Centroid Method Research Articles

Research on Automatic Tracking and Size Estimation Algorithm of “Low, Slow and Small” Targets Based on Gm-APD Single-Photon LIDAR

In order to solve the problem of detecting, tracking and estimating the size of “low, slow and small” targets (such as UAVs) in the air, this paper designs a single-photon LiDAR imaging system based on Geiger-mode Avalanche Photodiode (Gm-APD). It improves the Mean-Shift algorithm and proposes an automatic tracking method that combines the weighted centroid method to realize target extraction, and the principal component analysis (PCA) method of the adaptive rotating rectangle is realized to fit the flight attitude of the target. This method uses the target intensity and distance information provided by Gm-APD LiDAR. It addresses the problem of automatic calibration and size estimation under multiple flight attitudes. The experimental results show that the improved algorithm can automatically track the targets in different flight attitudes in real time and accurately calculate their sizes. The improved algorithm is stable in the 1250-frame tracking experiment of DJI Elf 4 UAV with a flying speed of 5 m/s and a flying distance of 100 m. Among them, the fitting error of the target is always less than 2 pixels, while the size calculation error of the target is less than 2.5 cm. This shows the remarkable advantages of Gm-APD LiDAR in detecting “low, slow and small” targets. It is of practical significance to comprehensively improve the ability of UAV detection and C-UAS systems. However, the application of this technology in complex backgrounds, especially in occlusion or multi-target tracking, still faces certain challenges. In order to realize long-distance detection, further optimizing the field of view of the Gm-APD single-photon LiDAR is still a future research direction.

Generation of an optimal triangulated irregular network for topographic surface via optimal transport theory

ABSTRACT A digital elevation model (DEM) is widely recognized as the most effective digital representation of the Earth’s surface and serves as the fundamental platform for simulating various Earth systems. Extensive efforts have been devoted to exploring methods for generating high-fidelity DEM datasets that are computationally efficient for diverse applications. However, the existing methods do not guarantee the optimal digital representation of the Earth’s surface. This study proposed a novel curvature-based geodesic centroidal Voronoi tessellation method for generating a topographic triangulated irregular network (TIN) DEM based on optimal transport theory. This study is the first to present a globally optimized digital representation of the Earth’s surface with a predetermined number of vertices, which is crucial for computational feasibility. This study achieves the optimal TIN by measuring mean curvature and introducing geodesic distances on the topographic surface. Representative vertices that best adapt to the topography are identified through an optimal surface approximation process. Experimental results confirm that the proposed method effectively generates the optimal digital representation of the topographic surface with the lowest elevation errors and minimal deviations from the original topographic features. By generating optimal TIN DEM with any desired number of vertices, the proposed method not only balances high-precision representation and computational efficiency but also offers a novel approach to deepening the understanding of topographic structures. Furthermore, it provides an effective solution for compressing extensive topographic data and facilitating multiscale representation of the Earth’s surface.

Assessment of water quality of the Narmada River (India) using the fuzzy water quality index method

ABSTRACT This study aims to assess the water quality of the Narmada River utilizing the fuzzy water quality index (FWQI) method. In this context, samples of water were gathered from six stations for various parameters such as turbidity, pH, DO, BOD5, TDS, TSS, COD, EC, TH, TA, and chloride from 2017 to 2022. Due to contamination from urbanization, water quality assessment has become essential. To address this requirement, the water quality index (WQI) was developed which incorporates various water quality parameters and expresses total water quality into a single value. Nowadays, a new method, the FWQI has been developed. To develop FWQI, 11 inputs, single output, Mamdani method, And operators, fuzzy inference rules, and centroid methods for defuzzification have been used. The average values of FWQI at the first, second, third, fourth, fifth, and sixth stations were 61.28, 57.66, 62.18, 61.90, 50.00, and 49.96, respectively. Meanwhile, the average values of WQI for the same stations were 57.98, 57.40, 58.97, 58.85, 48.39, and 49.03. The findings from both methods revealed that water quality was poor at the first four stations and good at the last two stations. This new index could serve as an alternative approach to assessing water quality.

Which Method of the Radiologic Measurements of the Angle of Curvature in Idiopathic Scoliosis is the Most Reliable for an Inexperienced Researcher?

