Non-dominated Sorting Genetic Algorithm II Research Articles

Preload Multi-Objective Optimization Method for Ultrasonic Motors Based on NSGA-II

The preload has a significant impact on the output performance of ultrasonic motors. To achieve optimal output from the ultrasonic motor, this paper proposes a novel multi-objective optimization method for preload, utilizing the Non-dominated Sorting Genetic Algorithm II (NSGA-II) to solve the problem of determining the optimal preload. Firstly, a platform for preload adjustment and testing is established to conduct experimental tests on the motor’s characteristics. Based on the experimental data, the influence of preload on the motor’s output performance is analyzed. Secondly, linear regression models are established for three performance indicators: no-load speed, stall torque, and output efficiency with respect to preload, and the objective functions are fitted accordingly. Finally, the NSGA-II algorithm is used for multi-objective optimization with three objectives to obtain the Pareto front and determine the optimal preload for the ultrasonic motor. Simulation and experimental results show that after preload optimization, the no-load speed increased by 4.93%, stall torque increased by 26.10%, and output efficiency increased by 11.28%. Compared to existing optimization methods, this approach has lower computational complexity and better optimization performance, ensuring higher optimization accuracy and precision. The preload optimization method based on NSGA-II can improve the performance of ultrasonic motors in practical applications.

Integrated LI-NSGA-II Approach for Solving the Non-linear Multi-objective Optimization Problem

Real-world engineering projects frequently involve complex, non-linear multi-objective optimization challenges. Traditional methods like PERT/CPM and basic heuristics often fail to provide optimal solutions in such scenarios. Multi-objective evolutionary algorithms, such as genetic algorithms and non-dominated sorting genetic algorithms, are more effective for identifying true Pareto solutions. Among these, the Non-dominated Sorting Genetic Algorithm-II (NSGA-II) is a well-known algorithm for solving multi-objective optimization problems. This study presents an integrated approach, called LI-NSGA-II, which combines Lagrange's Interpolation (LI) with NSGA-II to solve non-linear multi-objective optimization problems in real-world projects. In this LI-NSGA-II approach, LI is used to handle the non-linearity of competing objectives, whereas NSGA-II optimizes these objectives to find optimal solutions. The outcomes achieved through the LI-NSGA-II approach are ideal for real-time monitoring and control of non-linear multi-objective optimization problems.

Integrated optimization for sizing, placement, and energy management of hybrid energy storage systems in renewable power systems

Power systems reliant on renewable energy sources (RES) encounter supply-demand imbalances and stability challenges due to their inherent uncertainties. Hybrid energy storage systems (HESS) have emerged as a flexible and cost-effective solution to address these issues. This paper proposes an integrated optimization method for the capacity, location, and energy management of a HESS in RES-based power systems. The method optimizes battery energy storage system (BESS), electrolyzer (EL), fuel cell (FC), and hydrogen storage tank (HST) to minimize total costs, including power purchase, curtailment compensation, operation and maintenance (O&M), and investment costs, alongside voltage profile deviation index (VPDI) and active power loss index (APLI). A novel hybrid framework is proposed, utilizing mixed-integer linear programming (MILP) optimization at the master level and non-dominated sorting genetic algorithm II (NSGA-II) optimization at the slave level. This framework ensures that MILP provides robust solutions, while NSGA-II derives balanced optimal solutions from the optimal Pareto front. The proposed method was validated using real seasonal data from Korea and tested in IEEE 33 and IEEE 69 bus systems against various benchmark cases. Results indicate significant improvements, with VPDI enhanced by 16.00 % and 10.11 %, APLI by 15.64 % and 12.15 %, and total costs reduced by 58.67 % and 50.56 %, respectively. The sensitivity analysis demonstrated the robustness of the proposed method, showing zero sensitivity to iterations and positive correlations with increased levels of RES. Further verification against other optimization algorithms showed that the proposed method excels in cost efficiency, voltage stability, and line loss reduction, providing highly reliable results.

