Optimization Algorithm Research Articles

A short-term load demand forecasting: Levenberg–Marquardt (LM), Bayesian regularization (BR), and scaled conjugate gradient (SCG) optimization algorithm analysis

A short-term load demand forecasting: Levenberg–Marquardt (LM), Bayesian regularization (BR), and scaled conjugate gradient (SCG) optimization algorithm analysis

TOP2DFVT: An Efficient Matlab Implementation for Topology Optimization based on the Finite-Volume Theory.

The finite-volume theory has shown to be numerically efficient and stable for topology optimization of continuum elastic structures. The significant features of this numerical technique are the local satisfaction of equilibrium equations and the employment of compatibility conditions along edges in a surface-averaged sense. These are essential properties to adequately mitigate some numerical instabilities in the gradient version of topology optimization algorithms, such as checkerboard, mesh dependence, and local minima issues. Several computational tools have been proposed for topology optimization employing analysis domains discretized with essential features for finite-element approaches. However, this is the first contribution to offer a platform to generate optimized topologies by employing a Matlab code based on the finite-volume theory for compliance minimization problems. The Top2DFVT provides a platform to perform 2D topology optimization of structures in Matlab, from domain initialization for structured meshes to data post-processing. This contribution represents a significant advancement over earlier publications on topology optimization based on the finite-volume theory, which needed more efficient computational tools. Moreover, the Top2DFVT algorithm incorporates SIMP and RAMP material interpolation schemes alongside sensitivity and density filtering techniques, culminating in a notably enhanced optimization tool. The application of this algorithm to various illustrative cases confirms its efficacy and underscores its potential for advancing the field of structural optimization.

Optimized beamforming vector and antenna selection using autoencoder with Bayesian RNN and hybrid heuristic algorithm for MIMO systems

SummaryWireless communication systems depend on channel state information (CSI). However, the CSI collection is a significant barrier to achieving high performance. Present wireless communication models mainly rely on many antennas and related signal dispensation for increasing effectiveness. User mobility, the usage of greater frequencies, and a better number of antennas contribute to beamforming and beam management in these networks. Therefore, an improved hybrid heuristic algorithm is proposed here for providing optimized Joint beam forming and antenna selection (JBAS) in a multi‐input multi‐output (MIMO) model. The major novelty of the research work is to jointly perform the beam forming and antenna selection in MIMO that effectively enhanced the performance of the wireless transmission system. The major intention of this paper is to optimize the beamforming vectors and antennae by using the designed algorithm, where the spectral efficiency gets maximized. Initially, the data is collected by varying the number of users, locations, and sampling points, which is optimally selected by developing the hybrid reptile search‐based lion optimization algorithm (HRS‐LOA). Once it is collected, the relevant CSI is considered. This state of information is fed as input to the Auto Encoder with Bayesian Recurrent Neural Network (AE‐BRNN). Finally, through the network, the optimized value of the beamforming vector and antenna is obtained, which is useful to increase the sum rate/spectral efficiency. From the numerical results, the recommended model attained a spectral efficiency of 30 bits/s/Hz, which is higher than the BWO‐AE‐BRNN with 13%, RSO‐AE‐BRNN with 16%, RSA‐AE‐BRNN with 17%, and LA‐AE‐BRNN with 40% respectively.

Advanced parameter estimation for lithium-ion battery model using the information sharing group teaching optimization algorithm