The aim of the examination was to determine which of the three measurement methods Cobb (CB), Ferguson (FR), and Centroid (CN) has the best repeatability and reliability when the measurements are made by inexperienced researchers. Three researchers (from the student research group) measured the angle of spine curvature on X-rays of the entire spine in standing anteroposterior view in 50 patients with severe idiopathic scoliosis qualified for surgery. Cobb, Ferguson, and Centroid methods were used. One of the researchers repeated all examinations twice at 3-week intervals. The measurements were compared with each other using the intraclass correlation coefficient (ICC) method. Values less than 0.5 are indicative of poor reliability, values between 0.5 and 0.75 indicate moderate reliability, values between 0.75 and 0.9 indicate good reliability and values greater than 0.90 indicate excellent reliability. The ICC (inter-rater) between the researchers' measurements was 0.9387 for CB, 0.9169 for FR, and 0.9061 for CR. Whereas the ICC (intra-rater) between measurements taken by a single researcher was 0.9824 for CB, 0.9088 for FR, and 0.9546 for CR. The above results show that Cobb angle measurement method is the most reliable for measuring the curvature angle of the spine for novice researchers. Although it seems to be difficult to measure, it provides the most repeatable results.

Examination of the potential for geothermal energy in parts of the Benue trough, Nigeria, through the use of high-resolution aeromagnetic data

Aeromagnetic data from nine sheets covering parts of Benue Trough Nigeria were analyzed to identify potential geothermal locations. The aeromagnetic data sheets were analyzed with Oasis Montaj 8.4, Matlab 7.5, Arcmap 10.7.1, and Surfer 13 combined. The centroid depth method was used to spectrally evaluate the depth of high-resolution aeromagnetic data. The findings showed that the research area's geothermal heat flow values range from 88.52259 mW/m2 to 166.2844 mW/m2, while the Curie Point Depth values range from 8.55 km to 16.38 km with a mean depth of 12.2068 km. The Geothermal Gradient values vary from 35.40904 ℃/km to 66.51376 ℃/km with a mean value of 48.85639 ℃/km. The region's mean thermal transfer is 122.1410 mW/m2. It appears that the Curie Point Depth is highest for the lowest thermal transfer values and lowest for the highest thermal transfer values. The study area's heat flow measurements indicate that the crust is oceanic. The comprehensive geothermal data this study provides may aid the study area's exploitation of geothermal energy. Given the extremely high heat flow recorded, there is a likelihood that the studied area has geothermal energy sources. As a result, it was observed that practically the entire study region had substantial heat flow (>80 mW/m2), suggesting the possibility of a geothermal energy source.

Adaptive Window Approach for Curie Depth Calculation Based on Modified Centroid Method and the Application in the South China Block

Adaptive Window Approach for Curie Depth Calculation Based on Modified Centroid Method and the Application in the South China Block

Intelligent Perception and Seam Tracking System for Thick Plate Weldments Based on Constant-Focus Optical Path

To achieve efficient and accurate thick plate welding, as well as to precisely extract and plan the paths of complex three-dimensional weld seams in large steel structures, this study introduces a novel vision-guided approach for robotic welding systems utilizing a constant-focus laser sensor. This methodology specifically targets and mitigates several critical shortcomings inherent in conventional vision-guided welding techniques, including limited detection ranges, diminished precision in both detection and tracking, and suboptimal real-time performance. For preprocessed weld images, an improved grayscale extreme centroid method was developed to extract the center of the light stripe. Furthermore, a sophisticated feature point extraction algorithm, which integrates a maximum distance search strategy with a least-squares fitting procedure, was developed to facilitate the precise and timely identification of weld seam characteristic points. To further optimize the outcomes, a cylindrical filtering mechanism was employed to eliminate substantial discrepancies, whereas local Non-Uniform Rational B-Spline (NURBS) curve interpolation was utilized for the generation of smooth and accurate trajectory plans. A spatial vector-based pose adjustment strategy was then implemented to provide robust guidance for the welding robot, ensuring the successful execution of the welding operations. The experimental results indicated that the proposed algorithm achieved a tracking error of 0.3197 mm for welding workpieces with a thickness of 60 mm, demonstrating the method’s substantial potential in the manufacturing sector, especially in the domain of automated welding.