BIM-based optimization approach to reduce life cycle costs by focusing on the integration of construction and operation phases in office-commercial buildings

This study investigates the application of Building Information Modeling (BIM) to reduce life cycle costs (LCC) by addressing critical nonconformances encountered during the construction and operational phases of buildings. The primary aim is to identify nonconformances that escalate operational costs and to evaluate how BIM can effectively reduce these costs. The research begins with a comprehensive review of existing literature and utilizes the Delphi method to identify four key nonconformances that most significantly impact operational expenses. To further analyze these issues, semi-structured interviews and the Non-dominated Sorting Genetic Algorithm II (NSGA-II) are employed to determine the cost ranges of components associated with these nonconformances in both the construction and operational phases. The study finds that BIM-based optimization can lead to substantial cost reductions, although there are limitations due to the lack of precise data on the operational phase of low-cost equipment. The key contribution of this research is the development of a framework that helps designers create more effective BIM models, enhancing facilities management (FM) and reducing LCC. The originality of this study lies in its integrated approach, combining BIM and optimization techniques to directly address operational cost issues, which provides a novel perspective on improving life cycle cost management in buildings. Future research should expand the dataset and explore a broader range of nonconformances to refine and extend the framework's applicability.

Multi-Objective Feature Selection Algorithm using Beluga Whale Optimization

The advancement of science and technology has resulted in large datasets with noisy or redundant features that hamper classification. In feature selection, relevant attributes are selected to reduce dimensionality, thereby improving classification accuracy. Multi-objective optimization is crucial in feature selection because it allows simultaneous evaluation of multiple, often conflicting objectives, such as maximizing model accuracy and minimizing the number of features. Traditional single-objective methods might focus solely on accuracy, often leading to models that are complex and computationally expensive. Multi-objective optimization, on the other hand, considers trade-offs between different criteria, identifying a set of optimal solutions (a Pareto front) where no one solution is clearly superior. It is especially useful when analyzing high-dimensional datasets, as it reduces overfitting and enhances model performance by selecting the most informative subset of features. This article introduces and evaluates the performance of the Binary version of Beluga Whale Optimization and the Multi-Objective Beluga Whale Optimization (MOBWO) algorithm in the context of feature selection. Features are encoded as binary matrices to denote their presence or absence, making it easier to stratify datasets. MOBWO emulates the exploration and exploitation patterns of Beluga Whale Optimization (BWO) through continuous search space. Optimal classification accuracy and minimum feature subset size are two conflicting objectives. The MOBWO was compared using 12 datasets from the University of California Irvine (UCI) repository with eleven well-known optimization algorithms, such as Genetic Algorithm (GA), Sine Cosine Algorithm (SCA), Bat Optimization Algorithm (BOA), Differential Evolution (DE), Whale Optimization Algorithm (WOA), Non-dominated Sorting Genetic Algorithm II (NSGA-II), Multi-Objective Particle Swarm Optimization (MOPSO), Multi-Objective Grey Wolf Optimizer (MOGWO), Multi-Objective Grasshopper Optimization Algorithm (MOGOA), Multi-Objective Non-dominated advanced Butterfly Optimization Algorithm (MONSBOA), and Multi-Objective Slime Mould Algorithm (MOSMA). In experiments using Random Forest (RF) as the classifier, different performance metrics were evaluated. The computational results show that the proposed BBWO algorithm achieves an average accuracy rate of 99.06% across 12 datasets. Additionally, the proposed MOBWO algorithm outperforms existing multi-objective feature selection methods on all 12 datasets based on three metrics: Success Counting (SCC), Inverted Generational Distance (IGD), and Hypervolume indicators (HV). For instance, MOBWO achieves an average HV that is at least 3.54% higher than all other methods.

The Path Tracking Control of Unmanned Surface Vehicles Based on an Improved Non-Dominated Sorting Genetic Algorithm II-Based Multi-Objective Nonlinear Model Predictive Control Method

This paper proposes a multi-objective nonlinear model predictive control (MOMPC) method based on an improved non-dominated sorting genetic algorithm II (NSGAII) for the path tracking problem of unmanned surface vehicles (USVs). To enhance performance in cross-track error, a varying look-ahead distance is utilized in the line of sight (LOS) algorithm, which allows the MPC control algorithm to compute the look-ahead distance and desired speed rather than directly calculating the control input. Since the cost function of the MPC algorithm includes multiple objective terms, a multi-objective model predictive control algorithm is employed to improve overall control performance. Additionally, an adaptive rotation-based simulated binary crossover (ARSBX) is integrated into the NSGAII algorithm, and the non-dominated sorting method is optimized to reduce computation time. These enhancements increase diversity and exploration in the solution space, enabling the algorithm to find the optimal solution more efficiently. Simulations conducted in two different scenarios demonstrate that the nonlinear MPC method based on the improved NSGAII successfully tracks the desired path; it achieved an improvement of approximately 41% in time performance and about 5% in path-tracking error performance, exhibiting strong control performance and robustness.

