Competitive Method Research Articles

Kinematic Calibration of a Redundant Robot in Closed-Loop System Using Indicated Competitive Swarm Method

Kinematic Calibration of a Redundant Robot in Closed-Loop System Using Indicated Competitive Swarm Method

Sustainable logistics development strategy based on SWOT and analytic hierarchy process.

Due to the implementation of sustainable green development strategy, the study uses the competitive situation method and analytical hierarchy process of logistics enterprises to conduct structural analysis and weight calculation of factor indicators for their development strategies. Afterwards, the exponential smoothing prediction method was used to weight the logistics data. Moreover, through comparative analysis of the indicator evaluation system, the average weights of the advantageous factors of logistics enterprises were found to be 0.0232, 0.1102, 0.0522, and 0.0468, respectively. The energy consumption, transportation volume, and turnover of logistics transportation increased by 68.9% and 52.3% in 2022, respectively, which had a significant impact on the sustainable development of the logistics industry. It has been demonstrated that a sustainable logistics development strategy based on the enterprise competitive situation method and the analytic hierarchy process is both feasible and superior. Furthermore, this strategy has the potential to enhance the environmental benefits and green development of the economy and society.

Dual Contrastive Learning for Cross-Domain Named Entity Recognition

Benefiting many information retrieval applications, named entity recognition (NER) has shown impressive progress. Recently, there has been a growing trend to decompose complex NER tasks into two subtasks (e.g., entity span detection (ESD) and entity type classification (ETC), to achieve better performance. Despite the remarkable success, from the perspective of representation, existing methods do not explicitly distinguish non-entities and entities, which may lead to ESD errors. Meanwhile, they do not explicitly distinguish entities with different entity types, which may lead to entity type misclassification. As such, the limited representation abilities may challenge some competitive NER methods, leading to unsatisfactory performance, especially in the low-resource setting (e.g., cross-domain NER). In light of these challenges, we propose to utilize contrastive learning to refine the original chaotic representations and learn the generalized representations for cross-domain NER. In particular, this article proposes a dual contrastive learning model (Dual-CL), which respectively utilizes a token-level contrastive learning module and a sentence-level contrastive learning module to enhance ESD, ETC for cross-domain NER. Empirical results on 10 domain pairs under two different settings show that Dual-CL achieves better performances than compared baselines in terms of several standard metrics. Moreover, we conduct detailed analyses to are presented to better understand each component’s effectiveness.

Unveiling connections: bisphenol A and vitamin D dynamics in breast milk among healthy lactating mothers

ABSTRACT Bisphenol A (BPA) has negative impact on human health. This study aims to explore the association between the levels of BPA and 25(OH) vitamin D in breastmilk. BPA exposure and vitamin D sources were recorded on the study form. Breastmilk 25(OH) vitamin D was measured by the competitive enzyme-linked immunosorbent assay method, and BPA was analyzed by fluorescence detector high pressure liquid chromatography technique. Multiple logistic regression analysis were performed for association. Among 70 lactating mothers, the median 25(OH) vitamin D level was 12.6 ng/mL. In 92.9% of breastmilk samples, detectable levels of BPA were found, with a median BPA concentration of 2.56 ng/mL. However, both univariate and multivariate analyses failed to reveal any association between breastmilk BPA and 25(OH) vitamin D levels. This lack of association in healthy lactating mothers, highlighting the need for further research to comprehensively understand the intricate dynamics of these factors.

Non-stationary mechanical sound source separation: An all-neural beamforming network driven by time–frequency convolution and self-attention

To tackle the challenging task of extracting target signal features amidst significant interference from complex, time-varying non-stationary noise in industrial scenarios, we propose a novel time-domain all-neural beamforming network tailored for non-stationary mechanical sound source separation, named TFANBNet. Leveraging time–frequency convolution and self-attention, TFANBNet promised robust performance in challenging acoustic environments. First, the proposed time–frequency convolution module employs parameterized complex-valued convolutional layers to simulate time–frequency transformations, thereby implicitly extracting time–frequency features. Then, the proposed adaptive attention transformer network enhances local attention interaction by integrating intermediate features, effectively enhancing long-range context modeling. Third, the beamforming module utilizes deep neural networks for beamforming weight estimation, replacing conventional noise covariance matrix computations, which have inherent performance limitations. Finally, the experimental results on the multi-channel spatial reverberation dataset synthesized from the MIMII dataset show that the proposed TFANBNet yields superior separation performance and generalization capabilities compared to the considered competitive methods.

