Algorithm Parameters Research Articles

Application of a novel metaheuristic algorithm inspired by connected banking system in truss size and layout optimum design problems and optimization problems

Optimum design of truss structures can be a challenging and difficult field of study specially if an optimum design problem is comprised of continuous and discrete decision variables such as in truss size and layout optimization problems. Usually metaheuristic approaches are selected to solve these kind of problems and there are many different metaheuristic methods that have been used in dealing with truss optimization problems, which provide optimum solutions for those problems. However, among these various proposed metaheuristics, it is very seldom to face and find a method that is based on technologies such as computer networks. This paper aims to utilize a metaheuristic algorithm which is based on some principals of connected banking systems, which is called the Connected Banking System Optimizer(CBSO) algorithm. The performance of the CBSO is tested against three benchmark truss size/layout optimum design problems namely 15, 18 and 25 bar truss structures. Besides the truss size/layout problems, CEC-BC-2017 test functions with ten dimensions and also three other constrained optimum design problems are also investigated to examine the performance of the CBSO in dealing with these standard unconstrained and constrained test functions. The results of the CBSO optimum designs are presented and compared with some of the available studies in the literature. Statistical analyses are done to have a fair evaluation of the CBSO algorithm’s efficiency and its ability in dealing with the benchmark truss size/layout optimization problems. The CBSO outperforms its rivals and presents lighter weight truss structures. There is no need for configuration of extra parameters in the CBSO algorithm when solving the examined benchmark truss structures, which makes it a robust parameter-free optimization algorithm.

Mono-pole blocking accident analysis caused by inaccurate measurement of optical CT in ±800 kV UHVDC transmission system under lightning strike

The ultra-high voltage direct current (UHVDC) power transmission systems usually transmit a capacity of 6-8 GW, their stability and reliability are of vital importance to the large power grid. This paper analyses a mono-pole blocking accident caused by the inaccurate measurement of optical current transformer (OCT) when the pole line of Shaanbei-Wuhan ±800 kV UHVDC transmission system was struck by lightning. Combined with electromagnetic transient simulation of the UHVDC transmission system, digital simulation and in-plant testing of the optical CT, a comprehensive understanding of the factors contributing to the accident is achieved. It reveals that optical CT measurement anomalies were significant contributors to the misoperation of protective relays, which led to the mono-pole blocking accident. Based on the simulations of optical CT under high peak currents and high di/dt conditions caused by lightning strike, the importance of proper setting of demodulation algorithm parameters in optical CT to avoid inaccurate measurements is emphasized. Through in-plant testing and steady state current accuracy verification, the reasonableness of demodulation parameters is verified. Simulation results illustrate the challenges in measuring very fast transient current accurately, which are the premise and guarantee of stable operation of UHVDC transmission systems under lightning strike.

Research of Options for Constructing Information Management Systems Based on Network Models of Queuing Systems

The use of information management systems (IMSs) for the management of technical facilities is currently one of the directions for further improvement and increase in the effectiveness of the use of technical facilities in solving their target tasks. The existing modern IMSs are a set of hardware and software tools designed for collecting, processing and storing information and management. In the presence of a large amount of information and contradictory factors affecting the quality of management, making informed and timely decisions in the management process is impossible without the use of IMSs. The IMSs currently being developed are, for the most part, specialized systems and are designed to solve specific tasks. In this regard, the development and design of IMSs should be carried out taking into account the relationship with the target indicators and features of the management facilities, the results of a comprehensive analysis of information about the IMS elements in the process of functioning, and structural and algorithmic parameters that affect performance indicators. The use of mathematical models for the study of options for the construction of an IMS is the basis for the design and development of devices and subsystems of an IMS. The IMS models currently being developed make it possible to conduct research for single-stage management processes with the presence of similar service facilities in the system. At the same time, modern technical facilities and control systems are complex complexes with cyclically repeating control processes of various types of means. As a rule, such complexes have a set of parallel operating devices (control channels) that provide control of different types of objects at various stages of information processing. In this case, the structure of the IMS must be represented as a multiphase multichannel technical system in which the process of simultaneous management of several objects of various types takes place. In this regard, the purpose of the article is to develop a mathematical model of an IMS with two phases of management and the presence of an arbitrary number of serviced different types of management facilities. The basis of the model is a multiphase CFR network model with a limited waiting time for an application in the service queue. The study on the model allows choosing an option for building an IMS, in particular, choosing the optimal number of control channels for various types of objects according to the criterion of optimality and restrictions on the cost and time of management. An algorithm for selecting an option for building an IMS has been developed, and an example of calculating the number of control channels for managing three types of objects is given.