Multi-Scenario Simulation of Optimal Landscape Pattern Configuration in Saline Soil Areas of Western Jilin Province, China

The Songnen Plain is a significant region in China, known for its high grain production and concentrated distribution of soda saline land. It is also considered a priority area for cropland development in the country. However, the Songnen Plain is now facing prominent issues such as soil salinization, soil erosion, and deteriorating cropland quality, which are exacerbated by climate change and intensified human activities. In order to address these challenges, it is crucial to adjust the quantitative structure and layout of different landscapes in a harmonious manner, aiming to achieve synergistic optimization, which is posed as the key scientific approach to guide comprehensive renovation policies, improve saline–alkaline land conditions, and promote sustainable agricultural development. In this study, four scenarios including natural development, priority food production (PFP), ecological security priority (ESP), and economic–ecological-balanced saline soil improvement were set up based on Nondominated Sorting Genetic Algorithm II (NSGA-II) and the Future Land Use Simulation (FLUS) model. The results demonstrated that the SSI scenario, which focused on economic–ecological equilibrium, displayed the most rational quantitative structure and spatial layout of landscape types, with total benefits surpassing those of the other scenarios. Notably, this scenario involved converting unused land into saline cropland and transforming saline cropland into normal cropland, thereby increasing the amount of high-quality cropland and potential cropland while enhancing the habitat quality of the region. Consequently, the conflict between food production and ecological environmental protection was effectively mitigated. Furthermore, the SSI scenario facilitated the establishment of a robust ecological security and protection barrier, offering valuable insights for land use planning and ecological security pattern construction in the Songnen Plain, particularly in salt-affected areas.

A sustainable vaccine supply-production-distribution network with heterologous and homologous vaccination strategies: Bi-objective optimization

Heterologous and homologous Coronavirus Disease 2019 (COVID-19) vaccination against Severe Acute Respiratory Syndrome (SARS)-CoV-2 are robust and proactively adaptable strategies. However, there is still a lack of appropriate mathematical models for integrating vaccination strategies into the vaccine supply chain network. This study develops a supply-production-distribution-inventory-allocation problem in the Sustainable Vaccine Supply-Production-Distribution Network (SVSPDN) to fill this gap for the first time. The outstanding novelties of this research are prioritizing vaccines and sequencing injection doses to increase vaccination effectiveness. In addition, the remarkable new contribution of the proposed mathematical model is the design of new bi-objective, multi-dose, multi-level, and multi-period to ensure the sustainability performance of the entire network. This aim is achievable by minimizing the cost of supplying, producing, and distributing vaccines and fulfilling social goals by maximizing vaccination effectiveness. Also, a scenario-based robust stochastic optimization approach is presented to handle uncertainties. Since the SVSPDN design is an NP-hard problem, to solve the proposed mathematical model, three Pareto-based evolutionary algorithms, including Non-Dominated Sorting Genetic Algorithm II (NSGA-II), Multi-Objective Particle Swarm Optimization (MOPSO), and Multi-Objective Gray Wolf Optimizer (MOGWO), are applied. The Taguchi design method is applied to tuning the parameters due to the sensitivity of meta-heuristic algorithms to input parameters. Then, a comparison is performed using four assessment metrics, including the Number of Pareto Solutions (NPS), Diversification Matrix (DM), Mean Ideal Distance (MID), Spread of Non-Dominance Solutions (SNS), and Computation Time (CT). The results reveal that the NSGA-II and MOGWO algorithms have performances that are very close to each other. However, MOGWO performs better in tackling the problem and is superior to the NSGA-II and MOPSO regarding assessment metrics and computation time. A case study of Iran is investigated to indicate the efficiency and applicability of the proposed model. Finally, sensitivity analyses, managerial insights, and practical implications are discussed.