BIF-Net: Boundary information fusion network for abdominal aortic aneurysm segmentation

The accurate abdominal aortic aneurysm (AAA) segmentation is significant for assisting clinicians in diagnosis and treatment planning. However, existing segmentation methods exhibit a low utilization rate for the semantic information of vessel boundaries, which is disadvantageous for segmenting AAA with significant scale variability of vessel diameter (diameter ranges from 4 mm to 85 mm). To tackle this problem, we introduce a boundary information fusion network (BIF-Net) specially designed for AAA segmentation. BIF-Net initially constructs convolutional kernel functions based on Gabor and Sobel operators, enriching the global semantic features and localization information through the Gabor and Sobel dilated convolution (GSDC) module. Additionally, BIF-Net supplements lost boundary feature information during the sampling process through the guided filtering feature supplementation (GFFS) module and the channel-spatial attention module (CSAM), enhancing the ability to capture targets with shape diversity and boundary features. Finally, we introduce a boundary feature loss function to alleviate the impact of the imbalance between positive and negative samples. The results demonstrate that BIF-Net outperforms current state-of-the-art methods across multiple evaluation metrics, achieving the highest Dice similarity coefficient (DSC) accuracies of 93.29 % and 91.01 % on the preoperative and postoperative datasets, respectively. Compared to the state-of-the-art methods, BIF-Net improves DSC by 6.86 % and 3.85 %. Due to the powerful boundary feature extraction ability, the proposed BIF-Net is a competitive AAA segmentation method exhibiting significant potential for application in diagnosis and treatment processes of AAA.

Development of a chemiluminescent immunoassay based on magnetic solid phase for quantification of homocysteine in human serum

BackgroundHomocysteine (HCY) is a sulfur-containing amino acid that is an independent or important risk factor for the occurrence of many chronic diseases and is one of the most important indicators for determining health risks. However, existing HCY detection methods do not meet the requirements of clinical diagnosis. Therefore, there is an urgent need to establish new detection methods to meet the needs of clinical detection.ResultsIn this study, we used the principle of competitive method to establish a new method for the determination of HCY in human serum using a chemiluminescent enzyme immunoassay in conjunction with a chemiluminescent assay instrument that uses magnetic microparticles as the solid phase of the immunoreaction. The established method achieved satisfactory results in terms of minimum detection limit, specificity, accuracy, and clinical application. The limit of detection was 0.03 ng/mL. The intra-assay coefficient of variation (CV) was 1.94–5.05%, the inter-assay CV was 2.29–6.88%, and the recovery rate was 88.60–93.27%. Cross-reactivity with L-cysteine ranged from 0.0100 to 0.0200 μmol/L, and cross-reactivity with glutathione ranged from 0.0100 to 0.200 μmol/L, all of which were less than the limit of detection (LoD) of this method. The linear factor R of this method was greater than 0.99.ConclusionsIn summary, the developed method showed a good correlation with the product from Abbott. A total of 996 clinical patients with cardiovascular diseases were evaluated using the method developed in this study.

Integrating AI with medical industry chain data: enhancing clinical nutrition research through semantic knowledge graphs.

In clinical nutrition research, the medical industry chain generates a wealth of multidimensional spatial data across various formats, including text, images, and semi-structured tables. This data's inherent heterogeneity and diversity present significant challenges for processing and mining, which are further compounded by the data's diverse features, which are difficult to extract. To address these challenges, we propose an innovative integration of artificial intelligence (AI) with the medical industry chain data, focusing on constructing semantic knowledge graphs and extracting core features. These knowledge graphs are pivotal for efficiently acquiring insights from the vast and granular big data within the medical industry chain. Our study introduces the Clinical Feature Extraction Knowledge Mapping ( ) model, designed to augment the attributes of medical industry chain knowledge graphs through an entity extraction method grounded in syntactic dependency rules. The model is applied to real and large-scale datasets within the medical industry chain, demonstrating robust performance in relation extraction, data complementation, and feature extraction. It achieves superior results to several competitive baseline methods, highlighting its effectiveness in handling medical industry chain data complexities. By representing compact semantic knowledge in a structured knowledge graph, our model identifies knowledge gaps and enhances the decision-making process in clinical nutrition research.

AeroPath: An airway segmentation benchmark dataset with challenging pathology and baseline method.