Detection of hidden drawings using multi-wavelength dynamic speckle, tuneable algorithms, and unsupervised learning

Abstract We implemented an experiment to reveal hidden drawings on papyrus, utilizing an optical technique based on the speckle phenomenon. The goal is to optimize the detection of hidden objects. Our approach proposes using multiple wavelengths for illumination and tuneable algorithms to process the dynamic speckle images. By implementing the suggested method, we generated various results with varying quality, contingent upon the tuneable algorithm parameters. It's feasible to identify the optimal parameter combination to achieve the most effective visualization of the recovered image. To streamline the selection of tuneable algorithms and mitigate reliance on subjective visual judgment, we employed unsupervised machine learning techniques to determine the conditions necessary to achieve optimal results. This approach simplifies the selection procedure and offers an objective and non-invasive method. Furthermore, the proposed procedure holds promise for extending its application to uncover hidden paintings, subsurface archaeological artefacts, and other dynamic speckle experiments.

Chaotic-Based Improved Henry Gas Solubility Optimization Algorithm: Application to Electric Motor Control

This study presents a novel meta-heuristic optimization method that combines the Henry Gas Solubility Optimization (HGSO) technique with symmetric chaotic systems. By leveraging the randomness of chaotic systems, the parameters of the HGSO algorithm that require random generation are produced through chaotic processes, allowing the algorithm to exhibit chaotic behavior in its pursuit of optimal values. This innovative approach is termed Chaotic Henry Gas Solubility Optimization (CHGSO), with the primary objective of enhancing the performance of the HGSO method. The randomness of the data obtained from chaotic systems was validated using NIST-800-22 tests. The CHGSO method was applied to both 47 benchmark functions and the optimization of parameters for a PID controller utilized in the speed control of a DC motor. To evaluate the effectiveness of the proposed method, it was compared with several widely recognized algorithms in the literature, including PSO, WOA, GWO, EA, SA, and the original HGSO algorithm. The results demonstrate that the proposed method achieved the best performance in 43 of the benchmark functions, outperforming the other algorithms. In the context of controller design, the PID parameters were optimized using the error-based ITSE objective function. According to the controller responses, the proposed method has achieved the best results in the simulation studies, with a settling time of 0.035 and a rise time of 0.014 without overshooting, and in the experimental studies, with a settling time of 0.15 and a settling time of 1.4%. When the results are examined, it is observed that it has achieved successful results in the controller design problem.

Photovoltaic Maximum Power Point Tracking Technology Based on Power Prediction Algorithm Combined with Variable Step Length Disturbance Observation Method

Under partial shading conditions, the system operating sequence of photovoltaic cells does not fall on a single characteristic curve, and the traditional maximum power point tracking (MPPT) control algorithm is prone to causing the system output power to oscillate around the maximum power point. In the case of multiple peaks, the traditional MPPT algorithm is prone to being trapped in a local optimum solution. This paper proposes an MPPT control algorithm based on the WOA-RF prediction algorithm combined with a variable step disturbance observation method. By establishing a photovoltaic system simulation model, the improved algorithm is verified through simulation. The improved MPPT control algorithm can adaptively adjust the step size under different conditions to ensure that the system can quickly and accurately track the maximum power point. At the same time, by optimizing the algorithm logic and parameter settings, it can effectively avoid problems such as local optimum solutions and instability in the system, and improve the system convergence speed and tracking accuracy.