Multi-objective optimization design and multi-physics simulation analysis of a novel magnetorheological fluid-elastomer damper

Aiming at the problem of the suspension isolator not being able to adjust the damping, magnetorheological damping technology was introduced based on the traditional rubber passive suspension. A magnetorheological (MR) fluid damper was designed inside the rubber suspension and connected with the rubber structure to form a magnetorheological fluid-elastomer (MRFE) damper. The design principle of the MR damper was described. The principle prototype of the initial MRFE damper was designed, and the mechanical properties of the damper were tested through experiments. Taking the damping force, dynamic adjustable range and power as the optimization objectives, the structure of the MR damper was optimized using the non-dominated sorting genetic algorithm II (NSGA-II). The Pareto optimal solution set of MR damper parameters was obtained, and the structural parameters of the MR damper were determined based on the principle of minimum response time. A multi-physics field simulation model of the optimal damper was established to analyze the dynamic performance of the MR damper. The principle prototype of the optimal MRFE damper was obtained, and the optimal MR damper increased the damping force by 63.1% and the dynamic adjustable range by 104.9% compared with the initial MR damper, while the optimal MRFE damper increased the damping force by 9.27% and the dynamic adjustable range by 8.85% compared with the initial MRFE damper. The design results meet the requirements, and the proposed optimization method can provide a theoretical basis for the optimal design of related vibration-damping equipment.

Experimental analysis and low-damage machining strategy for composite ultrasonic vibration-assisted grinding of silicon carbide based on DA-MLP-NSGA-II algorithm

At present, because of the lack of ultrasonic composite vibration assisted grinding mechanism, neural network optimization algorithm (NNOA) is used to optimize the processing results. In NNOA, multi-layer perceptron (MLP) neural network model and non-dominated sorting genetic algorithm-II (NSGA-II) are very efficient and accurate methods. In this paper, based on the measurement and analysis of the specific ultrasonic vibration device, the CUVAG experiments on silicon carbide (SiC) ceramic were carried out to investigate the influence of processing parameters on the grinding forces, the ground surface roughness and morphology, and the subsurface damage. Then, the brittle-ductile removal behavior of hard-and-brittle materials could be revealed according to the above analysis. After that, MLP model and NSGA-II were utilized to predict and optimize the processing results in CUVAG. The results show that the grinding forces are basically constant, the surface quality deteriorates, and the subsurface damage increases with increased axial vibration amplitude and workpiece infeed speed, but all fluctuate with enlarged wheel speed, and turns at the inflection point of brittle-ductile transition with increased elliptic vibration amplitude. The fitting goodness R2 of the established MLP neural network prediction model is between 0.94 and 0.975, and the process parameters calculated by the NSGA-II optimization algorithm are verified. With optimized processing parameters, the grinding forces are reduced by about 13 %, the surface roughness is reduced to Ra0.037 μm (by 29 %), and the depth of subsurface damage is reduced by 68 %.

Data-driven optimization of nose profiles for water entry impact load reduction

The prediction and reduction of impact loads during water entry are critical in various engineering applications, such as ship slamming and seaplane landings. Traditional methods, including theoretical models, computational fluid dynamics (CFD), and experimental approaches, often suffer from limited applicability, lengthy processing times, and high costs. To address these challenges, this study explores the use of Deep Neural Networks (DNNs) for predicting peak impact loads during the oblique water entry of cylinders with different nose profiles. Optimized Latin Hypercube Sampling (OLHS) and Sobol sequences are employed to generate dataset inputs from a 7-dimensional parameter space that governs impact dynamics. These inputs are then fed into OpenFOAM solvers to compute the peak impact accelerations, serving as dataset outputs for training and testing the DNN. Experiments are conducted to validate the numerical method, confirming that the resulting dataset outputs can be considered as ground truth. With Bayesian hyperparameter optimization, the well-trained DNN model demonstrates reliable accuracy in predicting peak axial and normal accelerations. Further, this study integrates the DNN model with the Non-dominated Sorting Genetic Algorithm II (NSGA-II) to optimize the nose profiles, resulting in a maximum weighted reduction of 48% in the combined axial and normal accelerations.

Satellite-informed smart sensor placement framework for near-optimal PM2.5 monitoring in urban areas.

