Optimization Algorithm Research Articles

Integrated trajectory optimization for UAV-enabled wireless powered MEC system with joint energy consumption and AoI minimization

This paper studies an unmanned aerial vehicle (UAV)-enabled wireless powered mobile edge computing (MEC) system, where a UAV, equipped with RF Chains and MEC servers, can sustainably provide wireless energy for charging Internet of Things (IoT) devices and executing computing tasks from these devices while hovering at designated hover points. Our goal is to minimize the weighted sum of energy consumption and Age of information (AoI) in this system, which depended on the UAV’s hovering time at designated points and its flying time. To achieve this, we jointly optimize the deployment of hover points and the visiting order of these points by the UAV. It is NP hard and mixed-integer non-convex which is difficult to solve by traditional methods. To tackle this problem, we present a trajectory optimization algorithm for joint energy consumption and AoI (TOJEA), which consists of two phases. In the first phase, an Equilibrium Optimizer (EO) algorithm with a variable individual size via its coding and updating strategies, in which each particle (individual) with its concentration (position) represents a target solution i.e. the whole deployment of hover points, is proposed to optimize the number and locations of hover points. Based on the deployment of hover points, a low-complexity greedy algorithm is adopted in the second stage to generate the optimal visiting order for the UAV. Experimental results demonstrate that TOJEA outperforms other algorithms on ten instances with up to 400 IoT devices.

An efficient water quality index forecasting and categorization using optimized Deep Capsule Crystal Edge Graph neural network.

The world's freshwater supply, predominantly sourced from rivers, faces significant contamination from various economic activities, confirming that the quality of river water is critical for public health, environmental sustainability, and effective pollution control. This research addresses the urgent need for accurate and reliable water quality monitoring by introducing a novel method for estimating the water quality index (WQI). The proposed approach combines cutting-edge optimization techniques with Deep Capsule Crystal Edge Graph neural networks, marking a significant advancement in the field. The innovation lies in the integration of a Hybrid Crested Porcupine Genghis Khan Shark Optimization Algorithm for precise feature selection, ensuring that the most relevant indicators of water quality (WQ) are utilized. Furthermore, the use of the Greylag Goose Optimization Algorithm to fine-tune the neural network's weight parameters enhances the model's predictive accuracy. This dual optimization framework significantly improves WQI prediction, achieving a remarkable mean squared error (MSE) of 6.7 and an accuracy of 99%. By providing a robust and highly accurate method for WQ assessment, this research offers a powerful tool for environmental authorities to proactively manage river WQ, prevent pollution, and evaluate the success of restoration efforts. PRACTITIONER POINTS: Novel method combines optimization and Deep Capsule Crystal Edge Graph for WQI estimation. Preprocessing includes data cleanup and feature selection using advanced algorithms. Deep Capsule Crystal Edge Graph neural network predicts WQI with high accuracy. Greylag Goose Optimization fine-tunes network parameters for precise forecasts. Proposed method achieves low MSE of 6.7 and high accuracy of 99%.

Enhanced fatigue SN curve generation for gear bending fatigue life prediction using multi-objective optimization algorithms

This study presents a comprehensive methodology for fatigue life prediction and SN curve generation in gear systems, integrating specimen and product-level testing, simulations, and multi-objective optimization. The approach addresses the discrepancy between specimen-based testing and actual product performance through a novel multi-fidelity framework. To solve the proposed multi-objective optimization problem, several optimization algorithms were compared and analyzed, demonstrating the effectiveness of each approach in balancing different objectives. A key innovation is the development of an empirical formulation-based surrogate model, enabling efficient optimization while significantly reducing computational demands. The methodology covers both specimen and product-level testing along with simulations, offering a holistic solution for accurate fatigue life prediction. Extensive validation through experiments and comparison with commercial software confirms the reliability and practical applicability of the method. This approach provides a sustainable and efficient solution for SN curve generation and fatigue life prediction in automotive gear train development, allowing for continuous improvement as new data becomes available. Its practicality and accuracy make it particularly valuable for early-stage design optimization and large-scale applications in the automotive industry.

