Simulation Experiments Research Articles

An Innovative Nonlinear Bounded Component Analysis Algorithm Based on Multivariate Nonlinear Chirp Mode Decomposition

In complex and diverse practical application scenarios, the challenge of blind source separation under underdetermined and nonlinear conditions is often encountered. To address this challenge, this paper proposes an innovative underdetermined nonlinear bounded component analysis method. This method first employs Multivariate Nonlinear Chirp Mode Decomposition (MNCMD) to process and reconstruct the observed signals, transforming the original underdetermined problem into a positive definite problem. Subsequently, Gaussianization techniques are introduced as a means of nonlinear compensation, successfully converting the nonlinear model into an analyzable linear model, laying a solid foundation for subsequent signal separation. Finally, the signal is separated by the bounded component analysis method, which does not require the source signals to be independent of each other. To validate the effectiveness and superiority of the proposed algorithm, detailed simulation experiments were designed and implemented. The experimental results demonstrate that compared to traditional underdetermined blind source separation algorithms, the algorithm presented in this paper exhibits significant advantages in terms of universality, convergence speed, separation accuracy, and robustness. Furthermore, this paper successfully applies the algorithm to the blind extraction of fetal electrocardiogram (FECG) signals from real datasets. The experimental results show that the algorithm can rapidly and effectively extract clearer and more accurate FECG signals, demonstrating its great potential and value in practical applications.

Application and practice of LILCEO sintering proportion optimization algorithm in sintering plant

Purpose To ensure the stable operation of ironmaking process and the quality and output of sinter, the multi-objective optimization of sintering machine batching process was carried out. Design/methodology/approach The purpose of this study is to establish a multi-objective optimization model with iron taste content and batch cost as targets, constrained by field process requirements and sinter quality standards, and to propose an improved balance optimizer algorithm (LILCEO) based on a lens imaging anti-learning mechanism and a population redundancy error correction mechanism. In this method, the lens imaging inverse learning strategy is introduced to initialize the population, improve the population diversity in the early iteration period, avoid falling into local optimal in the late iteration period and improve the population redundancy error correction mechanism to accelerate the convergence rate in the early iteration period. Findings By selecting nine standard test functions of BT series for simulation experiments, and comparing with NSGA-?, MOEAD, EO, LMOCSO, NMPSO and other mainstream optimization algorithms, the experimental results verify the superior performance of the improved algorithm. The results show that the algorithm can effectively reduce the cost of sintering ingredients while ensuring the iron taste of sinter, which is of great significance for the comprehensive utilization and quality assurance of sinter iron ore resources. Originality/value An optimization model with dual objectives of TFe content and raw material cost was developed taking into account the chemical composition and quality indicators required by the blast furnace as well as factors such as raw material inventory and cost constraints. This model was used to adjust and optimize the sintering raw material ratio. Addressing the limitations of existing optimization algorithms for sintering raw materials including low convergence accuracy slow speed limited initial solution production and difficulty in practical application we proposed the LILCEO algorithm. Comparative tests with NSGA-III MOEAD EO LMOCSO and NMPSO algorithms demonstrated the superiority of the proposed algorithm. Practical applications showed that the proposed method effectively overcomes many limitations of the current manual raw material ratio model providing scientific and stable decision-making guidance for sintering production operations.

Critical-information-based attack-defense strategy for nonlinear systems: an encoding-decoding scheme

This paper investigated both attack design and defense framework for a class of nonlinear cyber-physical systems. Firstly, a novel critical-information-based attack policy is developed from the attackers' angle, resulting in severe destructiveness by targeting crucial packers evaluated by the proposed importance standard. Subsequently, it is proven theoretically that there are positive correlation among attack parameters, attack success probability, and system performance. Secondly, from a security defender's perspective, a security protection strategy based on an encoding-decoding mechanism is proposed, founded on a thorough understanding of the attack mechanism. This approach aims to mislead attackers by preventing them from obtaining correct data packets, thereby leading to incorrect attack judgments, and ultimately enhancing system estimation performance through secure data transmission. Finally, two simulation experiments verify the effectiveness of the proposed critical-information-based attack strategy and the encoding-decoding mechanism.

