Non-dominated Sorting Genetic Algorithm II Research Articles

Solving Multi-Criteria Shortest Path by Optimization with Morphological Filters

The challenge of determining the shortest path within a multimodal transportation network involves identifying the most efficient travel route while considering various interconnected modes of transportation, such as roads, railways, and public transit. This problem becomes increasingly complex when numerous criteria and modes are involved, complicating the decision-making process. This study proposes a novel approach to computing the shortest path in multimodal networks, focusing on four modes of transportation: metro, trams, buses, and taxis. The optimization criteria include distance, travel time, and monetary cost. The proposed method utilizes a new metaheuristic called Optimization by Morphological Filters (OMF), inspired by image processing techniques. This approach was compared with the Genetic Algorithms (GA) and the Non-Dominated Sorting Genetic Algorithm II (NSGA-II). Experiments were carried out using graph models of multimodal transport networks that closely resemble real-world scenarios varying in size. Furthermore, the proposed method was evaluated using a real network from the city of Lyon, France. The results demonstrate that the OMF approach performs well in terms of convergence to optimal solutions and computation time.

Multi-Target Firefighting Task Planning Strategy for Multiple UAVs Under Dynamic Forest Fire Environment

The frequent occurrence of forest fires in mountainous regions has posed severe threats to both the ecological environment and human activities. This study proposed a multi-target firefighting task planning method of forest fires by multiple UAVs (Unmanned Aerial Vehicles) integrating task allocation and path planning. The forest fire environment factors such high temperatures, dense smoke, and signal shielding zones were considered as the threats. The multi-UAVs task allocation and path planning model was established with the minimum of flight time, flight angle, altitude variance, and environmental threats. In this process, the study considers only the use of fire-extinguishing balls as the fire suppressant for the UAVs. The improved multi-population grey wolf optimization (MP–GWO) algorithm and non-Dominated sorting genetic algorithm II (NSGA-II) were designed to solve the path planning and task allocation models, respectively. Both algorithms were validated compared with traditional algorithms through simulation experiments, and the sensitivity analysis of different scenarios were conducted. Results from benchmark tests and case studies indicate that the improved MP–GWO algorithm outperforms the grey wolf optimizer (GWO), pelican optimizer (POA), Harris hawks optimizer (HHO), coyote optimizer (CPO), and particle swarm optimizer (PSO) in solving more complex optimization problems, providing better average results, greater stability, and effectively reducing flight time and path cost. At the same scenario and benchmark tests, the improved NSGA-II demonstrates advantages in both solution quality and coverage compared to the original algorithm. Sensitivity analysis revealed that with the increase in UAV speed, the flight time in the completion of firefighting mission decreases, but the average number of remaining fire-extinguishing balls per UAV initially decreases and then rises with a minimum of 1.9 at 35 km/h. The increase in UAV load capacity results in a higher average of remaining fire-extinguishing balls per UAV. For example, a 20% increase in UAV load capacity can reduce the number of UAVs from 11 to 9 to complete firefighting tasks. Additionally, as the number of fire points increases, both the required number of UAVs and the total remaining fire-extinguishing balls increase. Therefore, the results in the current study can offer an effective solution for multiple UAVs firefighting task planning in forest fire scenarios.

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.

Climate-adaptive optimal water resources management: A multi-sectoral approach for the Munneru river basin, India.