Prediction of permeability characteristics of mine slurries and sediments using finite-strain framework by optimisation algorithms

Determining the permeability characteristics of mine tailing slurries through laboratory finite-strain consolidation tests is costly due to the extensive testing required. An alternative approach involves back-predicting hydraulic conductivities from settling column test data. This study employed settlement and excess pore pressure data as inputs in optimising the permeability parameters using the finite-strain consolidation (FSC) framework. The proposed approach used the finite difference method to solve the governing equation for FSC in the forward analysis. Two novel variants of the Artificial Bee Colony (ABC) algorithm, namely the Modified Artificial Bee Colony (MABC) and the Hybrid Artificial Bee Colony (HABC) algorithms, were used to back-predict the hydraulic conductivity function parameters from the FSC test data. These models predicted the permeability characteristics using the temporal variation of the settlements for six slurries and excess pore pressure dissipation data for four slurries. The performance of the proposed algorithms along with Hybrid Particle Swarm Optimisation (HPSO) was compared with the conventional optimisation algorithms, viz., PSO, ABC, and Quantum PSO (QPSO). The HABC and HPSO exhibited remarkable stability, consistently converging to identical solution sets across multiple iterations, thereby outperforming other algorithms in this study. Despite its higher time complexity by HABC relative to the other evaluated methods, this complexity is warranted for enhanced robustness. The current study is helpful in brining practical and reliable methodologies for estimating the hydraulic conductivity function from field-scale conductivity (FSC) data, providing critical insights for the safe and efficient management of slurry wastes.

MPC Optimization Algorithm and Strategy for HVAC System under Smart City Construction

As a giant in energy consumption, buildings urgently need to optimize control strategies for the main energy consuming equipment inside buildings. It is of great significance to design advanced control algorithms to improve the efficiency of the main energy consuming equipment in buildings, namely air conditioning, in the current energy shortage. The model predictive control algorithms and strategies were used in this study to control the HVAC system to improve the energy utilization of the city. Then linear matrix inequality with robust model predictive feedback controller was used to optimize and get the model predictive control optimization algorithm. The research results showed that, under the influence of different factors, the three regions controlled by the model predictive control optimization algorithm showed a little overshooting in the initial state. But it was quickly corrected after adjustment. Meanwhile, the average tracking error of temperature and humidity in each region was 0.139◦ C and 0.13g/kg dry air, respectively. The average predicted mean vote was 0.32. In actual office buildings, the proposed algorithm controlled the temperature within the reference value range of 0.1◦ C throughout the entire process. The total electricity consumption and electricity price costs were reduced by 12.11% and 22.54%, respectively. In summary, the proposed method has good performance for HVAC system application, which can effectively realize energy saving and emission reduction and improve human comfort. This method makes important contributions to promote the construction of smart cities and the development of green buildings.

A Novel Reinforcement Learning-Based Particle Swarm Optimization Algorithm for Better Symmetry between Convergence Speed and Diversity

This paper introduces a novel Particle Swarm Optimization (RLPSO) algorithm based on reinforcement learning, embodying a fundamental symmetry between global and local search processes. This symmetry aims at addressing the trade-off issue between convergence speed and diversity in traditional algorithms. Traditional Particle Swarm Optimization (PSO) algorithms often struggle to maintain good convergence speed and particle diversity when solving multi-modal function problems. To tackle this challenge, we propose a new algorithm that incorporates the principles of reinforcement learning, enabling particles to intelligently learn and adjust their behavior for better convergence speed and richer exploration of the search space. This algorithm guides particle learning behavior through online updating of a Q-table, allowing particles to selectively learn effective information from other particles and dynamically adjust their strategies during the learning process, thus finding a better balance between convergence speed and diversity. The results demonstrate the superior performance of this algorithm on 16 benchmark functions of the CEC2005 test suite compared to three other algorithms. The RLPSO algorithm can find all global optimum solutions within a certain error range on all 16 benchmark functions, exhibiting outstanding performance and better robustness. Additionally, the algorithm’s performance was tested on 13 benchmark functions from CEC2017, where it outperformed six other algorithms by achieving the minimum value on 11 benchmark functions. Overall, the RLPSO algorithm shows significant improvements and advantages over traditional PSO algorithms in aspects such as local search strategy, parameter adaptive adjustment, convergence speed, and multi-modal problem handling, resulting in better performance and robustness in solving optimization problems. This study provides new insights and methods for the further development of Particle Swarm Optimization algorithms.