Air pollution is a global public health concern, particularly due to PM2.5, which can cause respiratory and cardiovascular diseases. Accurate placement of monitoring sensors is essential to effectively monitor and mitigate PM2.5 effects. However, the complex nature of air pollution, including factors like traffic density, population density, and weather conditions, poses challenges for sensor placement. Additionally, cost and resource constraints further complicate the process. In this study, we propose a novel algorithm that utilizes a multi-criteria optimization approach to identify optimal locations and distribution of PM2.5 monitoring sensors. The algorithm integrates various geographical covariates, such as roads, population density, terrain elevation, and satellite observations of surface PM2.5. By applying the Non-dominated Sorting Genetic Algorithm II (NSGA-II), we optimize sensor placement. Our algorithm is validated through a case study in a metropolitan area, demonstrating its ability to identify optimal sensor locations while reducing their number and maintaining high accuracy. Furthermore, we highlight the value of satellite observations for initial PM2.5 estimates and aiding sensor placement. Our comprehensive algorithm optimizes air quality monitoring, enabling the identification of pollution hotspots, assessment of health risks, and informing policy and mitigation strategies.

Research on the Optimization of Urban–Rural Passenger and Postal Integration Operation Scheduling Based on Uncertainty Theory

The integration of postal and passenger transport is an effective measure to enhance the utilization efficiency of passenger and freight transportation resources and to promote the sustainable development of urban–rural transit and logistics. This paper considers the uncertainty in passenger and freight demand as well as transit operation times, constructing an optimization model for integrated urban–rural transit and postal services based on uncertainty theory. Passenger and freight demand, along with the inverse uncertain distribution of events, serve as constraints, while minimizing passenger travel time and the cost for passenger transport companies are the optimization objectives. Taking into account the uncertainty of urban–rural bus travel time, the scheduling model is transformed into a robust form for scenarios involving single and multiple origin stations. The model is solved using an improved NSGA-II (Nondominated Sorting Genetic Algorithm II) to achieve effective coordinated scheduling of both passenger and freight services. Through a case study in Lotus County, Jiangxi Province, vehicle routing plans with varying levels of conservativeness were obtained. Comparing the results from different scenarios, it was found that when the total vehicle operating mileage increased from 1.96% to 62.26%, passenger transport costs rose from 2.95% to 62.66%, while the total passenger travel time decreased from 55.99% to 172.31%. In terms of optimizing costs and improving passenger travel efficiency, operations involving multiple starting stations for a single vehicle demonstrated greater advantages. Meanwhile, at a moderate level of robustness, it was easier to achieve a balance between operational costs and passenger travel time. The research findings provide theoretical support for improving travel conditions and resource utilization in rural areas, which not only helps enhance the operational efficiency of urban–rural transit but also contributes positively to promoting balanced urban–rural sustainable development and narrowing the urban–rural gap.

The multi-depot pickup and delivery vehicle routing problem with time windows and dynamic demands

The rapid development of the urban logistics recycling industry, combined with the complexity of the pickup and delivery networks, has created a surge in dynamic customer demands and exacerbated the difficulty of logistics resource sharing. Accordingly, this work focuses on a multi-depot pickup and delivery vehicle routing problem with time windows and dynamic demands, which incorporates resource sharing. A bi-objective mathematical model is formulated to minimize the total operating cost and number of vehicles. A three-dimensional affinity propagation clustering and an adaptive nondominated sorting genetic algorithm-II are combined to find Pareto optimal solutions. A dynamic demand insertion strategy is proposed to determine the vehicle service sequences for dynamic situations. Combined with an elite iteration mechanism to prevent the proposed algorithm from falling into search stagnation and improve the convergence performance. The superiority of the proposed algorithm is verified by comparing with CPLEX solver (i.e., ILOG CPLEX Optimization Studio 12.10), multi-objective ant colony optimization, multi-objective particle swarm optimization, multi-objective evolutionary algorithm, multi-objective genetic algorithm, and decomposition-based multi-objective evolutionary algorithm with tabu search. Besides, the proposed model and algorithm are tested by a real-world case study in Chongqing city, China, and the further analysis indicates that significant improvement can be achieved. Furthermore, by incorporating the recognition and prediction techniques of artificial intelligence on dynamic demand data, the proposed approach can realize the self-optimization of multi-depot vehicle routes and the precise allocation of logistics resources in dynamic environments. This study is conducive to the construction of a digitally-intelligent urban logistics system.