Hybrid plug-and-play CT image restoration using nonconvex low-rank group sparsity and deep denoiser priors

Objective. Low-dose computed tomography (LDCT) is an imaging technique that can effectively help patients reduce radiation dose, which has attracted increasing interest from researchers in the field of medical imaging. Nevertheless, LDCT imaging is often affected by a large amount of noise, making it difficult to clearly display subtle abnormalities or lesions. Therefore, this paper proposes a multiple complementary priors CT image reconstruction method by simultaneously considering both the internal prior and external image information of CT images, thereby enhancing the reconstruction quality of CT images.Approach. Specifically, we propose a CT image reconstruction method based on weighted nonconvex low-rank regularized group sparse and deep image priors under hybrid plug-and-play framework by utilizing the weighted nonconvex low rankness and group sparsity of dictionary domain coefficients of each group of similar patches, and a convolutional neural network denoiser. To make the proposed reconstruction problem easier to tackle, we utilize the alternate direction method of multipliers for optimization.Main results. To verify the performance of the proposed method, we conduct detailed simulation experiments on the images of the abdominal, pelvic, and thoracic at projection views of 45, 65, and 85, and at noise levels of1×105and1×106, respectively. A large number of qualitative and quantitative experimental results indicate that the proposed method has achieved better results in texture preservation and noise suppression compared to several existing iterative reconstruction methods.Significance. The proposed method fully considers the internal nonlocal low rankness and sparsity, as well as the external local information of CT images, providing a more effective solution for CT image reconstruction. Consequently, this method enables doctors to diagnose and treat diseases more accurately by reconstructing high-quality CT images.

Robot path planning based on IAPF-A-Star algorithm

The traditional A-Star algorithm has the problem of performing much invalid traversal work of nodes near concave obstacles when dealing with path planning tasks in the presence of concave obstacles. To solve this problem, a fusion algorithm (called IAPF-A-Star here) based on improved artificial potential field (IAPF) and A-Star is proposed. In IAPF-A-Star, to better measure the distances between the nodes and the target node, the potential energy of the artificial potential field is used to represent the node’s proximity to the target node, and an iterative correction method is used to improve the potential field in order to eliminate the local low-potential energy that may exist in the artificial potential field. The improved potential field effectively avoids the occurrence of local low-potential fields and makes it possible to measure the position of nodes in relation to their target node by means of the potential field. Finally, the obtained path is converted to a curve path by using a cubic spline curve. In addition, to solve the problem that the curve path generated by the cubic spline curve may collide with obstacles, a path optimization strategy based on an adaptive cubic spline curve is proposed to make the final path smooth and collision avoidance, shorter and closer to the actual situation. Simulation experiments show that the proposed IAPF-A-Star algorithm in this paper can effectively avoid the problem of additional local energy consumption caused by concave obstacles when processing path planning tasks, and the traversal of invalid nodes facing different complex concave obstacles is effectively avoided.

Radar Moving Target Detection Based on Small-Sample Transfer Learning and Attention Mechanism

Moving target detection is one of the most important tasks of radar systems. The clutter echo received by radar is usually strong and heterogeneous when the radar works in a complex terrain environment, resulting in performance degradation in moving target detection. Utilizing prior knowledge of the clutter distribution in the space–time domain, this paper proposes a novel moving target detection network based on small-sample transfer learning and attention mechanism. The proposed network first utilizes offline data to train the feature extraction network and reduce the online training time. Meanwhile, the attention mechanism used for feature extraction is applied in the beam-Doppler domain to improve classification accuracy of targets. Then, a small amount of real-time data are applied to a small-sample transfer network to fine-tune the feature extraction network. Finally, the target detection can be realized by the fine-tuned network. Simulation experiments show that the proposed network can eliminate the influence of heterogeneous clutter on moving target detection, and the attention mechanism can improve clutter suppression under a low signal-to-noise ratio regime. The proposed network has a lower computational load compared to conventional neural networks, enabling its use in real-time applications on space-borne/airborne radars.