Climate change profoundly affects water resource allocation by disrupting the availability, distribution, and quality of water across various regions. Optimal allocation of water resources represents a comprehensive strategy for water resource management by addressing the intricate connections between water allocation systems and their repercussions on the environment, society, and economy. In this study, an Optimal Water Resources Management (OWRM) framework was developed, focusing on the optimal allocation of water resources and crop planting structures across various sectors. The Munneru river basin, located in the lower Krishna River region of India, was selected as the study area to validate the proposed framework. Five distinct water-demanding sectors-irrigation, domestic, livestock, industrial, and irrigation water requirements for major agricultural seasons-were identified in the study area, and their sectoral water demands were calculated at the basin level. The crop water and irrigation water requirements for various crops were estimated using the CROPWAT tool, while the framework also optimized crop planting structures to maximize returns and resource efficiency. The Non-dominated Sorting Genetic Algorithm-II (NSGA-II) was applied, with two objectives focused on equity and economic value. Superior solutions were then identified using the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS). The OWRM framework was applied after identifying critical cases of water availability in future periods under climate change scenarios. Through this integrated approach, an average annual increase of 52.6% in agricultural sector returns was achieved for the simulation period (2016-17 to 2022-23). For a condition of providing at least 90% water supply to each sector, the optimal crop patterns led to revenue increases of 136.4%, 59.2%, and 74.7% compared to actual revenues for the water years 2020-21, 2021-22 and 2022-23 respectively. The developed OWRM methodology can be applied to other basins across the world that are impacted by climate change.

Multi-objective geometric optimization of protrusion, droplet ribs, and inclined upper plate of a slit jet impingement heat sink to enhance its thermal performance

To meet the needs of heat transfer and temperature uniformity in the heat dissipation of high-heat-flux electronic devices, a novel slit jet impingement heat sink with a protrusion in the stagnation region, droplet ribs in the wall jet region, and an inclined upper-plate (PDI-SJIHS) is proposed, the coolant is Al2O3-H2O nanofluid. The influences of the structural parameters of the above three components and the Reynolds number on the heat transfer and flow of the PDI-SJIHS are studied numerically. Using the comprehensive heat transfer and temperature standard deviation as objective functions, a multi-objective optimization of PDI-SJIHS is conducted using non-dominated sorting genetic algorithm-Ⅱ (NSGA-Ⅱ). The results indicate that the combination of the three components enhances the thermal performance of the PDI-SJIHS, compared with the slit jet impingement heat sink with only one of the above components. Rising the Reynolds number can enhance the thermal characteristics of the PDI-SJIHS. For a Reynolds number of 8000 and nanofluids with a 3% volume fraction, the average convective heat transfer coefficient, friction coefficient, and comprehensive heat transfer coefficient of the optimized PDI-SJIHS are increased by 182%, 153%, and 108%, respectively, and the standard deviation of temperature is 91% less than that of F-SJIHS.

Design and Performance Characterization of the E-core Outer-Rotor Hybrid-Excitation Flux Switching Machine

This paper proposes an E-core outer-rotor hybrid-excitation flux switching (OR-HEFS) machine for in-wheel direct driving application. According to the general air gap field modulation theory, the magneto-motive force (MMF) permeance model was established to investigate the air gap flux density, and then the torque generation, the flux regulation principle, and the excitation-winding-induced voltage of the E-core OR-HEFS machine were analyzed. To characterize the output performances, the influence of the design parameters was investigated for the E-core OR-HEFS machine, including the split ratio, stator tooth arc, PM arc, fault-tolerant tooth arc, rotor tooth arc, stator yoke width and rotor yoke width. The performances contained the output torque, torque ripple, flux regulation ratio, and the excitation-winding-induced voltage. On this basis, the aforementioned four performances were optimized by means of the non-dominated sorting genetic algorithm II (NSGA-II). Based on the optimization result, a prototype was manufactured and tested to verify the whole investigation of this paper.