Benchmarking of Individual Tree Segmentation Methods in Mediterranean Forest Based on Point Clouds from Unmanned Aerial Vehicle Imagery and Low-Density Airborne Laser Scanning

Three raster-based (RB) and one point cloud-based (PCB) algorithms were tested to segment individual Aleppo pine trees and extract their tree height (H) and crown diameter (CD) using two types of point clouds generated from two different techniques: (1) Low-Density (≈1.5 points/m2) Airborne Laser Scanning (LD-ALS) and (2) photogrammetry based on high-resolution unmanned aerial vehicle (UAV) images. Through intensive experiments, it was concluded that the tested RB algorithms performed best in the case of UAV point clouds (F1-score > 80.57%, H Pearson’s r > 0.97, and CD Pearson´s r > 0.73), while the PCB algorithm yielded the best results when working with LD-ALS point clouds (F1-score = 89.51%, H Pearson´s r = 0.94, and CD Pearson´s r = 0.57). The best set of algorithm parameters was applied to all plots, i.e., it was not optimized for each plot, in order to develop an automatic pipeline for mapping large areas of Mediterranean forests. In this case, tree detection and height estimation showed good results for both UAV and LD-ALS (F1-score > 85% and >76%, and H Pearson´s r > 0.96 and >0.93, respectively). However, very poor results were found when estimating crown diameter (CD Pearson´s r around 0.20 for both approaches).

Theoretical Analysis on Active Polarization Control of Fiber Laser Based on Root Mean Square Propagation Algorithm

High-power linearly polarized fiber lasers are widely used in coherent beam combination, nonlinear frequency conversion, and gravitational wave detection. With the increase in output power, it is challenging for fiber lasers to maintain a high polarization extinction ratio (PER). Combined with intelligent techniques, active polarization control is a prospective method to obtain the laser output with high PER and high stability. We demonstrate a comprehensive model of an active polarization control system. The root mean square propagation (RMS-Prop) algorithm is used to control the non-polarization-maintaining (non-PM) fiber laser to generate linearly polarized laser. The parameters of the RMS-Prop algorithm are theoretically analyzed, including cost function, perturbation amplitude, and global learning rate. The simulation results show that PER is the optimal cost function. When the perturbation amplitude is 0.06 and the global learning rate is 0.6, the system can achieve the optimal control speed and accuracy. By comparison with the stochastic parallel gradient descent (SPGD) algorithm, the RMS-Prop algorithm has an advantage in obtaining higher PER.

On formal modeling, analysis and optimization of reconfigurable manufacturing systems

ABSTRACT This paper deals with the formal specification and multi-objective optimization of reconfigurable manufacturing systems (RMSs). RMSs are characterized by their dynamic structure, crucial for responding to functional variations, adjusting production capacity and overcoming operational dysfuntions promptly and cos-effectively. The effective utilization of this dynamic capability depends on thorough study and optimization of RMS configurations. To tackle this challenge, a novel formalism called genetic reconfigurable object nets (Gen-RONs) is introduced. Gen-RONs formalism integrates the modeling, verification and reconfiguration capabilities of reconfigurable object nets with advanced multi-objective techniques from genetic algorithms. Within Gen-RONs, Petri nets serve to: (i) encode RMS configurations, (ii) manage RMS scheduling and (iii) employ graph transformation theory rules as genetic algorithm operators for real-time system reconfiguration. Thus, Gen-RONs formalism provides a robust framework for optimizing RMSs using genetic algorithms. To ensure deadlock-free RMS configurations, Gen-RONs incorporate a binary tree-based technique. Furthermore, Taguchi’s method is employed to fine-tune genetic algorithm parameters. The contribution of this paper is demonstrated through a case study involving an RMS and the widely used genetic algorithm NSGA-II. Significant gains in terms of the number of solutions (i.e. configurations) and the criteria values are achieved.

An optimal parameterized Newton-type structure-preserving doubling algorithm for impact angle guidance-based 3D pursuer/target interception engagement

The proposed strategy, finite-time state-dependent Riccati equation (FT-SDRE)-based impact angle guidance, is generally employed to solve the 3D pursuer/target interception model with fixed lateral accelerations. This article expands its application to a general scenario where the lateral acceleration of a target may change. To achieve this, we approximate the accelerations of the azimuth and elevation angles of the target in the inertial frame via second-order finite difference schemes and develop a high-performance FT-SDRE algorithm with structure-preserving doubling algorithms (SDAs). As a result, the update frequency of the controller can be increased, and better guidance of the pursuer can be obtained to address the high maneuverability of the target during the entire interception procedure. At every state of the FT-SDRE, a modified Newton–Lyapunov method is employed to solve the continuous algebraic Riccati equation (CARE), and a new simplified SDA with adaptive optimal parameter selection is proposed for solving the associated Lyapunov equation. Our numerical results demonstrate that the FT-SDRE algorithm accelerated by our proposed methods is approximately three times faster than the FT-SDRE algorithm, in which the MATLAB functions icare and lyap are used to solve the CARE and the Lyapunov equation, respectively, throughout the entire interception procedure. In other words, the control frequency can be increased threefold. In our benchmark cases where the target maneuvers with nonlinear lateral acceleration, the target can be intercepted earlier via the proposed FT-SDRE algorithm.