Removal of pyrene from domestic water supply using styrene-based imprinted polymer

Pyrene removal from domestic water supply (DWS) is important due to its mutagenicity, toxicity, and carcinogenicity. Use of molecularly imprinted polymer for pyrene removal is scarce, therefore the study developed polystyrene to complement conventional water treatment methods for pyrene removal. Bulk polymerisation was used to synthesise Pyrene imprinted polystyrene (PIP) and its non-imprinted analogue (NIP). Monoliths were characterised using SEM–EDX, FT-IR, and XRD. Extraction and concentration of pyrene were determined using GC–MS. Adsorption experiments on simulated solutions of pyrene with different concentrations, pH, adsorbent dosage, time, and temperature using PIP and NIP with equilibrium studies were done. Recovery was performed, while pyrene was adsorbed using simulated experiment best conditions. Used PIP and NIP were characterised. Pre-adsorption characterisation confirmed PIP and NIP while post-adsorption showed a clogged surface for NIP, but PIP was pyrene-selective. Pyrene concentration (0.05 mg/L) in DWS was > WHO standard (0.0002 mg/L) with 100% recovery. Adsorption efficiencies ranged from 25–100% and 15–100% with varied parameters for PIP and NIP respectively. However, best adsorption conditions are pH-9, time-1 h, adsorbent dosage-0.8 g, concentration-0.001 mgL−1, and temperature-27 °C with ΔG° (KJmol−1)13.48, 13.70, 13.94 for NIP and 21.24, 21.60, 21.95 for PIP. NIP ΔH° -0.3233 KJmol−1 and ΔS, 0.046 KJmol−1 k−1, while PIP ΔH° -0.0717KJmol−1 and ΔS, 0.071 KJmol−1 k−1. Adsorption fits Langmuir isotherm compared to others with R2 of 0.9140 and 0.9540 for PIP and NIP respectively. The study showed that both PIP and NIP removed pyrene from DWS with excellent efficiencies. However, PIP showed greater selectivity for pyrene removal.Graphical

Eucalyptus spp.-inspired degradable lubricant-releasing coating for marine antifouling surfaces

Currently, slippery liquid-infused porous surfaces (SLIPS) have garnered considerable attention in marine antifouling field because of its dynamic slippery surface. However, the reduction of antifouling performance due to uncontrolled loss of lubricant limits its application. Herein, inspired by the stripping of eucalyptus bark and the secretion of essential oil by leaf gland cells, a degradable lubricant-releasing coating (DLRC) without antifouling agent is designed. The DLRC presents excellent anti-algae, antibacterial adhesion properties, and its antifouling ability has been further verified by simulation experiments. The excellent antifouling performance is mainly due to the dual role of degradation surface-renewal and the continuous seepage of lubricating oil to the surface. Compared with previously reported responsive lubricant release methods, this self-degrading controlled release method is more suitable for marine environments. What's more, the ester and lubricant content can be adjusted to meet different antifouling needs. Finally, we hope that this strategy will shed some light on the design of functional coatings.

A robust optimization approach for steeling-continuous casting charge batch planning with uncertain slab weight

The volatility of slab weight in steelmaking-continuous casting (SCC) production, attributed to factors such as flexible order demand, is addressed in this paper. A robust optimization mathematical model for charge batch planning (CBP) with uncertain slab weight is established, and a collaborative optimization method using the surrogate Lagrangian relaxation (SLR) framework and improved objective feasibility pump (IOFP) is developed to solve the problem. In the SLR method, new step-size updating conditions are developed, eliminating the need for pre-estimating the optimal dual value. Additionally, only a subset of subproblems that satisfy the optimality conditions of the surrogate needs to be solved to overcome the low optimization efficiency resulting from oscillations in the feasible domain during internal searches in traditional Lagrangian relaxation (LR) methods. The IOFP method is employed to match the structure of the subproblem model of 0–1 mixed integer programming (MIP). During the search for integer solutions, a weighted objective function is added to the auxiliary model to improve the quality of solutions. Furthermore, it combines a variable neighborhood branching method to prevent the algorithm from entering into cycles. Finally, the effectiveness of the proposed model and the performance of the algorithm are validated through simulation experiments.

Research on intelligent three-dimensional anchor position detection method for ships utilizing Traversal and Monte Carlo algorithms