Optimized Design of a Triangular Shear Piezoelectric Sensor Using Non-Dominated Sorting Genetic Algorithm-II(NSGA-II)

A new piezoelectric sensor with a triangular shear structure was designed to conduct the deformation monitoring of geotechnical bodies in mining airspace. Firstly, a three-dimensional sensor model was developed to analyze the impact of structural parameters on resonant frequency and voltage, utilizing both finite element and experimental methods. Secondly, the NSGA-II genetic algorithm was employed to optimize the sensor’s structural parameters, focusing on resonant frequency and voltage, resulting in a Pareto optimal solution set. For the first time, the optimal parameter combination was selected by minimizing the difference method (the height of the mass block was 10.6 mm, the thickness of the piezoelectric plate was 3.29 mm, the height of the piezoelectric plate was 8.1 mm, and the height of the central column was 19 mm). The optimized sensor exhibited a 4.14% increase in resonant frequency and a 9.11% increase in voltage. Finally, the prototype was fabricated, and the effectiveness and feasibility of the design were verified through experiments. The findings indicate the sensor’s promising potential for monitoring geotechnical deformation in mining airspace regions.

Carbon Policies and Liner Speed Optimization: Comparisons of Carbon Trading and Carbon Tax Combined with the European Union Emissions Trading Scheme

This paper explores how optimizing vessel speeds can help reduce carbon emissions in the maritime industry. Focusing on liner shipping routes between China and Europe, it examines how carbon pricing mechanisms, including carbon taxes and emissions trading under the European Union Emissions Trading Scheme (EU ETS), impact operational costs and emissions reduction. With the use of advanced optimization methods, such as the Non-dominated Sorting Genetic Algorithm-II (NSGA-II) and the Technique for Order of Preference by Similarity to an Ideal Solution (TOPSIS), this research explores the balance between adjusting vessel speeds and minimizing emissions. The findings show that shipping companies on the China–Europe route can reduce the financial strain of carbon pricing by carefully managing speeds and voyage times. This study compares two scenarios of carbon tax policy and carbon trading rights in terms of voyage costs and carbon emissions. The results of this comparison based on the given parameters indicate a reduction of 1124 tons of carbon emissions with the carbon tax policy scenario, while the carbon trading rights scenario allows for more voyages yearly (5.24 vs. 5.30). This demonstrates one policy being more economical, while the other is also more environmentally efficient. These insights support the development of strategies that align environmental goals with economic priorities, paving the way for more sustainable maritime operations. The study introduces its objectives and reviews relevant literature by presenting a detailed methodology, incorporating emissions modeling with clearly defined parameters. The analysis presents results that undergo sensitivity testing and limitations using MATLAB (R2022a version). The study concludes by discussing policy implications and recommendations for future research and practical advancement

A method for calibrating robotic kinematic parameters based on a multi-error source model and an optimized measurement pose set

Purpose The purpose of this study is to improve the accuracy of industrial robot kinematic parameter identification and position accuracy by solving the problem of insufficient consideration of error sources in the kinematic parameter identification model and optimizing the selection of measurement pose set. Design/methodology/approach In this study, a kinematic calibration method for industrial robots considering multiple error sources is proposed. Based on the Modified Denavit Hartenberg (MD-H) model, a robot kinematics identification model including joint reduction ratio error, target ball installation error and coordinate system transformation error is established. Taking the optimal observability index O1 and the minimum flexible deformation as the optimization objectives, a measurement pose set optimization method based on Non-dominated Sorting Genetic Algorithm II (NSGA-II) is proposed to obtain a measurement pose set with higher identification accuracy. Findings Through experiments conducted with the Nantong Zhenkang ZK1400-6 robot as the test subject, the kinematic parameters identified by the optimized measurement pose set are more accurate than the randomly selected measurement pose set, and the positioning accuracy of the robot is improved from 2.11 to 0.31 mm, an increase of 85.3%. Originality/value This study introduces a position error model that comprehensively accounts for the error sources causing positioning inaccuracies. Building on this foundation, a novel flexible deformation index is proposed to quantify the flexible deformation in the measurement pose set, thereby reducing the impact of such deformation on the position error in the model. To the best of the authors’ knowledge, for the first time, this study presents an optimization method for the measurement pose set based on NSGA-II, using the flexible deformation index and observability index as objectives for multi-objective optimization, simultaneously optimizing the pose error and Jacobian matrix in the error model.