Interpreting the patterns of local failure following postoperative volumetric-modulated arctherapy in oral cavity and oropharynx cancers: Impact of the different methods of analysis

PurposeIntensity-modulated radiation therapy or volumetric-modulated arctherapy is nowadays the recommended radiation technique for the treatment of head and neck cancers. However, by providing a significant dose gradient between target volumes and organs at risk, there is a risk of target missing and thus recurrence in case of inadequate delineation. It is therefore necessary to determine the origin of these recurrences to improve clinical practice. Over the past years, different methods have been described for the analysis of recurrences. Using the patterns of failure of patients with oral cavity and oropharynx carcinoma, treated with postoperative volumetric-modulated arctherapy in our institution, the purpose of this work was to analyse the sites of local recurrences and to evaluate the disparity in the classification of recurrences when different methods were used. Material and methodsBetween 2011 and 2019, 167 patients who underwent postoperative volumetric-modulated arctherapy for oral cavity or oropharyngeal cancers were included (60 and 40 % respectively). Two or three dose levels were prescribed (54Gy, 59.4/60Gy±66Gy). Local recurrence occurred in 17 patients (10.2 %). We assessed the patterns of local recurrences according to four methods: 1/ volume-based method using the volume overlap between the recurrence volume and initial target volumes; 2/ volume-based method of overlap between the recurrence volume and the 95 % treatment isodose; 3/ point-based method using the position of the barycentre of the recurrence volume; 4/ combined centroid method classifying recurrences according to both the initial target volumes and dose distribution. Each case was reviewed to make a clinical judgment on these classifications and assessed them as “appropriate”, “possible”, or “inappropriate”. ResultsFor the volume-based method using overlap between the recurrence volume and the initial clinical target volume, this classification was clinically judged as inappropriate in 11 out of 17 cases (65 %). For the volume-based method using overlap between the recurrence volume and the 95 % prescribed isodose, this classification was clinically judged as appropriate in 15 out of 17 cases (88 %). For the point-based method, this classification was clinically judged as appropriate in 14 out of 17 cases (82 %). Thirteen out of 17 local recurrences had the same classification between this point-based method and the volume-based method of overlap between the recurrence volume and the 95 % prescribed isodose. For the combined centroid method, among 17 local recurrences nine were classified as type A, two as type B, two as type C, three as type D and one as type E. This classification was clinically judged as appropriate in 15 out of 17 cases (88 %). Only five out of 17 of the local recurrences were classified the same way according to the four different methods (29 %). ConclusionRecurrences that are “marginal” or “outfield” represent a major challenge for intensity-modulated radiation therapy/volumetric-modulated arctherapy quality assurance and improvement of delineation recommendations. To date, there are no published methods that give complete satisfaction.

The Development of Volumetric Quantitative Evaluation Software for Assessing Respiratory-Induced Target Motion.

Purpose We developed a volumetric quantitative evaluation software called vector volume histogram (VVH) to evaluate respiratory-induced organ motion using deformable image registration (DIR). Methods The B-spline-based DIR algorithm was used to compute the deformation vector field (DVF), which included the DVFLR (left-right), DVFAP (anterior-posterior), and DVFCC (craniocaudal). The VVH software was written as a plug-in using Python, thus allowing anyone to easily modify the code. A shifted target within the moving phantom was used to evaluate the performance of the VVH software. The 2 cm diameter target was systematically shifted by 5, 10, 15, and 20 mm in the CC direction. To evaluate respiration-induced target motion, the VVH method was applied during the inhalation and exhalation phases of 4D CT scans in a patient with lung cancer. Length at 5% volume (L5%) and length at 50% volume (L50%​​​​​) were calculated to evaluate the target motion. Results In the phantom study, the VVH software accurately measured target displacements with L5% and L50% values of 5.4 mm and 4.8 mm, 10.4 mm and 9.8 mm, 14.9 mm, and 14.6 mm, and 19.9 mm and 19.6 mm for 5, 10, 15 and 20 mm displacements, respectively. For the lung cancer patient study, the VVH method showed target motion with L5% and L50% values of 1.9 mm and 1.8 mm in LR, 1.9 mm and 0.9 mm in AP, 18.8 mm and 15.8 mm in CC, and 18.9 mm and 15.8 mm in 3D vector. The centroid method measured respiratory tumor motion between the inhalation and exhalation phases as 0.5 mm, 0.7 mm, 13.5 mm, and 13.5 mm in the LR, AP, and CC directions and in the 3D vector. Conclusions The VVH software provided a volumetric quantitative assessment of respiratory-induced target motion and may provide strategic decisions for clinical use at the time of treatment planning.