As intelligent ship technology advances, the importance of intelligent anchor position detection, as one of the key technologies, can ensure the safe anchoring of ships and enhance the efficiency of port operation. At present, most of the anchor position selection and detection algorithms are mainly based on two-dimensional planes, and there is a lack of research on the intelligent detection of safe water depth for ship anchoring in three-dimensional space. It not only restricts the full utilization of anchorage resources but also affects the safety and environmental adaptability of anchoring operations. To address these issues, this study proposes a three-dimension anchor position detection method. Firstly, based on the establishment of a three-dimensional ocean model, the possible anchor positions selected by the ship are simulated using the Monte Carlo algorithm. Secondly, the simulated anchor positions are optimized using a Traversal algorithm to filter out the optimal anchoring position that meets the requirements, the safety distance between each point and the existing ship is calculated, and the anchor position is determined according to the corresponding required safety spacing. Finally, to verify the applicability and effectiveness of the method under different sea conditions and different ship types, this study conducts a series of simulation experiments with 5000 random samples. These experiments compare the demand of anchor position selection for anchoring ships with changing water depths in the case of empty and full load drafts, and visualize the impact of varying water depth parameters on the selection of anchor positions for anchoring ships in various ship types. The outcomes of the experiment indicate that the algorithm’s detection area encompasses the whole anchorage area, ensuring both the anchorage area’s usage rate and the accuracy of anchor position detection. This study demonstrates that the Traversal and Monte Carlo Algorithms effectively improve the accuracy of the selection of anchoring position of the ship, makes full use of the resources of anchorage, and further improves the safety and efficiency of the anchoring operation.

Insights into the carbonate/bicarbonate ion-induced failure mechanism of bentonite in drilling fluids

During deep oil and gas drilling, drilling fluids are often polluted with carbon dioxide (CO2). The intrusion of CO2 can affect bentonite performance, leading to deterioration or even failure of drilling fluids. To further investigate the influence of CO2 pollution on the bentonite and its failure mechanism, Na2CO3 simulation experiments were conducted. Performance evaluation showed that as the concentration of HCO3− and CO32− increased, the viscosity, filtration loss, and mud cake permeability of drilling fluids significantly increased. Meanwhile, the drilling fluid foaming was particularly evident. Mechanism studies indicated that the adsorption of HCO3− and CO32− on the bentonite diminished the electrostatic interactions and interlayer spacing between particles, leading to the particles aggregation and a decrease in Zeta potential, ultimately causing the hydration film on the bentonite to become thinner. Simultaneously, this also led to a decline in the dispersibility of bentonite. This study can offer a new research thread for the failure mechanism of bentonite under CO2 pollution and indicate the direction for the study of drilling fluid resistance to acidic gas pollution.

Change and Detection of Emotions Expressed on People’s Faces in Photos

Human emotions are an element of attention in various areas of interest such as psychology, marketing, medicine, and public safety. Correctly detecting human emotions is a complex matter. The more complex and visually similar emotions are, the more difficult they become to distinguish. Making visual modifications to the faces of people in photos in a way that changes the perceived emotion while preserving the characteristic features of the original face is one of the areas of research in deepfake technologies. The aim of this article is to showcase the outcomes of computer simulation experiments that utilize artificial intelligence algorithms to change the emotions on people’s faces. In order to detect and change emotions, deep neural networks discussed further in this article were used.

Heterogeneous multi-robot system mission planning with cooperative replenishment through data-driven rendezvous point selection

AbstractWith advances in autonomy technology, the use of multi-robot systems is becoming increasingly viable and efficient. In particular, heterogeneous systems are effective in complicated missions that require various capabilities. When the missions are prolonged, certain robots such as quadcopter-typed unmanned aerial vehicles (UAVs) run out of energy and require replenishment. As some robots such as unmanned surface vessels (USVs) have large payload capacities for carrying supplies, the UAVs can receive the necessary resources from the USVs amid the missions. In this paper, we propose a mission planning framework that aims to minimize the total mission duration with consideration of the energy replenishment of robots and the heterogeneity of robots and tasks. The proposed framework consists of four components: task allocation, rendezvous point selection, task planning, and plan combination. For replenishment, the location of the rendezvous must be chosen, and we propose using a data-driven approach to predict the best rendezvous point. Then, the prediction result is used for computing candidate plans of each robot in a distributed fashion, and the best candidates are combined to compute the final plan. To validate the efficacy of the proposed method, simulated experiments of various problem configurations are performed and analyzed.

The HADRIAN novel human–machine interface prototype for automated driving: safety and impact assessment

The current paper was performed within the HADRIAN project and focuses on exploring the effects of innovative Human–Machine Interface (HMI) prototypes on safety, driving performance, and driver perceptions. Employing driving simulator experiments and questionnaires, this study investigates whether HADRIAN innovative HMI enhances safety and receives positive evaluations from drivers. Specifically, the research centers on a driving simulator experiment that evaluates novel HMI prototypes designed to improve automated driving at SAE Levels 2 or 3. To facilitate HMI assessment, a tailored safety and impact assessment methodology was developed using unique Key Performance Indicators (KPIs). To benchmark and generate a total score for the HADRIAN HMI, data envelopment analysis was deployed based on the aforementioned KPIs. The findings shed light on the influence of HADRIAN HMI innovations on safety and perceived impact when compared to a baseline “state-of-the-art” HMI. Subsequently, a comprehensive discussion unfolds, highlighting the key KPIs that contributed significantly to the safety and perceived impact scores. This method and its outcomes can serve as a valuable resource for other HMI stakeholders, enabling them to employ similar human-centered assessment methodologies to assess the safety and perceived impact of potential HMI configurations.