To improve the prognosis of patients suffering from pulmonary diseases, such as lung cancer, early diagnosis and treatment are crucial. The analysis of CT images is invaluable for diagnosis, whereas high quality segmentation of the airway tree are required for intervention planning and live guidance during bronchoscopy. Recently, the Multi-domain Airway Tree Modeling (ATM'22) challenge released a large dataset, both enabling training of deep-learning based models and bringing substantial improvement of the state-of-the-art for the airway segmentation task. The ATM'22 dataset includes a large group of COVID'19 patients and a range of other lung diseases, however, relatively few patients with severe pathologies affecting the airway tree anatomy was found. In this study, we introduce a new public benchmark dataset (AeroPath), consisting of 27 CT images from patients with pathologies ranging from emphysema to large tumors, with corresponding trachea and bronchi annotations. Second, we present a multiscale fusion design for automatic airway segmentation. Models were trained on the ATM'22 dataset, tested on the AeroPath dataset, and further evaluated against competitive open-source methods. The same performance metrics as used in the ATM'22 challenge were used to benchmark the different considered approaches. Lastly, an open web application is developed, to easily test the proposed model on new data. The results demonstrated that our proposed architecture predicted topologically correct segmentations for all the patients included in the AeroPath dataset. The proposed method is robust and able to handle various anomalies, down to at least the fifth airway generation. In addition, the AeroPath dataset, featuring patients with challenging pathologies, will contribute to development of new state-of-the-art methods. The AeroPath dataset and the web application are made openly available.

FHLight: A novel method of indoor scene illumination estimation using improved loss function

In augmented reality tasks, especially in indoor scenes, achieving illumination consistency between virtual objects and real environments is a critical challenge. Currently, mainstream methods are illumination parameters regression and illumination map generation. Among these two categories of methods, few works can effectively recover both high-frequency and low-frequency illumination information within indoor scenes. In this work, we argue that effective restoration of low-frequency illumination information forms the foundation for capturing high-frequency illumination details. In this way, we propose a novel illumination estimation method called FHLight. Technically, we use a low-frequency spherical harmonic irradiance map (LFSHIM) restored by the low-frequency illumination regression network (LFIRN) as prior information to guide the high-frequency illumination generator (HFIG) to restore the illumination map. Furthermore, we suggest an improved loss function to optimize the network training procedure, ensuring that the model accurately restores both low-frequency and high-frequency illumination information within the scene. We compare FHLight with several competitive methods, and the results demonstrate significant improvements in metrics such as RMSE, si-RMSE, and Angular error. In addition, visual experiments further confirm that FHLight is capable of generating scene illumination maps with genuine frequencies, effectively resolving the illumination consistency issue between virtual objects and real scenes. The code is available at https://github.com/WA-tyro/FHLight.git.

3-D Brain Reconstruction by Hierarchical Shape-Perception Network From a Single Incomplete Image.

3-D shape reconstruction is essential in the navigation of minimally invasive and auto robot-guided surgeries whose operating environments are indirect and narrow, and there have been some works that focused on reconstructing the 3-D shape of the surgical organ through limited 2-D information available. However, the lack and incompleteness of such information caused by intraoperative emergencies (such as bleeding) and risk control conditions have not been considered. In this article, a novel hierarchical shape-perception network (HSPN) is proposed to reconstruct the 3-D point clouds (PCs) of specific brains from one single incomplete image with low latency. A branching predictor and several hierarchical attention pipelines are constructed to generate PCs that accurately describe the incomplete images and then complete these PCs with high quality. Meanwhile, attention gate blocks (AGBs) are designed to efficiently aggregate geometric local features of incomplete PCs transmitted by hierarchical attention pipelines and internal features of reconstructing PCs. With the proposed HSPN, 3-D shape perception and completion can be achieved spontaneously. Comprehensive results measured by Chamfer distance (CD) and PC-to-PC error demonstrate that the performance of the proposed HSPN outperforms other competitive methods in terms of qualitative displays, quantitative experiment, and classification evaluation.