Exact and parameterized algorithms for the independent cutset problem

The Independent Cutset problem asks whether there is a set of vertices in a given graph that is both independent and a cutset. Such a problem is ▪-complete even when the input graph is planar and has maximum degree five. In this paper, we first present a O⁎(1.4423n)-time algorithm to compute a minimum independent cutset (if any). Since the property of having an independent cutset is MSO1-expressible, our main results are concerned with structural parameterizations for the problem considering parameters incomparable with clique-width. We present ▪-time algorithms for the problem considering the following parameters: the dual of the maximum degree, the dual of the solution size, the size of a dominating set (where a dominating set is given as an additional input), the size of an odd cycle transversal, the distance to chordal graphs, and the distance to P5-free graphs. We close by introducing the notion of α-domination, which allows us to identify more fixed-parameter tractable and polynomial-time solvable cases.

Influence of Structure from Motion Algorithm Parameters on Metrics for Individual Tree Detection Accuracy and Precision

Uncrewed aerial system (UAS) structure from motion (SfM) monitoring strategies for individual trees has rapidly expanded in the early 21st century. It has become common for studies to report accuracies for individual tree heights and DBH, along with stand density metrics. This study evaluates individual tree detection and stand basal area accuracy and precision in five ponderosa pine sites against the range of SfM parameters in the Agisoft Metashape, Pix4DMapper, and OpenDroneMap algorithms. The study is designed to frame UAS-SfM individual tree monitoring accuracy in the context of data processing and storage demands as a function of SfM algorithm parameter levels. Results show that when SfM algorithms are properly tuned, differences between software types are negligible, with Metashape providing a median F-score improvement over OpenDroneMap of 0.02 and PIX4DMapper of 0.06. However, tree extraction performance varied greatly across algorithm parameters, with the greatest extraction rates typically coming from parameters causing increased density in dense point clouds and minimal point cloud filtering. Transferring UAS-SfM forest monitoring into management will require tradeoffs between accuracy and efficiency. Our analysis shows that a one-step reduction in dense point cloud quality saves 77–86% in point cloud processing time without decreasing tree extraction (F-score) or basal area precision using Metashape and PIX4DMapper but the same parameter change for OpenDroneMap caused a ~5% loss in precision. Providing reproducible processing strategies is a vital step in successfully transferring these technologies into usage as management tools.

Baltic sea ice thickness estimation based on X-band SAR data and background information

Abstract In this study an operational sea ice thickness (SIT) estimation algorithm, based on HH-polarized X-band synthetic aperture radar (SAR) data, background information from the most recent, typically from the previous day, available daily Baltic Sea ice chart and the operational Finnish Meteorological Institute (FMI) thermodynamic ice model, was developed and evaluated. The algorithm was designed to complement the C-band SAR SIT algorithm developed earlier at FMI and applied daily as part of the operational Copernicus Marine Service (CMS). The X-band SIT algorithm was developed by utilizing the sea ice thickness measurements made onboard the Finnish and Swedish ice breakers during two winters seasons: 2021–2022 and 2022–2023. The former season measurements were used for defining the algorithm parameters and the later season for evaluation of the algorithm performance. According to the evaluation metrics the X-band algorithm performance is slightly better than that of the operational CMS C-band SAR SIT algorithm, indicating its suitability for operational use in CMS.