Simulation of marine wireless sensor network coverage based on improved grey wolf optimization algorithm

Abstract A new improved algorithm (IGWO) is proposed based on the Grey Wolf Optimization (GWO) algorithm to solve the issue of low overall coverage easily caused by the random deployment of nodes in wireless sensor networks. The IGWO algorithm improves its searchability by adjusting the convergence factor a and changing the updating rules of individual gray wolf positions. IGWO improves the overall search capabilities of the algorithm by converging and adjusting the trend of convergence factors and dynamically adjusts the gray wolf position update strategy by taking the Euclidean distance ratio values of the omega wolf to the alpha wolf, beta wolf, and delta wolf as parameters, further expanding the search capability of the algorithm. Using MATLAB for simulation, we select 20, 30, and 40 sensor nodes respectively, and the coverage of IGWO compared to GWO’s WSN increases by 0.09%, 2.09%, and 1.17%, respectively, proving that the IGWO algorithm can effectively improve the coverage and performance of WSN.

Spatio-Temporal Modeling of Asthma-Prone Areas: Exploring the Influence of Urban Climate Factors with Explainable Artificial Intelligence (XAI)

Urbanization's impact on climate is increasingly recognized as a significant public health challenge, particularly for respiratory conditions like asthma. Despite progress in understanding asthma, a critical gap remains regarding the interaction between urban environmental factors and asthma-prone areas. This study addresses this gap by applying innovative spatio-temporal modeling techniques with explainable artificial intelligence (XAI). Using data from 872 asthma patients in Tehran, Iran, and 19 factors affecting asthma exacerbations, including climate and air pollution, spatio-temporal modeling was conducted using XGBoost (eXtreme Gradient Boosting) algorithm optimization by the Bat algorithm (BA). Evaluation of asthma-prone area maps using receiver operating characteristic (ROC) curves revealed accuracies of 97.3% in spring, 97.5% in summer, 97.8% in autumn, and 98.4% in winter. Interpretability analysis of the XGBoost model utilizing the SHAP (Shapley Additive exPlanations) method highlighted rainfall in spring and autumn and temperature in summer and winter as having the most significant impacts on asthma. Particulate matter (PM2.5) in spring, carbon monoxide (CO) in summer, ozone (O3) in autumn, and PM10 in winter exhibited the most substantial effects among air pollution factors. This research enhances understanding of asthma dynamics in urban environments, informing targeted interventions for urban planning strategies to mitigate adverse health consequences of urbanization.

A Multi-Stage Differential-Multifactorial Evolutionary Algorithm for Ingredient Optimization in the Copper Industry

A Multi-Stage Differential-Multifactorial Evolutionary Algorithm for Ingredient Optimization in the Copper Industry

Innovative energy solutions: Evaluating reinforcement learning algorithms for battery storage optimization in residential settings