Multi-objective optimization design of steel cross section of integrated supports and hangers based on NSGA-Ⅱ and MDOS

The stability and safety of building Mechanical, Electrical, and Plumbing (MEP) systems are significantly influenced by the steel section design of integrated supports and hangers. Traditional design methods, reliant on empirical steel section selection, are often inefficient and can result in unnecessary material usage. This paper introduces a novel multi-objective optimization approach that enhances the section design process by minimizing material costs and maximizing bending performance of supports and hangers. Firstly, the Non-dominated Sorting Genetic Algorithm II (NSGA-II) is employed to identify an array of optimal cross-sectional designs that reconcile economic considerations with structural mechanical performance. Subsequently, in order to pinpoint the most suitable design from the resulting Pareto-optimal scheme, the Method of Determining the Optimal Scheme (MDOS) is proposed. MDOS harnesses expert scoring, the Continuous ordered weighted averaging (COWA) operator, game theory, and the Technique for Order Preference by Similarity to an Ideal Solution (TOPSIS) to objectively evaluate and prioritize design schemes according to stakeholder preferences and project-specific criteria. Finally, the optimization results are analyzed and the influence of different spacing of supports and hangers and different subjective weight factors on the selection of cross-section is studied. The findings reveal that the method can precisely determine all cross-section designs that adhere to the established constraints, achieving a 4.09-fold enhancement in design efficiency. It was observed that the two outlined objectives are predominantly affected by the height of the web. The distribution trend of the optimal solution value of the independent variable is almost the same under the condition of different values of the supports and hangers spacing. With the change of subjective weight, the cross section selection focuses on three optimal schemes. Therefore, the proposed method can be used as reference for similar steel design optimization.

Refined dimensional accuracy in FDM components via ensemble weight-optimized surrogates and hybrid NSGA-II-TOPSIS optimization

ABSTRACT In this paper, a novel approach was developed to predict the geometrical deviation and density of fused deposition modeling (FDM) parts and to optimize these outcomes by fine-tuning the relevant process variables. The prediction process utilized an ensemble approach, combining the optimized weighted sum of three individual surrogate models. These models correlate the input variables – nozzle temperature (NT), infill fraction (IF), and print speed (PS) – with the output responses of density, internal ovality (IO), and external ovality (EO). According to the results, the ensemble outperformed the individual surrogates, exhibiting enhanced predictive accuracy. Subsequently, non-dominated sorting genetic algorithm II (NSGA-II), a multi-objective optimization technique, was integrated with the technique for order of preference by similarity to ideal solution (TOPSIS), a multi-criteria decision-making method, to optimize the ensemble of surrogates (EoS) and generate a ranked set of Pareto-optimal solutions. The outcomes from the EoS, combined with the NSGA-II-TOPSIS hybrid optimization process, revealed that the most effective optimal control parameters were approximately 195°C for NT, 50% for IF, and a range of 55–66 mm/min for PS. This hybrid approach affirmed the reliability and robustness of the identified processing parameters for the production of top-quality FDM parts with desired dimensional accuracy and density.

AI-Enhanced Generative Design for Efficient Heat Exchangers in Thermoelectric Generators: Revolutionizing Waste Heat Recovery in Thermoelectricity

Thermoelectric generators (TEGs) offer the potential for converting waste heat into electricity, but their efficiency, particularly at low temperatures, remains inadequate. Plate-Fin Heat Exchangers (PFHEs) in TEG systems are not fully optimized, resulting in limited efficiency and applicability. The low conversion efficiency of TEGs means only a small fraction of waste heat is utilized, posing challenges to their long-term viability. While Genetic Algorithms (GAs) have shown promise in optimizing heat exchanger designs, advanced methods like Non-dominated Sorting Genetic Algorithm II (NSGAII) have yet to be fully applied for PFHE TEG design. This study addresses these challenges by using NSGA-II, combined with a semi-empirical model, to optimize PFHE design in TEG systems. The optimization focuses on refining fin design parameters such as number, width, and height while adhering toconstraints on fin area and pressure drop. The optimized design achieved a 3.94% increase in output power and a 1.72% increase in efficiency at 373.15 K, with conversion efficiency rising by 154.74% and maximum output power by 549.64% at 428.15 K. In conclusion, this research bridges the gap by applying NSGA-II to enhance PFHE design in TEGs, significantly improving performance and sustainability. Future work will explore alternative models and further optimization to achieve even higher efficiencies.