Accurate battery modeling is crucial for optimizing the performance and safety of Lithium-ion batteries (LiBs), particularly in applications such as electric vehicles and smart grids. This paper introduces the Information Sharing Group Teaching Optimization Algorithm (ISGTOA), a novel human-based metaheuristic algorithm designed to estimate the 21 parameters of third-order Equivalent Circuit Model (ECM) for LiBs. Unlike existing methods, ISGTOA effectively manages the increased complexity of a model through advanced group teaching strategies and sophisticated information-sharing mechanisms inspired by classroom dynamics. We validated the effectiveness of ISGTOA using two distinct datasets—High Dynamic Profile (HDP) and Urban Dynamometer Driving Schedule (UDDS)—across various LiB chemistries (NCA and NMC) and temperature conditions (−5 °C, 25 °C and 45 °C). The results demonstrate that ISGTOA achieves superior parameter estimation accuracy and convergence speed compared to established and recent optimization methods such as TLBO, TLSBO, AISA, MTBO, and DTBO. Specifically, ISGTOA attained a minimum RMSE of 8.357 mV with rapid convergence and high stability in the HDP dataset and minimized RMSE to 10 mV within ten iterations at both 25 °C and 45 °C in the UDDS dataset. Additionally, ISGTOA exhibited high efficiency (up to 97.75 %) under varying thermal environments. This work not only introduces a novel algorithm for complex battery modeling but also sets the stage for future advancements in real-time battery management systems, emphasizing the importance of robust, versatile, and efficient parameter estimation techniques.

Knowledge-assisted hybrid optimization strategy of large-scale crude oil scheduling integrated production planning

As modern refinery crude oil scheduling scales up, traditional manual operations and independent optimization methods struggle with complexity and dynamics. This study proposes an empirically assisted integrated planning and scheduling optimization model.The integrated model is solved using a hybrid optimization algorithm combining mathematical programming and particle swarm optimization (MP/PSO). Long-term planning aims to minimize operational and transportation costs while maximizing refinery profits; short-term scheduling, based on initial long-term plans, aims to minimize unit switchovers. In the short-term scheduling phase, heuristic rules based on empirical operational knowledge generate a high-performing initial population to accelerate convergence. This strategy is crucial for enhancing refinery emergency response capabilities, ensuring stable operations, and improving economic benefits. Experimental results show that within a reasonable time frame, MP/PSO performs better than PSO and manual scheduling in large-scale crude oil scheduling scenarios.

Power Transformer Fault Diagnosis Based on Random Forest and Improved Particle Swarm Optimization–Backpropagation–AdaBoost

This paper proposes a novel fault diagnosis methodology for oil-immersed transformers to improve the diagnostic accuracy influenced by gas components in power transformer oil. Firstly, the Random Forest (RF) algorithm is utilized to evaluate and filter the raw data features, solving the problem of determining significant features in the dataset. Secondly, a multi-strategy Improved Particle Swarm Optimization (IPSO) is applied to optimize a double-hidden layer backpropagation neural network (BPNN), which overcomes the challenge of determining hyperparameters in the model. Four enhancement strategies, including SPM chaos mapping based on opposition-based learning, adaptive weight, spiral flight search, and crisscross strategies, are introduced based on traditional Particle Swarm Optimization (PSO) to enhance the model’s optimization capabilities. Lastly, AdaBoost is integrated to fortify the resilience of the IPSO-BP network. Ablation experiments demonstrate an enhanced convergence rate and model accuracy of IPSO. Case analysis using Dissolved Gas Analysis (DGA) samples compares the proposed IPSO–BP–AdaBoost model with other swarm intelligence optimization algorithms integrated with BPNN. The experimental findings highlight the superior diagnostic accuracy and classification performance of the IPSO–BP–AdaBoost model.

Coverage optimization for IoT-based network using Monarch Butterfly Optimization with greedy strategy and self-adaptive crossover operator

In the dynamic development of technology, the Internet of Things (IoT) stands as a driving force of innovation and opening new frontiers. Data collection and processing in IoT systems provide services to decision making in various fields including home automation, disaster management, healthcare, and so on. In such services, the IoT has certain limitations to perform the required tasks and it can cause extreme consequences in the network. One of the main challenge in IoT is to ensure maximum coverage by optimally placing the sensors. To address the coverage problem, a meta-heuristic algorithm Monarch Butterfly Optimization with greedy strategy and self-adaptive crossover operator (GCMBO) is proposed for Optimal Sensor Placement (OSP) in 3D monitoring region. The performance of proposed algorithm is evaluated by a series of simulation and it provide efficient results when compared to other similar existing optimization algorithms. Additionally, a statistical analysis (ANOVA and LSD post-hoc) is exhibited from the comparative results.