Applying MCDM to evaluate benchmark scores in the logistics sector for the period 2021–2023: Application to universities in Vietnam

Choosing a university is a crucial decision for each field of study, as it significantly influences the quality of graduates. An important factor in this decision is the university’s annual benchmark scores. The benchmark score represents the minimum score required for admission. This study evaluates the benchmark scores in the logistics sector for several prominent universities in Vietnam during the period 2021–2023. The research process utilized data on the benchmark scores for the years 2021, 2022, and 2023. The weights of these benchmark scores were calculated using the Rank Order Centroid (ROC) method, and the Probability method was employed to compare the benchmark scores of the universities. The analysis identified C3 as the criterion with the highest importance, while U3 emerged as the top-ranked alternative. The two-stage comprehensive sensitivity analysis revealed that universities consistently ranked high or low regardless of the method used to calculate benchmark score weights or the method employed for ranking. Additionally, the smallest weight change that affected the overall Probability ranking was 4.61%. This study provides significant guidance for students in selecting a university for logistics studies and serves as a foundational reference for universities to assess their capabilities in logistics education, thereby fostering healthy competition among institutions.

Green Computing Innovation Index Measurement Using Fuzzy Inference System for Young Inventors

Green Computing Innovation (GCI) aims to address the negative effects of traditional computing on the environment and promote sustainable practices in computing. Subsequently, the Green Computing Innovation Index (GCII) was used to measure young inventors' inventiveness to evaluate from green computing perspectives. Nonetheless, one of the most significant challenges evaluators face in the measurement process includes uncertainty and imprecise data in measuring the young inventors' green computing innovation. Thus, this paper measures the Green Computing Innovation Index (GCII) with a novel approach utilizing Fuzzy Inference System (FIS) with the Mamdani method. The study measures the top 10 young inventors' innovations from the Faculty of Computing and Meta-Technology, UPSI. Fuzzy Inference System will assist in providing improved decision-making solutions for evaluators to prevent any uncertainty and subjectivity during the evaluation process. The development of FIS is based on four key innovation elements. It uses a triangular membership function with Centroid, Middle of Maximum (MOM), Smallest of Maximum (SOM), Bisector and Largest of Maximum (LOM) methods for defuzzification. The centroid defuzzification method is observed to perform better than other defuzzification methods. It is due to the fact that the adopted defuzzification approach is equally sensitive to all key innovation criteria inputs and can truly represent the minor change in the values of any parameters across all products. The centroid method was found to be the most efficient for decision-making, providing precise results with the ability to detect discrepancies as small as 0.001. Thus, the centroid method can improve decision-making efficiency, leading to fairness in selecting the best green computing innovation product. Ultimately, GCII rating contribute mainly on encouraging young inventors to innovate and promote sustainable practices in innovations.

Gradient transformation-weighted centroid: enhancing peak localization precision in the line chromatic confocal sensing system

Line chromatic confocal imaging (LCI) is an advanced system used for quick and accurate three-dimensional surface imaging without vertical mechanical scanning. It is extensively employed for fast industrial inspection. For high-speed and accurate measurement characteristics of the LCI technique, a rapid and accurate peak localization method is important. In this paper, we propose a gradient transform weight centroid method (GTWC) to improve the accuracy of peak positioning, without compromising high-speed characteristics. This method helps reconstruct the 3D profile at a higher axial resolution by reducing the full width at half maximum (FWHM) without altering the original structure of the LCI system. Both simulations and experiments show the feasibility and good performance of this method.

ROC and COPRAS Algorithms in a Decision Support System for Employee Career Assessment

PT. Union Confectionery, a long-established company in the confectionery production industry, had encountered significant challenges in managing employee career progression, including transfers, demotions, and promotions. The primary issue faced was the difficulty in conducting objective employee performance assessments and determining the appropriate criteria for career development. These assessments involved various complex factors such as work experience, discipline, education level, age, job performance, attendance, teamwork, and work ethics. To address these challenges, this study proposed the implementation of a web-based Decision Support System (DSS) utilizing the Rank Order Centroid (ROC) method for criteria weighting and the Complex Proportional Assessment (COPRAS) method for ranking decisions. The implementation of this system was expected to assist the management of PT. Union Confectionery in making more accurate and transparent decisions, while also improving employee satisfaction and operational efficiency. This research also compared previous studies that used similar methods for performance assessment, albeit with a narrower focus, specifically at the supervisory level. The findings from this study were anticipated to provide a comprehensive and innovative solution for employee career management across all levels of PT. Union Confectionery’s organization, as well as contribute to the development of more effective human resource management practices.