The implementation of BESS (battery energy storage systems) and the efficient optimization of their scheduling are crucial research challenges in effectively managing the intermittency and volatility of solar-PV (photovoltaic) systems. Nevertheless, an examination of the existing body of knowledge uncovers notable deficiencies in the ideal arrangement of energy systems' timetables. Most models primarily concentrate on a single aim, whereas only a few tackle the intricacies of multi-objective scenarios. This study examines homes connected to the power grid equipped with a BESS and a solar PV system. It leverages four distinct reinforcement learning (RL) algorithms, selected for their unique training methodologies, to develop effective scheduling models. The findings demonstrate that the RL model using Trust Region Policy Optimization (TRPO) effectively manages the BESS and PV system despite real-world uncertainties. This case study confirms the suitability and effectiveness of this approach. The TRPO-based RL framework surpasses previous models in decision-making by choosing the most optimal BESS scheduling strategies. The TRPO model exhibited the highest mean self-sufficiency rates compared to the A3C (Asynchronous Advantage Actor-Critic), DDPG (Deep Deterministic Policy Gradient), and TAC (Twin Actor Cretic) models, surpassing them by ∼ 3 %, 0.72 %, and 3.5 %, correspondingly. This results in enhanced autonomy and economic benefits by adapting to dynamic real-world conditions. Consequently, our approach was strategically designed to deliver an optimized outcome. This framework is primarily intended for seamless integration into an automated energy plant environment, facilitating regular electricity trading among multiple buildings. Backed by initiatives like the Renewable Energy Certificate weight, this technology is expected to play a crucial role in maintaining a balance between power generation and consumption. The MILP (Mixed Integer Linear Programming) architecture achieved a self-sufficiency rate of 29.12 %, surpassing the rates of A3C, TRPO, DDPG, and TAC by 2.48 %, 0.64 %, 2 %, and 3.04 %, correspondingly.

Enhancing requirements-to-code traceability with GA-XWCoDe: Integrating XGBoost, Node2Vec, and genetic algorithms for improving model performance and stability

This study addresses the challenge of requirements-to-code traceability by proposing a novel model, Genetic Algorithm-XGBoost With Code Dependency (GA-XWCoDe), which integrates eXtreme Gradient Boosting (XGBoost) with a Node2Vec model-weighted code dependency strategy and genetic algorithms for parameter optimization. XGBoost mitigates overfitting and enhances model stability, while Node2Vec improves prediction accuracy for low-confidence links. Genetic algorithms are employed to optimize model parameters efficiently, reducing the resource intensity of traditional methods. Experimental results show that GA-XWCoDe outperforms the state-of-the-art method TRAceability lInk cLassifier (TRAIL) by 17.44% and Deep Forest for Requirement traceability (DF4RT) by 33.36% in terms of average F1 performance across four datasets. It is significantly superior to all baseline methods at a confidence level of α¡0.01 and demonstrates exceptional performance and stability across various training data scales.

A multi-objective optimization and evaluation framework for LID facilities considering urban surface runoff and shallow groundwater regulation

Currently, the goals of sponge city construction mostly focus on urban surface water, with few considerations of groundwater indicators, and there is a lack of simulation methods that consider the interaction between surface water and groundwater under low impact development (LID) scenarios. Taking a sponge campus in Xi'an as the research object, this paper proposes a method to realize the interaction between surface water and groundwater at different spatial and temporal scales, so as to construct a SWMM-MODFLOW coupled numerical model to carry out the analysis of hydrological effect of sponge city comprehensively taking into account both surface water and groundwater. Simultaneously, the SWMM is coupled with the NSGA-III algorithm for multi-objective optimization of LID facility configurations, aiming to identify a globally optimal solution set for LID facility configurations under both long and short-duration rainfall scenarios. A novel framework for a comprehensive evaluation system of surface water-groundwater benefits is also developed to comprehensively assess LID facility optimization schemes that meet the goals of stormwater runoff infiltration and reduction, pollution interception and purification, and groundwater recharge conservation. The simulation results show that after optimization, the LID scheme has good surface runoff regulation effects and pollution reduction capabilities, reducing the runoff peak from 2.297 m3/s to 2.128 m3/s, significantly lowering the risk of inundation and effectively reducing the total suspended solids (SS) load by 42.38 kg at the outfall. Meanwhile, the groundwater recharge effect is particularly significant in September. Compared to the baseline scenario, the average groundwater levels from June to September were raised by 3.966 cm, 5.120 cm, 6.743 cm, and 7.639 cm, respectively. The maximum daily groundwater level rise reached 7.82 cm, while the average groundwater level for the whole year of 2023 increased by 2.61 cm. The maximum transport area of pollutants decreased from 2,359.44 m2 to 1,796.54 m2, and the maximum transport distance reduced from 79.985 m to 69.977 m. Additionally, the central concentration of pollutants also decreased. This optimization evaluation framework will aid decision-makers in selecting LID facility management strategies that balance the dual benefits of surface water runoff and groundwater regulation.

