimodal optimization

An efficient history-guided surrogate models-assisted niching evolutionary algorithm for expensive multimodal optimization

A comparison-relationship-surrogate evolutionary algorithm for multi-objective optimization

A comparison-relationship-surrogate evolutionary algorithm for multi-objective optimization

Global information enhanced adaptive bald eagle search algorithm for photovoltaic system optimization

Global information enhanced adaptive bald eagle search algorithm for photovoltaic system optimization

Reinforcement and opposition-based learning enhanced weighted mean of vectors algorithm for global optimization and feature selection

Reinforcement and opposition-based learning enhanced weighted mean of vectors algorithm for global optimization and feature selection

A Two-Population Algorithm for Large-Scale Multiobjective Optimization Based on Fitness-Aware Operator and Adaptive Environmental Selection

A Two-Population Algorithm for Large-Scale Multiobjective Optimization Based on Fitness-Aware Operator and Adaptive Environmental Selection

EAEFA-R: Multiple learning-based ensemble artificial electric field algorithm for global optimization

EAEFA-R: Multiple learning-based ensemble artificial electric field algorithm for global optimization

Linear algorithm for data retrieval performance optimization in self-encryption hybrid data centers

Contemporary data centers implement hybrid storage systems that consist of layers from solid-state drives (SSDs) and hard disk drives (HDDs). Due to their high data retrieval speed, SSDs layer is used to store important data blocks that have features like high frequency of access. To boost their security level, many of such systems implement self-encryption algorithms like advanced encryption standard (AES), Blowfish, and triple data encryption standard (3DES) with different key sizes that vary in their complexity and their decryption latency whenever a block is requested for read. Frequently accessed data blocks with increased decryption latencies are better to be migrated to the SSDs layer to decrease their retrieval latency. In this paper, we introduce a linear complexity algorithm hybrid self-encryption storage data migration (HSESM) that migrates important data blocks that requires long decryption latencies from the HDDs layer to the SSDs one. Performance evaluation shows that HSESM data migration process can reduce data blocks read latencies in 13.71%-23.61% under worst-case scenarios.

Local search-based online learning algorithm for shape and cross-section optimization of free-form single-layer reticulated shells

Local search-based online learning algorithm for shape and cross-section optimization of free-form single-layer reticulated shells