An improved takagi - sugeno variable step size perturb and observe MPPT algorithm based lead acid battery charge controller for photovoltaic system

The main objective of photovoltaic system controller is to reduce the price of the kWh compared to traditional fuel-based electricity. Thus, the improvement of simple and low – cost controllers is a major challenge. Perturb and Observe (P&O) is the commonly used algorithm in order to ensure the power maximization of the photovoltaic system, due to its simplicity and effectiveness. However, on the main drawbacks of that algorithm is the fixed step size of perturbation, due to working environmental changes, which require dynamic tuning of the controller parameter. In order to overcome the cited disadvantage, a new Takagi - Sugeno variable step P&O is proposed. Based on the real-time measurements of the solar radiation and temperature, the proposed fuzzy system schedules the appropriate step size based on human expertise. A zero order Takagi – Sugeno with singleton output is proposed. The fuzzification were carried out using triangular and trapezoidal membership functions. The centroid defuzzification method based 25 If -Then rules are adopted. In order to verify the effectiveness of the improved P&O, three scenarios were used. The obtained results show the effectiveness of the proposed controller under shaded conditions and various temperatures.

Distance-Based Localization in Wireless Sensor Network Using Exponential Grey Prediction Model

Localization in wireless sensor network is the key for many applications in which location of occurrence of event is very important. Distance based localization techniques have proved to be more accurate and feasible for the location calculation of the sensor nodes. Moreover, Received Signal Strength Indicator (RSSI) based techniques are well adopted by the researcher to calculate the distance between the sensor nodes. The optimized value of RSSI may estimate the location more precisely. In this paper, Exponential Grey prediction model and weighted predicted RSSI values are utilized to provide estimated values of RSSI which are then optimized using an objective function. The decreasing term used in the differential equation of the Grey prediction model provides better prediction accuracy which if optimized provides efficient result. The conventional Grey prediction model is limited by the sample space and scope of applications. The Exponential Grey prediction model used in the proposed algorithm removes those drawbacks and can be applied in real time applications with larger sample space. The objective function used in the proposed algorithm uses weighting factor to be applied to Grey predicted RSSI and weighted predicted RSSI to estimate the RSSI value more accurately. The objective function calculates more near values of RSSI to localize the node more accurately. The simulation results obtained are compared with the latest techniques of RSSI calculation like weighted centroid method, RSSI quantization, TMA and FRBW algorithm. The results show that the proposed algorithm outperform the existing methods for RSSI estimation.

PENERAPAN METODE WASPAS DAN ROC DALAM PEMILIHAN BANK TERBAIK UNTUK PELAYANAN MOBILE BANKING

Technological developments encourage increased use of banking services via mobile devices. Choosing the optimal bank for mobile banking services is an important challenge in meeting customer needs. This research uses a Decision Support System with the application of the Weighted Aggregated Sum Product Assessment (WASPAS) and Rank Order Centroid (ROC) methods. The WASPAS method helps find positive and negative ideal solutions in solving problems. The ROC method was chosen to reduce the level of subjectivity and uncertainty in determining weights. The weights in ROC are determined based on priority criteria. In this research, 5 alternatives are used, namely BCA Mobile, Livin' by Mandiri, BSI Mobile, BNI Mobile Banking, BRImo BRI, and apply criteria such as security, many enthusiasts, user friendly, rating, and cost. This research aims to provide a comprehensive picture of the quality of mobile banking services provided by various banks so as to help customers choose mobile banking according to the criteria they need. The research results show that BCA Mobile owned by BCA bank is the best alternative with the highest value of 0.940, followed by BRImo with a value of 0.613, BNI Mobile with a value of 0.612, Livin' by Mandiri 0.611, and the lowest value of 0.323 for BSI Mobile. Thus, BCA Mobile is declared the best choice for mobile banking services.