Improved dung beetle optimization algorithm and finite element analysis for spindle optimization

Improved dung beetle optimization algorithm and finite element analysis for spindle optimization

Optical coherence tomography to guide percutaneous coronary intervention.

Percutaneous coronary intervention (PCI) has been most commonly guided by coronary angiography. However, to overcome the inherent limitations of conventional coronary angiography, there has been an increasing interest in the adjunctive tools of intracoronary imaging for PCI guidance. Recently, optical coherence tomography (OCT) has garnered substantial attention as a valid intravascular imaging modality for guiding PCI. However, despite the unparalleled high-resolution imaging capability of OCT, which offers detailed anatomical information on coronary lesion morphology and PCI optimisation, its broad application in routine PCI practice remains limited. Several factors may have curtailed the widespread adoption of OCT-guided PCI in daily practice, including the transitional challenge from intravascular ultrasound (IVUS), the experienced skill required for image acquisition and interpretation, the lack of a uniform algorithm for OCT-guided PCI optimisation, and the limited clinical evidence. Herein, we provide an in-depth review of OCT-guided PCI, involving the technical aspects, optimal strategies for OCT-guided PCI, and the wide application of OCT-guided PCI in various anatomical subsets. Special attention is given to the latest clinical evidence from recent randomised clinical trials with respect to OCT-guided PCI.

Optimization of temperature prediction algorithms and simulation of future neighborhood-scale thermal environments in Chongqing

With climate change and urbanization, predicting the outdoor thermal environment of residential areas has become increasingly crucial, particularly during extreme weather conditions. Existing studies lack a comprehensive approach to optimizing temperature prediction algorithms for Chongqing and simulating extreme thermal environments at the neighborhood scale for future decades. This study employed various algorithms, including Levenberg-Marquardt optimization, Monte Carlo simulation, ARIMA modeling, and SARIMA prediction, to simulate and forecast temperature data for the central urban area of Chongqing from 2014 to 2023. By assessing metrics such as root mean square error (RMSE), R-squared (R2), and mean absolute percentage error (MAPE), the most effective prediction algorithm was identified. Results indicate that the Levenberg-Marquardt optimization algorithm, known for its nonlinear curve fitting capabilities, demonstrated superior performance in both the test and training sets, achieving an RMSE of 0.82 °C, MAPE of 1.81 %, and R2 of 0.75. This algorithm was subsequently used to forecast the outdoor temperature trend in Chongqing from 2024 to 2050, while also simulating the outdoor wind field, temperature distribution, building surface temperatures, and wind speeds in a residential district at neighborhood scale for 2030, 2040, and 2050 at a building density of 40 %. Findings reveal that peak pedestrian-level temperatures could reach 45.62 °C, and peak roof surface temperatures could reach 93.93 °C under extreme conditions. Furthermore, the study proposes practical recommendations for building layouts and cooling strategies. These findings are expected to improve residential planning in Chongqing, enhance residents' quality of life, and provide insights for other regions facing future temperature changes.