An efficient enhanced stacked auto encoder assisted optimized deep neural network for forecasting Dry Eye Disease

Meibomian Gland Dysfunction (MGD) and Dry Eye Disease (DED) comprise two of the most significant eye diseases, impacting millions of sufferers worldwide. Several etiological factors influence the early symptoms of DED. Early diagnosis and treatment of erectile dysfunction may significantly improve the Quality of Life (QoL) for people. The current study introduces the ESAE-ODNN, an improved stacked autoencoder-aided optimised deep neural network, as a new way to predict DED using feature selection (FS), feature extraction (FE), and classification. The approach described here is novel because it merges chaotic maps into FS, employs SLSTM-STSA for improved classification accuracy (CA), and optimizes with the adaptive quantum rotation of the Enhanced Quantum Bacterial Foraging Optimisation Algorithm (EQBFOA). The present study enhances prediction functions by extracting MGD-related features and complicated relationships from the DED dataset. To ensure essential feature identification, the ESAE minimizes irrelevant and redundant features. To predict the DED, the ESAE first applies FE and then implements an ODNN classifier. This method fine-tunes the ODNN framework to enhance the effectiveness of the classification. The proposed ESAE-ODNN classification system efficiently assists in the early diagnosis of DED. Combining advanced Deep Learning (DL) methods with optimization can help us understand MGD features better and sort the data with the best accuracy (96.34%). The experimental evaluation with relevant performance metrics indicates that the proposed method is efficient in diverse aspects: accurate identification, reduced complexity, and fine-tuned performance. The ESAE-ODNN’s robustness in handling intricate feature indications and high-dimensional data outperforms the existing state-of-the-art techniques.

Chemically tuning stability and superconductivity of Pd–H compounds: A routine to high temperature superconductivity under modest pressures

Motivated by searching room-temperature superconductors that could be realized near ambient conditions, palladium hydrides were chosen as the research subject considering that they can stably exist under ambient conditions, and Li as an electron donor for its outstanding performance in chemically tuning stability. A novel cubic phase structure of Li2PdH6 with a remarkably high estimated Tc of ∼165 K at 90 GPa was found using particle swarm optimization algorithm calculations. The superconducting behavior persists down to 10 GPa with a high Tc of 106.382 K. Even though the parent binary Pd–H system is not a good superconductor, the introduction of extra electrons breaks up the H2 molecules, inducing the increase of atomic hydrogen compared with parent hydride, which is necessary for outstanding superconducting behavior. The existence of relatively soft phonons associated with the H atoms in phonon dispersion curves is responsible for its high-Tc. Our results indicated that the doping of Li to binary hydrides, especially to binary hydrides with low-Tc that exist under ambient pressure, can produce robust phonon-mediated superconductivity. This may be a strategy to design and optimize room-temperature superconductors that can be synthesized under modest pressure. The findings may pave the way for realizing new high-Tc superconductors in experiments under lower pressure than recently documented superconducting hydrides.

Quantum-Based Maximum Likelihood Detection in MIMO-NOMA Systems for 6G Networks

As wireless networks advance toward the Sixth Generation (6G), which will support highly heterogeneous scenarios and massive data traffic, conventional computing methods may struggle to meet the immense processing demands in a resource-efficient manner. This paper explores the potential of quantum computing (QC) to address these challenges, specifically by enhancing the efficiency of Maximum-Likelihood detection in Multiple-Input Multiple-Output (MIMO) Non-Orthogonal Multiple Access (NOMA) communication systems, an essential technology anticipated for 6G. The study proposes the use of the Quantum Approximate Optimization Algorithm (QAOA), a variational quantum algorithm known for providing quantum advantages in certain combinatorial optimization problems. While current quantum systems are not yet capable of managing millions of physical qubits or performing high-fidelity, long gate sequences, the results indicate that QAOA is a promising QC approach for radio signal processing tasks. This research provides valuable insights into the potential transformative impact of QC on future wireless networks. This sets the stage for discussions on practical implementation challenges, such as constrained problem sizes and sensitivity to noise, and opens pathways for future research aimed at fully harnessing the potential of QC for 6G and beyond.

Near-Infrared Dual-Range Methane Sensor Using the OAIC-HC Mode Based on VMD-SG-Assisted Optical Noise Suppression.