An accurate state-of-charge estimation of lithium-ion batteries based on improved particle swarm optimization-adaptive square root cubature kalman filter

The state of charge (SOC) of lithium-ion batteries (LIBs) is regarded as the fundamental parameter of the battery management system (BMS). In this paper, a parameter optimization method for mobile estimation windows based on particle swarm optimization-adaptive square root cubature Kalman filter (PSO-ASRCKF) is established to improve the SOC estimation ability and accuracy of LIBs. The filtering algorithm parameters are optimized to achieve high-precision SOC estimation. An improved ASRCKF with the PSO algorithm to optimize the moving estimation window is constructed to obtain the best adaptive window value. Different temperatures and initial SOC values are used to verify the proposed method under dynamic stress test (DST) and other conditions. The results show that the relative error is mainly distributed within 0.5 % when the SOC is stable. In addition, robustness and adaptability are verified with the root mean square error (RMSE) and the mean absolute error (MAE) values of 0.0019 and 0.0017, respectively, under the DST working condition. The experimental results show that the proposed method can achieve accurate SOC estimation under different temperatures, operating conditions, and initial SOC values.

Optimization of Load-transfer Functions for a Large-diameter Pile in Multi-layered Geological Conditions via a Stochastic Search Algorithm

The paper presents the combination of the load transfer method (computational model) and the genetic algorithm (optimization model) for the automated inverse analysis of instrumented pile load tests. The output of this process are the input parameters governing the shape of the load-transfer functions and thus the prediction accuracy when the load-transfer method is used as a design tool for deep foundations. The optimization problem is converted into an unconstrained task by applying the static penalty approach. Two types of measurements are considered in a newly proposed objective function: the load-displacement curve monitored in a pile head and the axial force profiles along a pile derived from strain gauges. Firstly, the local sensitivity analysis via the Design of Experiments is carried out to identify the parameters of the genetic algorithm which significantly influences the rate of convergence during the optimization process. Subsequently, a fully automated inverse analysis of the loading test of the large-diameter bored pile in multilayered geological conditions is presented. The simultaneous combination of two instrumentation sources leads to a stable unique solution with a sufficient match between the prediction and measurements despite a larger number of unknown – optimized variables.

Algorithm Analysis and Optimization of a Digital Image Correlation Method Using a Non-Probability Interval Multidimensional Parallelepiped Model

Digital image correlation (DIC), a widely used non-contact measurement technique, often requires empirical tuning of several algorithmic parameters to strike a balance between computational accuracy and efficiency. This paper introduces a novel uncertainty analysis approach aimed at optimizing the parameter intervals of a DIC algorithm. Specifically, the method leverages the inverse compositional Gauss–Newton algorithm combined with a prediction-correction scheme (IC-GN-PC), considering three critical parameters as interval variables. Uncertainty analysis is conducted using a non-probabilistic interval-based multidimensional parallelepiped model, where accuracy and efficiency serve as the reliability indexes. To achieve both high computational accuracy and efficiency, these two reliability indexes are simultaneously improved by optimizing the chosen parameter intervals. The optimized algorithm parameters are subsequently tested and validated through two case studies. The proposed method can be generalized to enhance multiple aspects of an algorithm’s performance by optimizing the relevant parameter intervals.

An Enhanced LBPH Algorithm for Robust Face Feature Extraction

From security systems to human-computer interaction, face recognition technology is a key component in many different applications. In this domain, the local binary pattern histogram (lbph) algorithm has shown great promise due to its ability in texture-based feature extraction and robustness. In this study, the performance of lbph algorithm is improved by optimising important parameters: radius, number of neighbours and grid configuration. Tweaking these parameters systematically enable us to get the most success out of this algorithm. Lbph algorithm parameters has been tuned into a 5x7 dimensional matrix, which gives us total of 35 grids with equal width and height pixels. In other words, one central pixel plus 34 neighbouring pixels where the radii of 3 square neighbourhoods can be adjusted. The study combines the experimental exploration with fine-tuning via machine learning approaches to optimize lbph algorithm. Finally, we have provided experimental results on intra-class and inter-class feature distribution analysis conducted on selected images taken under constraints and unconstraint environments. The performance of our novel 34N-LBPH algorithm showed very low values by obtaining intra-class average means of 0.031, 0.078, and 0.101 for three classes under constraint environment indicating the proposed 34n-lbph is robust for facial feature extraction. The findings suggest that our enhanced algorithm, 34 neighbour linear binary pattern Histogram (34N-LBPH) can effectively handle variations in lighting, expressions and occlusions, contributing to the advancement of facial feature extraction for face recognition.