Optimization of submersible LNG centrifugal pump blades design based on support vector regression and the non-dominated sorting genetic algorithm Ⅱ

Optimizing the impeller blade design of submersible LNG centrifugal pump significantly enhances mechanical efficiency, reducing energy consumption and carbon emissions during LNG storage and transportation. This study focuses on optimizing blades of the centrifugal pump, combining Bezier blade design, CFD simulations, optimal Latin Hypercube sampling, Support Vector Regression (SVR), and Non-Dominated Sorting Genetic Algorithm II(NSGA-II) multi-objective optimization into a systematic method. During the development of this optimization method, it was found that directly applying sampled CFD simulation data for machine learning led to poor overall predictive performance of the models. To address this, a data augmentation method based on Gaussian noise was proposed. Through Bayesian optimization of the machine learning model's hyperparameters, the R2 of the predictive model was successfully increased from below 0.8 to above 0.99. The optimized design improved the prototype pump's efficiency from 70 % to 77.8 % under rated flow and head conditions. This efficiency gain is due to the significant reduction in flow separation vortices between blades and decreased turbulent kinetic energy between the impeller and diffuser vanes. Additionally, sensitivity and linear relationship analyses using the Sobol method and Pearson correlation provided valuable insights for blade optimization design.

Multitask scheduling on cloud additive manufacturing using NSGA-II

Purpose: Cloud manufacturing (CM) represents a new manufacturing paradigm that integrates distributed resources to provide on-demand services. The high consumer demand from various locations, coupled with the customizability and complexity of manufacturing, complicates task scheduling. In this context, 3D printers are crucial as innovative manufacturing technologies with significant potential in producing complex and custom products. Scheduling in CM falls under the non-deterministic polynomial time-hard category, where tasks must be scheduled and distributed rapidly. Considerations of distance, minimization of delays, and makespan become critical variables that must be considered. This research aims to schedule and distribute tasks in CM using the non-dominated sorting genetic algorithm II (NSGA-II) to minimize delays, reduce makespan, and decrease costs.Methodology: NSGA-II is employed to tackle the complexities of scheduling in CM. The strength of NSGA-II lies in its ability to determine optimal and efficient solutions for multiobjective problems. Tasks originating from requests at various locations are adjusted based on material parameters and dimensions and then distributed to providers while considering aspects such as makespan, delay minimization, and cost.Findings: The optimization results using NSGA-II demonstrate effective and efficient task distribution to providers. Across the four tested task distribution scenarios, the average computational time required was 5.59 seconds. Pareto analysis indicates a trade-off between various objective functions. Solutions with short distances tend to have increased maximum time and delays.Originality/value: NSGA-II is effective for task distribution with multiobjective considerations. Not all three objective functions can be optimized simultaneously, given the trade-offs between distance, maximum time, and lateness. The priority of the objective functions should be determined to achieve optimal results. If minimizing lateness is most important, the focus should be on points with low lateness values. Further development can be done by modifying the Pareto front to make data-driven decisions that consider these trade-offs.

Valley Path Planning on 3D Terrains Using NSGA-II Algorithm

Valley path planning on 3D terrains holds significant importance in navigating and understanding complex landscapes. This specialized form of path planning focuses on finding optimal routes that adhere to the natural contours of valleys within three-dimensional terrains. The significance of valley path planning lies in its ability to address specific challenges presented by valleys, such as varying depths, steep slopes, and potential obstacles. By following the natural flow of valleys, path planning can enhance the efficiency of navigation and minimize the risk of encountering difficult terrain or hazards. In recent years, an increasing number of researchers have focused on the study of valley path planning on 3D terrains. This study presents a valley path planning method utilizing the NSGA-II (Non-dominated Sorting Genetic Algorithm II) approach. To ensure that the paths generated by the algorithm closely follow the valley lines, the algorithm establishes an optimization function that includes three optimization criteria: mean altitude, flight route length, and mean offset. To test the performance of this algorithm, we conducted experiments based on workspaces based on three datasets full of 3D terrains and compared it with three baseline algorithms. The evaluation indicates that the suggested algorithm successfully designs routes that closely follow the valley contours.