Based on tunable diode laser absorption spectroscopy and off-axis integrated cavity output spectroscopy, a dual-range methane hybrid sensor was constructed utilizing the overtone absorption band of CH4 gas molecules at 1653.7 nm. By simultaneously utilizing an off-axis integrated cavity and Herriott cell with an effective absorption path of 11 and 405 m, respectively, the two received photoelectric signals are decomposed into different frequency components by VMD and then reconstructed after SG filtering. Applying the global optimization algorithm DA-ELM to CH4 concentration inversion, the correlation coefficient R2 is as high as 0.9995. Through long-term stability verification, the instrument's standard deviation at 1 ppm is 27 ppb. After Allan-Werle deviation analysis, the sensor's limit of detection is 2.298 ppb at an integration time of 138 s. Using the self-developed sensor, the detection of dual-range trace CH4 gas is achieved, enabling a dynamic detection range of trace CH4 gas ranging from 400 ppb to 1000 ppm. The sensor realizes dual-range methane trace detection and actively controls methane emissions to improve environmental quality while taking into account the safety benefits of reducing production accidents.

A Coupled Model for Forecasting Spatiotemporal Variability of Regional Drought in the Mu Us Sandy Land Using a Meta-Heuristic Algorithm

Vegetation plays a vital role in terrestrial ecosystems, and droughts driven by rising temperatures pose significant threats to vegetation health. This study investigates the evolution of vegetation drought from 2010 to 2024 and introduces a deep-learning-based forecasting model for analyzing regional spatial and temporal variations in drought. Extensive time-series remote-sensing data were utilized, and we integrated the Temperature–Vegetation Dryness Index (TVDI), Drought Severity Index (DSI), Evaporation Stress Index (ESI), and the Temperature–Vegetation–Precipitation Dryness Index (TVPDI) to develop a comprehensive methodology for extracting regional vegetation drought characteristics. To mitigate the effects of regional drought non-stationarity on predictive accuracy, we propose a coupling-enhancement strategy that combines the Whale Optimization Algorithm (WOA) with the Informer model, enabling more precise forecasting of long-term regional drought variations. Unlike conventional deep-learning models, this approach introduces rapid convergence and global search capabilities, utilizing a sparse self-attention mechanism that improves performance while reducing model complexity. The results demonstrate that: (1) compared to the traditional Transformer model, test accuracy is improved by 43%; (2) the WOA–Informer model efficiently handles multi-objective forecasting for extended time series, achieving MAE (Mean Absolute Error) ≤ 0.05, MSE (Mean Squared Error) ≤ 0.001, MSPE (Mean Squared Percentage Error) ≤ 0.01, and MAPE (Mean Absolute Percentage Error) ≤ 5%. This research provides advanced predictive tools and precise model support for long-term vegetation restoration efforts.

Based on improved crayfish optimization algorithm cooperative optimal scheduling of multi-microgrid system

In order to solve the influence of the complex interaction relationships among subjects on the system solution accuracy and speed of the Multi-Microgrid system under the high penetration rate of new energy. Firstly, the paper establishes the bi-level optimal scheduling Stackelberg game model based on shared energy storage, considering the inter-subject interaction in MMG. Subsequently, based on the four improvement methods of Chaotic Map, Quantum Behavior, Gaussian Distribution, and Nonlinear Control Strategy, the Chaotic Gaussian Quantum Crayfish Optimization Algorithm is proposed to solve the optimization scheduling model. The improved algorithm exhibits superior initial solutions and enhanced search capability. In comparison to the original algorithm, the relative errors of the CGQCOA optimization outcomes are 98%, 20.96%, 98.74% and 16.55%, respectively, enhancing the model-solving accuracy and the speed of convergence to the optimal solution. Finally, the simulation demonstrates that the revenue of Microgrid 1, Microgrid 2, and Microgrid 3 have increased by 0.73%, 1.17%, and 1.04%, respectively. Concurrently, the penalty cost of pollutant emission has decreased by 5.9%, 11.5%, and 12.68%, respectively. Furthermore, the revenue of the shared storage have increased by 1.91%. These findings validate the efficacy of the methodology proposed in enhancing the revenue of the various subjects and reducing pollutant gas emission.