A trajectory tracking control method for the discharge arm of the self-propelled forage harvester

The cooperative operation between the self-propelled forage harvester and the trailer aims to achieve precise and automatic forage unloading. The discharge arm serves as the core structure for conveying forage, and the precision and speed of its motion control are crucial factors influencing the efficiency of forage harvesting. In this context, we proposed a trajectory tracking control method for the discharge arm based on an improved particle swarm optimization (IPSO)-PID controller. Firstly, we utilized the Denavit-Hartenberg (D-H) model of the discharge arm for a positive kinematics solution and the geometric resolution and linear fitting method for the inverse kinematics solution. Secondly, we employed the polynomial interpolation method for trajectory planning on the joint space of the discharge arm, and the PSO algorithm for time-optimal trajectory planning. Then, we designed an IPSO-PID trajectory tracking control algorithm and built an Amesim-Simulink co-simulation model for multiple simulation experiments. Finally, we conducted several performance tests of the discharge arm automatic control system in the workstation and the field, respectively. The experiment results indicate that the performance of the IPSO-PID controller exceeds that of all other controllers, which can meet the discharge arm’s motion control accuracy and speed requirements. Our research results are of great significance for improving the productivity and automation process of the self-propelled forage harvester and provide valuable references for research on automatic and precise control of material loading in other agricultural cooperative harvesting.

Robust joint estimation of sideslip angle and tire lateral force for distributed drive electric vehicle

Accurate estimation of vehicle sideslip angle (SA) and tire lateral force (TLF) is essential for the effective functioning of vehicle active safety control systems. However, effective estimation of SA and TLF using low-cost on-board sensors is a major challenge. In this paper, a robust joint estimation method of SA and TLF for distributed drive electric vehicle (DDEV) is proposed. Adaptive sliding-mode observer (ASMO) is used to estimate the TLF and is used as the input to the subsequent filter. Maximum correntropy criterion (MCC) is introduced to improve the robustness of the unscented Kalman filter (UKF) under mixed Gaussian noise, and the maximum correntropy unscented Kalman filter (MCUKF) algorithm is composed to estimate SA. The effectiveness of the proposed ASMO and MCUKF joint estimation method is verified by Simulink/Carsim joint simulation experiments. The experimental results show that compared with the existing methods, the estimation method proposed in this paper effectively improves the estimation accuracy and robustness of SA and TLF under the mixed Gaussian environment, which is of great significance for the improvement of vehicle active safety.

A Combined Capacity Planning and Simulation Approach for the Optimization of AGV Systems in Complex Production Logistics Environments

Background: The capacity planning of production systems is one of the most fundamental strategic problems in the creation of a production plant. However, the implementation of increasingly complex production systems combined with sophisticated automated material handling justifies the development of novel approaches to solve the combined capacity planning and material handling problem, which is also the objective of the current study. Methods: The presented approach combines the use of capacity planning formulas and discrete event simulation for optimizing extensive automated guided vehicle (AGV) systems from the aspect of the number of required vehicles. Extensive series of simulation experiments are applied in the case of each model variant for optimal results and to account for machine failures in the system. Results: The application of the proposed method is demonstrated through a realistic sample problem in a plastic industry setting with the use of the Siemens Tecnomatix Plant Simulation software (version 2302.0003, Educational license). Conclusions: The results from the sample problem demonstrate the usefulness of the approach, as a non-intuitive solution proved to be the most efficient. Additionally, the main advantage of the method is that it provides a standardized framework for the simulation-based optimization of AGV systems starting out from the comprehensive production capacity parameters.

Computational Studies and Functional Validation of Eugenol and Ferulic Acid as Inhibitors of Against Enterococcus faecalis Sortase A.