Mass finishing of additively manufactured AISI 316L using pecan nutshells as sustainable abrasive media

Abrasive processes represent the main methods for obtaining high-quality surface finish in metal parts. As one main characteristic, these processes have a relatively high consumption of energy per material volume removed, as well as many inputs, such as abrasive tools and equipment, and, consequently, a high associated environmental impact. Additive manufacturing is becoming a competitive method to produce customized parts, but the surface finish is often not good enough for the application, and post-processing is needed. In this work, a mass finishing process using a planetary mill with abrasive media made of sustainable materials (pecan nutshells) was used on AISI 316L stainless steel samples manufactured by selective laser melting (SLM). The main goal was to perform a multicriteria analysis of the sample surface before and after post-processing. Through the analysis, it could be observed that the nutshell abrasive media has results close to those of traditional ceramic media. The increase in the rotation speed of the planetary mill, increase of finishing time, and use of abrasive pastes can further increase the effectiveness of the process.

USMDA: Unsupervised Multisource Domain Adaptive ADHD prediction model using neuroimaging

There is an increasing number of large-scale cross-site database collections of neuroimaging markers (sMRI and fMRI) for studying neurodevelopmental illnesses (NDDs). Although a huge amount of data favors machine learning-based categorization algorithms, the unique heterogeneity of each site can impair cross-site generalization capacity. It is critical to create Unsupervised domain adaption methods for NDDs because obtaining appropriate diagnoses or labeling for NDDs might be problematic. In our work, we focus on Attention-deficit/hyperactivity disorder, which is the most common and frequently co-occurring NDD. We present an unsupervised multisource domain adaptation network (USMDA) with four primary components: Domain Alignment Module, Discrepancy Estimator, Pre-trained Model Generator, and Unsupervised Network. The Domain Alignment module is intended to incrementally and effectively align graph representations of the source and target domains. At the same time, the binary cross entropy regularizer is introduced for the first time during the training of a model learned on multiple source domains to improve existing feature alignment methods such as Transfer Joint Matching (TJM) and Joint Distribution Adaptation (JDA) by learning good unsupervised features. In an unsupervised network, the grid search optimization technique generates the optimal pseudo labels for unlabeled target data. We validate our proposed technique first on existing feature-level DA methods such as JDA, TJM, and Correlation alignment (CORAL), on the publically accessible dataset ADHD-200 and then by using binary cross-entropy in existing DA methods such as TJMCE and JDACE. The experimental results show that our proposed USMDAJDACE method when applied to multisite sMRI and fMRI ADHD data, can significantly outperform competitive methods for multi-center ADHD diagnosis.

Development of an Ultrasensitive Competitive Double-Enzyme Cascade Fluorescence Signal Amplification Method for Factor XIa Inhibitor Screening.

Coagulation factor XIa (FXIa) is associated with a low risk of bleeding and has been identified as an effective and safe target for the development of novel anticoagulant drugs. In this study, we established an ultrasensitive competitive dual-enzyme cascade signal amplification method for the quantitative analysis and screening of FXIa inhibitors. Due to the specific recognition of FXIa's active site by the aptamer AptE40, the AptE40-QDs-EK recognition probe modified with enterokinase (EK) and the aptamer AptE40, was attached to the MNPs-FXIa capture probe. When FXIa inhibitor was present, it competed with AptE40 for binding to FXIa, resulting in the detachment of AptE40-QDs-EK from MNPs-FXIa. After magnetic separation, the enterokinase of AptE40-QDs-EK in the supernatant hydrolyzed N-terminal hexapeptide of trypsinogen, leading to the production of a large amount of trypsin as part of the first-stage signal cascade amplification. Next, trypsin could hydrolyze the hexameric arginine peptide (RRRRRR, R6), leading to the dissociation of RQDs from the R6-RQDs signal probe; this resulted in a dramatic increase in the fluorescence intensity of the supernatant as the second-stage signal cascade was amplified. The feasibility of the method was investigated using the FXIa inhibitor aptamer FELIAP as a positive model drug. Furthermore, the method was applied to screen the FXIa inhibitors in Eupolyphaga sinensis Walker. Two fractions with more active anticoagulated ingredients were successfully identified and validated via the conventional method, and the results were consistent. The established method provides a key technique for the sensitive detection, high-throughput analysis, and screening of the FXIa inhibitors.