An error-corrected deep Autoformer model via Bayesian optimization algorithm and secondary decomposition for photovoltaic power prediction

Accurate PV power prediction is crucial for stable grid operation and rational dispatch. However, due to the instability of PV power generation, PV power prediction still has great challenges. Therefore, an Autoformer model based on secondary decomposition, Bayesian optimization and error correction for PV power prediction. In order to reduce the complexity of the data and fully extract the features, two decomposition methods are employed. First, empirical mode decomposition (EMD) is applied to decompose the PV power series at the first level. Then, the sample entropy (SE) is introduced to measure the complexity of each component, and the variational mode decomposition (VMD) is employed to implement secondary decomposition of the component with the highest complexity. Secondly, a Bayesian optimization algorithm enhanced Autoformer model is developed for predicting each component, and the predicted component results are aggregated to obtain preliminary PV power prediction results. Finally, the preliminary prediction results are error corrected using a least squares support vector machine. A four-month PV dataset from a PV power plant in Hangzhou, China is utilized to validate the effectiveness of the proposed model. The experimental results show that the model after primary decomposition is superior to the single model, and the prediction accuracy is substantially improved after secondary decomposition. The proposed model has the best prediction performance in predicting the PV power for different seasons, which shows good robustness.

Optimization of machining efficiency and side quality in irregular sheet metal parts milling based on improved multi-objective seagull optimization algorithm

Optimization of machining efficiency and side quality in irregular sheet metal parts milling based on improved multi-objective seagull optimization algorithm

A rapid-convergent particle swarm optimization approach for multiscale design of high-permeance seawater reverse osmosis systems

Directly solving sophisticated partial differential equation constrained optimization problems is not only extremely time-consuming, but also very hard to find unique optimal solutions. Here, we propose stable and efficient surrogate models for seawater reverse osmosis desalination processes that enable thorough quantitative description of hydrodynamics and local transport characteristics in narrow flow channels. Without iteratively solving complex multi-physics simulation problem taking several hours, the proposed multi-scale design optimization framework significantly reduces the problem complexity by computing the surrogate models in seconds. Moreover, a fast-converging active subspace particle swarm optimization framework is proposed to address the optimal design problem. Compared to the standard particle swarm optimization algorithm, the proposed method enhances the average optimum by 14% and the standard deviation of optimum results for multiple runs is reduced by no less than ten times. The optimized desalination system achieves 9% reduction on energy consumption and 30% improvement on water production efficiency.

Multi‐Objective Optimization and Experimental Research of Ship Form Based on Improved Bare‐Bones Multi‐Objective Particle Swarm Optimization Algorithm

ABSTRACTShip form optimization poses a complex and high‐dimensional engineering challenge. Therefore, when conducting multi‐objective optimization research of ship forms, traditional intelligent optimization algorithms are prone to falling into local optima solution and difficult to converge. In order to effectively improve the diversity and convergence performance of the algorithm, this paper improves the bare‐bones multi‐objective particle swarm optimization (BBMOPSO) algorithm by dynamically adjusting the local and global search step sizes, and verifies the algorithm's reliability through standard function testing. Then, a multi‐objective optimization design framework with high efficiency and high integration is constructed. Taking DTMB 5512 as the research case, Free Form Deformation (FFD) method is used for hull deformation, and the proposed algorithm is used for multi‐objective optimization of resistance performance and motion response. Ship model tests were conducted on the DTMB 5512's original hull. And the numerical simulations were compared with the ship model tests. Finally, under the constructed multi‐objective optimization design framework, satisfactory solutions were obtained through the improved algorithm, which confirms the effectiveness and practicality of the improved algorithm. The results show that the algorithm improved in this paper can provide some theoretical basis and technical support for green ship design and low‐carbon shipping.