Enterococcus faecalis (E. faecalis) is commonly occurring pathogen associated with nosocomial infections. Infections are difficult to treat because of their multidrug-resistant (MDR) nature and their tendency to form biofilms. Therefore, it is essential to find alternative medicinal approaches to treatment. In this regard, targeting an important protein for drug development can be an alternative approach. Sortase A (SrtA) is an important enzyme involved in anchoring cell surface-exposed proteins to the cell envelope. SrtA is present in Gram-positive bacteria which catalyses the attachment of several virulence factors and other proteins to the cell membrane. It is involved in bacterial pathogenesis, therefore, it's a promising drug target for the development of anti-microbial drugs targeting cell adhesion, evasion, and biofilm development. To identify SrtA potential inhibitors, we have expressed and purified E. faecalis Sortase A (EfSrtAΔN59). Structural studies using homology modelling along with molecular docking of protein with selected ligand molecules were also done. The results were confirmed by MD simulation experiments. We have also performed functional validation of these compounds on bacterial growth, anti-biofilm assays and inhibition assays of selected ligands were also done against E. faecalis individually and in synergistic combinations. Results indicated that both Eugenol and Ferulic acid bind to EfSrtAΔN59 with significant interactions and show promising results.

A Microscopic Experimental Study on the Dominant Flow Channels of Water Flooding in Ultra-High Water Cut Reservoirs

The water drive reservoir in Shengli Oilfield has entered a stage of ultra-high water cut development, forming an advantageous flow channel for the water drive, resulting in the inefficient and ineffective circulation of injected water. Therefore, the distribution characteristics of water drive flow channels and their controlled residual oil in ultra-high water cut reservoirs are of great significance for treating water drive dominant flow channels and utilizing discontinuous residual oil. Through microscopic physical simulation of water flooding, color mixing recognition and image analysis technology were used to visualize the evolution characteristics of water flooding seepage channels and their changes during the control process. Research has shown that during the ultra-high water content period, the shrinkage of the water drive seepage channel forms a dominant seepage channel, forming a “seepage barrier” at the boundary of the dominant seepage channel, and dividing the affected area into the water drive dominant seepage zone and the seepage stagnation zone. The advantage of water flooding is that the oil displacement efficiency in the permeable zone is as high as 80.5%, and the remaining oil is highly dispersed. The water phase is almost a single-phase flow, revealing the reason for high water consumption in this stage. The remaining oil outside the affected area and within the stagnant flow zone accounts for 89.8% of the remaining oil, which has the potential to further improve oil recovery in the later stage of ultra-high water cut. For the first time, the redundancy index was proposed to quantitatively evaluate the control effect of liquid extraction and liquid flow direction on the dominant flow channels in water flooding. Experimental data showed that both liquid extraction and liquid flow direction can regulate the dominant flow channels in water flooding and improve oil recovery under certain conditions. Microscopic physical simulation experiments were conducted through the transformation of well network form in the later stage of ultra-high water content, which showed that the synergistic effect of liquid extraction and liquid flow direction can significantly improve the oil recovery effect, with an oil recovery rate of 68.02%, deepening the understanding of improving oil recovery rate in the later stage of ultra-high water content.

A minimum cost and maximum fairness-driven multi-objective optimization consensus model for large-scale group decision-making

The rise of social media and e-democracy has driven a paradigm shift in decision-making, notably reflected in the changing ways of public participation and policymaking within decision-making processes. The increased focus on fairness and efficiency not only complicates the consensus-building process among diverse interests and perspectives, but also significantly adds to the complexity of decision-making and its implementation. In this context, balancing the interests of all parties while ensuring fairness and improving decision-making effectiveness becomes crucial. To address these challenges, this study develops a multi-objective optimization consensus framework for large-scale group decision-making (LSGDM) that integrates the interests of decision-makers (DMs) and a moderator, providing a more comprehensive tool. Specifically, this study first designs a DM weight determination method based on structural hole theory within fuzzy social networks. The proposed DM weight determination method effectively leverages the flexibility of fuzzy social networks and the comprehensiveness of structural hole theory to enhance the accuracy and reliability of weight assignment. Building on this, a novel clustering method based on the maximum group consensus level is developed, taking into account the varying importance of different DMs. Furthermore, a minimum cost and maximum fairness-driven multi-objective optimization LSGDM consensus model, referred to as MCMF-MO-LSGDM, is explored in this study. Finally, the utility and superiority of the constructed model are confirmed through comparative analysis and simulation experiments against existing related works.