Optimizing Convolutional Neural Network Architectures

Convolutional neural networks (CNNs) are commonly employed for demanding applications, such as speech recognition, natural language processing, and computer vision. As CNN architectures become more complex, their computational demands grow, leading to substantial energy consumption and complicating their use on devices with limited resources (e.g., edge devices). Furthermore, a new line of research seeking more sustainable approaches to Artificial Intelligence development and research is increasingly drawing attention: Green AI. Motivated by an interest in optimizing Machine Learning models, in this paper, we propose Optimizing Convolutional Neural Network Architectures (OCNNA). It is a novel CNN optimization and construction method based on pruning designed to establish the importance of convolutional layers. The proposal was evaluated through a thorough empirical study including the best known datasets (CIFAR-10, CIFAR-100, and Imagenet) and CNN architectures (VGG-16, ResNet-50, DenseNet-40, and MobileNet), setting accuracy drop and the remaining parameters ratio as objective metrics to compare the performance of OCNNA with the other state-of-the-art approaches. Our method was compared with more than 20 convolutional neural network simplification algorithms, obtaining outstanding results. As a result, OCNNA is a competitive CNN construction method which could ease the deployment of neural networks on the IoT or resource-limited devices.

Suppressed possibilistic fuzzy c-means clustering based on shadow sets for noisy data with imbalanced sizes

Possibilistic fuzzy c-means clustering (PFCM) algorithm generates fewer overlapping clustering centers than the possibilistic c-means clustering (PCM) algorithm and possesses better noise immunity than fuzzy c-means clustering (FCM) algorithm. However, with the increasing noise intensity and number of clusters, PFCM still faces the problem of getting partially overlapping clustering centers or mislocated centers in noise regions. Moreover, the sample-size imbalance increasingly intensifies the difficulty of positioning centers of small clusters. To solve the above problems, a suppressed possibilistic fuzzy c-means clustering algorithm based on shadow sets (S-SPFCM) is proposed by introducing the shadow set theory and the "suppressed competitive learning" strategy. Firstly, KL divergence is introduced in the objective function of PFCM to increase the anti-noise robustness of fuzzy memberships against long-range noise and outliers. Secondly, to reduce the number of overlapping centers caused by possibilistic memberships, the shadow set theory is introduced to divide each class adaptively into three regions (core, shadow, and exclusion regions) by an uncertainty balance method. The suppressed competitive learning method is extended by modifying the memberships of points within the three regions, thus artificially guiding the iterative track of clustering centers. Meanwhile, to further reduce the influence of imbalanced sizes, a scheme to reset mislocated centers in the core and shadow regions is also designed. In addition, to improve the segmentation performance of S-SPFCM for noisy images, a suppressed possibilistic fuzzy c-means clustering algorithm based on shadow sets and local information (SL-SPFCM) is also proposed. The SL-SPFCM first improves the Euclidean distance by a distance filtering scheme. Then SL-SPFCM introduces the local median membership of each pixel into the KL divergence of the objective function. Finally, experiments on synthetic datasets and color images which are characteristic of imbalanced sizes and noise injection demonstrate the proposed S-SPFCM and SL-SPFCM algorithms achieve smaller center deviations and higher clustering accuracy compared with several state-of-the-art clustering algorithms.

Training Methods and Competitive Ability of High School Aerobics Athletes

This study is based on the relationship between the training methods and influencing factors of high school aerobics athletes' competitive ability, laying a foundation for improving the competitive ability of aerobics athletes. The results show that the improvement of aerobics athletes' training methods in training, such as Training goals and plans, Training content and methods, Training intensity and quantification, Training cycle and rhythm, Coaching team and support system, and Cultivation of athletes' psychological quality, all contribute to the improvement of competitive ability, thereby improving the sports performance of aerobics athletes. Through descriptive correlation design and SPSS analysis of 65 students' data, it was found that aerobics athletes' interest in high-dimensional competitive ability, including Technical level, Physical fitness, Artistic expression, Mental quality, Spirit of teamwork and Training goals and plans, Training content and methods, Training intensity and quantification, Training cycle and rhythm, Coaching team and support system, Cultivation of athletes' psychological quality in training methods, etc. In order to improve the competitive ability and training method level of aerobics athletes, teachers should take relevant measures in teaching, such as not considering sex, grade, and training years as the main factors for evaluating the competitive ability and training methods of aerobics athletes, emphasizing the actual content of course design and the organization of teachers, helping aerobics athletes to establish a good training method system and improve the development level of aerobics competitive ability.

Coarse-Fine Nested Network for Weakly Supervised Group Activity Recognition.