Optimal Control of FSBB Converter with Aquila Optimizer-Based PID Controller

This paper presents a new methodology for determining the optimal coefficients of a PID controller for a four-switch buck–boost (FSBB) converter. The main objective of this research is to improve the performance of FSBB converters by fine-tuning the parameters of the PID controller using the newly developed Aquila Optimizer (AO). PID controllers are widely recognized for their simple yet effective control in FSBB converters. However, to further improve the efficiency and reliability of the control system, the PID control parameters must be optimized. In this context, the application of the AO algorithm proves to be a significant advance. By optimizing the PID coefficients, the dynamic responsiveness of the system can be improved, thus reducing the response time. In addition, the robustness of the control system is enhanced, which is essential to ensure stable and reliable operation under varying conditions. The use of AOs plays a key role in maintaining system stability and ensuring the proper operation of the control system even under challenging conditions. In order to demonstrate the effectiveness and potential of the proposed method, the performance of the AO-optimized PID controller was compared with that of PID controllers tuned by other optimization algorithms in the same test environment. The results show that the AO outperforms the other optimization algorithms in terms of dynamic response and robustness, thus validating the efficiency and correctness of the proposed method. This work highlights the advantages of using the Aquila Optimizer in the PID tuning of FSBB converters, providing a promising solution for improving system performance.

Prediction of Capillary Pressure Curves Based on Particle Size Using Machine Learning

Capillary pressure curves are usually obtained through mercury injection experiments, which are mainly used to characterize pore structures. However, mercury injection experiments have many limitations, such as operation danger, a long experiment period, and great damage to the sample. Therefore, researchers have tried to predict capillary pressure data based on NMR data, but NMR data are expensive and unstable to obtain. This study aims to accurately predict capillary pressure curves. Based on rock particle size data, various machine learning methods, such as traditional machine learning and artificial neural networks, are used to build prediction models and predict different types of capillary pressure curves, aiming at studying the best prediction algorithm. In addition, through adjusting the amount of particle size characteristic data, the best amount of particle size characteristic data is explored. The results show that three correlation coefficients of the four optimal algorithms can reach more than 0.92, and the best performance is obtained using the Levenberg–Marquardt method. The prediction performance of this algorithm is excellent, with the three correlation coefficients being all higher than 0.96 and the root mean square error being only 5.866. When partial particle size characteristics are selected, the training performance is gradually improved with an increase in the amount of feature data, but it is far less than the performance of using all the features. When the interpolation increases the particle size characteristics, the best performance is achieved when the feature data volume is 50 groups and the root mean square error is the smallest, but the Kendall correlation coefficient decreases. This study provides a new way to obtain capillary pressure data accurately.

Prospective Connectomic-Based Deep Brain Stimulation Programming for Parkinson's Disease.

Efficacy of deep brain stimulation (DBS) relies on accurate lead placement as well as optimization of the stimulation parameters. Although clinical software tools are now available, programming still largely relies on a monopolar review, a tedious process for both patients and programmers. This study investigates the safety and feasibility of prospective automated connectomic DBS programming (automated connectomic programming [ACP]), focusing on the recruitment of specific white matter pathways. After DBS implantation, a detailed connectomic DBS model in patient-specific space was developed for each study participant. A driving-force model was used to quantify pathway recruitment across 2400 different DBS settings. Optimization algorithms maximized recruitment of therapeutic pathways while minimizing recruitment of side-effect pathways. Thirteen subjects were enrolled in two study phases that compared DBS settings derived from ACP to standard clinical DBS settings. Nine patients underwent reprogramming with ACP (5 globus pallidus interna [GPi], 4 subthalamic nucleus [STN]). Four patients underwent initial programming with ACP (3 GPi, 1 STN). All patients tolerated ACP without persistent side effects. In the reprogramming cohort, 3 patients preferred their ACP program, and 1 patient felt it was comparable to their clinical program. Unified Parkinson's Disease Rating Scale, Part III, scores for the initial ACP cohort (3 GPi, 1 STN) improved by an average of 43.5% (40.4-52.6 ± 5.6%). ACP appeared clinically safe and feasible. It provided reasonable motor improvement, which can be further optimized with subsequent clinical adjustment. Additional investigation is required to refine the optimization algorithm and to quantify the clinical benefit of ACP in a larger cohort. © 2024 International Parkinson and Movement Disorder Society.