Optimized Manufacturing and Construction Site Machines Maintenance Prediction using Machine Learning and Crayfish Algorithm

Efficient maintenance prediction is critical for ensuring the operational continuity and longevity of industrial machinery. This paper presents a comparative analysis of diverse machine-learning algorithms for the task of machine maintenance prediction. Through rigorous experimentation and evaluation, we assess the performance of algorithms including AdaBoost, Random Forest, Gradient Boosting, and Support Vector Machines (SVM). Additionally, to enhance predictive accuracy, we integrate an optimizer algorithm, Cuckoo Search,into our framework. This optimization technique fine-tunes algorithm parameters, further improving accuracy. Our findings offer valuable insights into optimizing machine maintenance prediction, empowering industries with proactive maintenance strategies to mitigate downtime and enhance productivity.

Robust estimation of structural orientation parameters and 2D/3D local anisotropic Tikhonov regularization

Understanding the orientation of geologic structures is crucial for analyzing the complexity of the earths’ subsurface. For instance, information about geologic structure orientation can be incorporated into local anisotropic regularization methods as a valuable tool to stabilize the solution of inverse problems and produce geologically plausible solutions. We introduce a new variational method that uses the alternating direction method of multipliers within an alternating minimization scheme to jointly estimate orientation and model parameters in 2D and 3D inverse problems. Specifically, our approach adaptively integrates recovered information about structural orientation, enhancing the effectiveness of anisotropic Tikhonov regularization in recovering geophysical parameters. The paper also discusses the automatic tuning of algorithmic parameters to maximize the new method’s performance. Our algorithm is tested across diverse 2D and 3D examples, including structure-oriented denoising and trace interpolation. The results indicate that the algorithm is robust in solving the considered large and challenging problems, alongside efficiently estimating the associated tilt field in 2D cases and the dip, strike, and tilt fields in 3D cases. Synthetic and field examples show that our anisotropic regularization method produces a model with enhanced resolution and provides a more accurate representation of the true structures.

Jump around: Selecting Markov Chain Monte Carlo parameters and diagnostics for improved food web model quality and ecosystem representation

Capturing ecological data variability in food web models is an important step for improving model representation of empirical systems. One approach is to use linear inverse modelling and Markov Chain Monte Carlo (LIM-MCMC) techniques to set up an inverse LIM problem using empirical data constraints, and then sample multiple plausible food webs from the inverse problem using an MCMC algorithm. We describe the set of plausible food webs as an ‘ensemble’ of solutions to the inverse problem sampled with the LIM-MCMC algorithm. The extent of data variability eventually integrated into an ensemble depends on how well the LIM-MCMC algorithm samples the solution space. Algorithm quality can be adjusted via user-defined parameters describing starting points, jump sizes, and number of iterations or food webs produced. However, little information exists on how each LIM-MCMC algorithm parameter affects the degree of empirical data variability introduced into the ensemble. Further, post hoc algorithm quality diagnostics with commonly used trace plots and the coefficient of variation (CoV) rarely address critical aspects of algorithm quality, such as (1) if the returned ensemble successfully targeted the solution space distribution (stationarity), (2) correlation between ensemble solutions (mixing), and (3) if the ensemble contains enough solutions to adequately capture input data variability (sampling efficiency). Therefore, we used several established MCMC convergence diagnostics to (1) quantify how algorithm parameters affect ensemble flow values and if these differences propagate to ecological indicators and (2) evaluate algorithm quality and compare to current evaluation and ecosystem modelling methods. We applied 30 LIM-MCMC algorithm combinations of varying starting points, jump sizes, and number of iterations to solve food web ensembles from a single food web model. We analysed ensembles with Ecological Network Analysis (ENA) to calculate indicators describing system function. Results show that LIM-MCMC algorithm parameters, in particular the jump size, affect ensemble flow values, which propagate to ecological indicators describing different ecosystem function of the same model. Thereafter, comparisons of post hoc diagnostics show that MCMC convergence diagnostics provided more robust estimates of algorithm quality than trace plots and CoV. Together, these findings underpin several novel recommendations to enhance LIM-MCMC algorithm parameter selection and quality assessments applicable to any ecological ensemble network study.