Comparison of Various Ranking Methods with Pareto Distribution Methods in Imprecise Data

The Pareto distribution has seen wide application in many different fields recently. Nowadays, the data we collect may be imprecise and contain uncertainties. This means that the values we gather cannot necessarily be considered a single, definitive value. In this paper, it is explored how various estimation methods like the method of moments, maximum likelihood estimation, and least squares estimation are used to determine the parameters of a Pareto distribution when the data is fuzzy or uncertain in nature. When making decisions, ranking methods are often heavily relied upon. For this reason, the application of several different ranking techniques to triangular fuzzy numbers (TFNs) is considered. The ranking methods for TFNs are found to tend to produce estimated parameters that are quite similar or even identical. The estimating parameters of a Pareto distribution can be solved for when dealing with fuzziness by the use of ranking methods such as alpha-cut, Yeager’s method, sub-interval method, Pascal method, magnitude method, and centroid method on the TFNs. By comparing the results of these various ranking techniques, an analysis is conducted to whether the estimated parameters of the Pareto distribution are consistent across the different ranking methods for TFNs get the uniqueness of the solution or differ. The decision making done by among the various ranking techniques, which method preferable for future comparison with extended of new ranking techniques. The conclusion is that the simulation results match up well with actual application data, serving as a good example for explaining this overall strategy.

Decorrelated nearest shrunken centroids for tensor data

AbstractThe nearest shrunken centroids (NSC) method is an efficient and accurate classifier. However, it is incapable of modelling correlation among predictors. Moreover, many contemporary datasets have tensor predictors that cannot be directly handled by NSC. We tackle these challenges by proposing a new distance‐based classifier, tensor decorrelated NSC (TDNSC). TDNSC leverages the popular separable covariance structure on tensor data to decorrelate data and allow easy application of NSC afterwards. Unlike existing tensor classifiers that often rely on complicated iterative algorithms, TDNSC has analytical solutions. The theoretical properties and empirical results suggest that TDNSC is a promising method for tensor classification.

Comparative investigation on the performance of spot positioning sensors in point-autofocus microscopy

As products evolve towards greater precision and intelligence, their components also develop towards miniaturisation and increasing complexity. In the era of intelligence, the demand for complex small components continues to grow. The point-autofocus microscopy (PAM), with its attributes such as small measurement spots and high vertical resolution, holds a comprehensive advantage in instruments for measuring the geometry of complex small components. The performance of the spot positioning sensor directly determines the capability of the PAM to measure complex small components. This study introduces the basic measurement principles of the PAM and commonly used spot positioning sensors. The analysis explored the feasibility of applying different sensors to the PAM. An automatic segmentation and weighted localisation algorithm of the spot based on the centroid method was proposed. Using this algorithm, the spot positioning error of the complementary metal–oxide–semiconductor (CMOS) sensor can be controlled within 0.48 μm, with a measurement uncertainty of no more than ±0.19 μm. This study focuses on comparing the performance of CMOS and position-sensitive detector sensors with the performance of seven indicators. In addition, a measurement system of the PAM was constructed, and measurements of the test object were conducted using both types of sensors. The experimental results show that when the measurement system employs the CMOS sensor, it achieves higher measurement accuracy. The maximum measurement error was approximately 0.22 μm, with a measurement uncertainty of no more than ±0.10 μm.

Surrogate-Assisted Differential Evolution with multiple sampling mechanisms for high-dimensional expensive problems

Recently, Surrogate-Assisted Evolutionary Algorithms (SAEAs) have been widely employed in solving Expensive Optimization Problems (EOPs) due to their efficiency in obtaining satisfactory solutions with limited resources. By leveraging historical data to construct surrogate models for approximation, SAEAs can significantly reduce the number of expensive fitness evaluations for EOPs. A hierarchical SAEA optimization framework based on evolutionary sampling methods has achieved remarkable success in High-dimensional EOPs (HEOPs), which can effectively balance the exploration and exploitation capabilities. However, the majority of existing hierarchical SAEAs focus on either switching between different sampling strategies or enhancing a single sampling strategy, potentially overlooking the potential to improve multiple sampling strategies simultaneously. In this paper, we propose a Surrogate-Assisted Differential Evolution with Multiple Sampling Mechanisms (SADE-MSM) to tackle HEOPs, incorporating three sampling strategies with different mechanisms. The contributions of SADE-MSM are summarized as follows: 1) A centroid sampling method is applied before iterative optimization to enhance the early exploration ability; 2) An improved global prescreening sampling strategy is introduced to balance the exploration and exploitation capabilities; 3) A local search sampling with the adaptive optimal region strategy is proposed, significantly improving the exploitation ability. To validate the performance of SADE-MSM, we compared it with the state-of-the-art SAEAs on benchmark problems with dimensions ranging from 30 to 500. Experimental results demonstrate that SADE-MSM has a significant performance superiority.