Research on Optimization of Visual Object Tracking Algorithm Based on Deep Learning

The appearance of deep gaining knowledge has extensively advanced the sphere of visible item tracking, permitting greater robust and accurate monitoring of items throughout complex scenes. This study optimises a visual item tracking set of rules based on the Siamese region inspiration network (Siamese RPN) monitoring algorithm, aiming to beautify its efficiency and effectiveness in actual-time packages. The Siamese RPN algorithm, acknowledged for its stability among accuracy and velocity because of its architecture that mixes the Siamese network for characteristic extraction with a vicinity suggestion community for item localisation, provides a promising basis for improvement. This examination introduces numerous optimisations to the authentic Siamese RPN framework. First, we endorse an improved feature extraction model that leverages a more efficient deep neural community structure, lowering computational load while preserving excessive accuracy. 2nd, we optimise the place concept mechanism by incorporating an adaptive anchor scaling method that dynamically adjusts the scale and ratio of anchors based on the object’s scale variations, enhancing the tracking accuracy across distinctive object sizes and aspect ratios. Moreover, we introduce a unique training method that employs an aggregate of actual global and synthetically generated statistics to beautify the robustness of the monitoring algorithm towards various demanding situations, including occlusions, speedy moves, and illumination adjustments. The effectiveness of the proposed optimizations is evaluated through complete experiments on numerous benchmark datasets, consisting of OTB, VOT, and LaSOT, demonstrating extensive upgrades in tracking accuracy and speed as compared to the authentic Siamese RPN algorithm and different modern-day tracking techniques. The outcomes of this study no longer underscore the potential of optimised Siamese RPN algorithms in visible item monitoring but additionally lay the basis for future explorations into actual-time, green, and strong tracking systems. Those improvements keep great promise for a wide variety of packages, from surveillance and protection to autonomous cars and augmented truth structures, where particular and dependable item monitoring is paramount.

Optimization of test methods and algorithms for assessing the resistance of field-effect transistors to various types of radiation using promising test equipment

Optimization of test methods and algorithms for assessing the resistance of field-effect transistors to various types of radiation using promising test equipment

Optimization of Iterative Control Algorithms for High-Resolution Adaptive Optics Systems

Optimization of Iterative Control Algorithms for High-Resolution Adaptive Optics Systems

Optimization of surgical evaluation algorithms for living donor liver transplantation

BackgroundLiving donor liver transplantation (LDLT) is an established and endorsed alternative for deceased donor liver transplantation with better recipient outcomes. Nevertheless, while extensive evaluation of potential donors is crucial, evaluation algorithms differ between transplant centres and guidelines. MethodsWe included 317 individuals evaluated for LDLT between 07/2007–07/2022 in a retrospective analysis. The evaluation process was analysed to identify the key reasons for declining 77 potential donors. Additionally, 146 donors that underwent LDLT were analysed regarding risk factors for complications. ResultsThe main reasons for donor refusal were liver volumetry (40.3 %) and metabolic factors including obesity or steatotic liver disease (20.8 %). Contrast-enhanced computed tomography (CECT) identified 63.6 % of all declined donors; CECT combined with assessment of medical history, physical examination, blood testing and ultrasonography, identified 87.0 % of declined potential donors. Associated with this selection, complication rates in donors were low (≥II in 17.1 %; none with ≥IVb). Notably, higher age was a risk factor for developing a complication ≥II after hemi-hepatectomy (p = 0.0373). ConclusionsWe propose a progressive 4-step evaluation algorithm that begins with a very basic assessment combined with up-front CECT. This early phase of testing is expected to identify nearly 90 % of ineligible donors, thereby conserving critical resources, time and money, as well as minimising burden for potential donors. FundingJ.M.W. received funding by grant We-4675/6–1 from the Deutsche Forschungsgemeinschaft (DFG) in Bonn, Germany.

An optimal machine learning-based algorithm for detecting phishing attacks using URL information

An optimal machine learning-based algorithm for detecting phishing attacks using URL information