Weakly supervised group activity recognition (WSGAR) aims at identifying the overall behavior of multiple persons without any fine-grained supervision information (including individual position and action label). Traditional methods usually adopt a person-to-whole way: detect persons via off-the-shelf detectors, obtain person-level features, and integrate into the group-level features for training the classifier. However, these methods are unflexible due to serious reliance on the quality of detectors. To get rid of the detector, recent works learn several prototype tokens from noisy grid features with learnable weights directly, which treat all the local visual information equally and bring in redundant and ambiguous information to some extent. To this end, we propose a novel coarse-fine nested network (CFNN) to coarsely localize the key visual patches of activity and further finely learn the local features, as well as the global features. Specifically, we design a nested interactor (NI) to progressively model the spatiotemporal interactions of the learnable global token. According to the cue of spatial interaction in NI, we localize several key visual patches via a new coarse-grained spatial localizer (CSL). Then, we finally encode these localized visual patches with the help of global spatiotemporal dependency via a new fine-grained spatiotemporal selector (FSS). Extensive experiments on Volleyball and NBA datasets demonstrate the effectiveness of the proposed CFNN compared with the existing competitive methods. Code is available at: https://github.com/gexiaojingshelby/CFNN.

Preparation and application of multiple particle binding-liposomes for electrochemiluminescent signal amplification in bioassays.

In this study, multiple particle binding-liposomes (MPB-Lips), encapsulating the luminophore tris(2',2-bipyridyl)ruthenium (II) complex ([Ru(bpy)3]2+), were developed as an electrochemiluminescence (ECL) signal amplifier and were applied to detect the model analyte streptavidin (SA) using the indirect competitive ECL method. The MPB-Lips were prepared by mixing various ratios of two different liposomes-one containing a phospholipid with a primary amine group and a biotinyl group (BIO/NH2-Lip) and one containing a phospholipid with an N-hydroxysuccinimide group (NHS-Lip) to allow binding between particles via amide bonds. Quartz crystal microbalance analysis using SA-modified gold-coated quartz crystals showed that the frequency shift values of MPB-Lips gradually decreased in the order BIO/NH2-Lip:NHS-Lip = 1:0 < 1:1 < 1:3 < 1:5. This indicated that MPB-Lips were successfully formed. The indirect competitive ECL method using SA-modified gold electrodes showed that the 1:5-Lip system had greater sensitivity than the 1:0-Lip system-the limit of detection and quantification values for the systems were 1.84 and 6.30μgmL-1 for 1:0-Lip, and 1.20 and 1.74μgmL-1 for 1:5-Lip. Finally, the recovery of SA spiked in fetal bovine serum samples using the 1:5-Lip system showed good accuracy and precision with a recovery rate of 83-106% and relative standard deviation of 4-14%. Our study demonstrated that the MPB-Lips system was an effective and useful ECL amplifier and the ECL method using MPB-Lips could be applied to detect an analyte in a real sample.

A tree seed algorithm with multi-strategy for parameter estimation of solar photovoltaic models

Tree seed algorithm, which is one of the metaheuristics algorithms recently proposed for the solution of continuous optimization problems, has an effective algorithmic structure inspired by the relation between trees and seeds. At the same time, the use of two different solution generation mechanisms by depending on the control parameter in TSA aims to balance the exploration and exploitation capabilities of the algorithm. However, when the structure of the algorithm is examined in detail, it is seen that there are some disadvantages such as loss of population diversity and getting stuck in local minimums. To overcome these disadvantages in the basic algorithm, three different approaches (self-adaptive weighting mechanism, chaotic elite learning approach and experience-based learning method) were proposed to TSA under the name of multi-strategies in this study. The algorithm improved with these approaches is named as the multi-strategy-based tree seed algorithm (MS-TSA). MS-TSA was first tested on CEC2017 functions. Then MS-TSA was applied to the problems in the CEC2020 competition and compared with the results of the best performing algorithms in this competition. As a result of the comparisons, MS-TSA was found to be a competitive method on solving benchmark functions. Then, parameter estimation of single diode, double diode and photovoltaic module models using the input data of various solar panels was carried out by the MS-TSA. The results obtained with MS-TSA were compared with both the results of the basic TSA and the results of well-known algorithms in the literature. The results obtained are 9.8642E-04, 9.8356E-04, 2.4251E-03, 1.7534E-03 respectively. As a result of the comparative analysis, the lowest RMSE value was obtained by MS-TSA. In addition, comprehensive performance analyzes of the algorithms were made with the convergence curve, boxplots, current (I) - voltage (V) and power (P) - voltage (V) characteristic curves obtained according to the experimental results. As a result of the experiments and analyses, MS-TSA was found to be a more successful method than the compared algorithms in parameter estimation of PV models.