Optimizing concrete mix proportions with zeolite, GGBS, and CDW: a data-driven approach integrating experimental analysis and machine learning models

Abstract The depletion of natural sand resources and the environmental impact of cement production necessitate sustainable alternatives in concrete manufacturing. This study evaluates the potential of zeolite, ground granulated blast-furnace slag (GGBS), and construction and demolition waste (CDW) as partial replacements for sand in concrete mix proportions. Experimental investigations revealed that the optimal mix proportion, identified as Mix Batch M4 (60% Sand, 20% Zeolite, 10% GGBS, and 10% CDW), achieved a compressive strength (CS) of 67.37 MPa, flexural strength (FS) of 6.80 MPa, split tensile strength (ST) of 5.61 MPa, and notable reductions in water absorption (WA) to 4.00% and drying shrinkage (DS) to 4.02%. Additionally, durability improvements included a 30% reduction in rapid chloride permeability and enhanced ultrasonic pulse velocity (UPV) and rebound hammer (RH) values. Advanced machine learning models were utilized to analyze and optimize the mix designs, integrating the Sparrow Search Algorithm (SSA) with models such as Extreme Gradient Boosting (XGB), Backpropagation Neural Network (BPNN), Support Vector Regression (SVR), Random Forest Regression (RFR), Gradient Boosting Regression (GBR), and LightGBM. The XGB model demonstrated the highest predictive accuracy with an R2 of 1.000. Multi-objective optimization techniques, including Non-dominated Sorting Genetic Algorithm II (NSGA-II), Multi-Objective Particle Swarm Optimization (MOPSO), and Genetic Algorithm with Fuzzy models, were employed to refine mix proportions further, balancing mechanical properties, material sustainability, and environmental benefits. This study highlights significant reductions in natural sand consumption and waste generation while enhancing concrete performance. Practical implications include reduced environmental impact, improved resource efficiency, and the promotion of circular economy principles. These findings provide a pathway toward innovative and sustainable concrete solutions, aligning with global sustainability goals in the construction industry.

Comprehensive benefit evaluation of mineral resources development based on dual population constrained multi-objective evolutionary algorithm

With the increasing global demand for mineral resources, the comprehensive benefit evaluation of mineral resources development is playing an increasingly important role in the field of mineral resources development. However, there are few attempts to use multi-objective evolutionary algorithms (MOEAs) to study the comprehensive benefit evaluation problem of mineral resources development. To effectively solve this problem, this study established a multi-objective optimization model for comprehensive benefit evaluation of mineral resources development and proposed a dual-population constrained multi-objective evolutionary algorithm (CMOEA). The algorithm uses two populations to organically combine the efficient convergence of the non-dominated sorting genetic algorithm II (NSGA-II) with the advantage of the adaptive geometry estimation based multi-objective evolutionary algorithm (AGE-MOEA) in estimating the shape of the Pareto front, named co-evolution based on non-dominated sorting genetic algorithm and adaptive geometry estimation (C-NSGA-AGE). 3 test suites are used to verify the performance of the proposed algorithm. Experimental results show that compared with competing algorithms, C-NSGA-AGE has strong competitiveness in most test functions. The proposed method is used to solve the model, and five optimal comprehensive benefit evaluation schemes are given for different objectives. Among them, the maximum benefit is obtained when each objective is developed in a balanced way, which reaches 0.837181.

Multi-objective mixed-model assembly line balancing with hierarchical worker assignment: A case study of gear reducer manufacturing operations

Assembly lines, generally speaking, can reduce production costs, shorten cycle times, and achieve higher quality levels. Since the current market is characterized by increasing product variability, mixed-model assembly lines, in which similar product models can be assembled simultaneously, are more suitable to respond to varied market demands than traditional single-model assembly lines. In addition, in an assembly line, tasks often differ in processing requirements, and workers may have different qualification levels. This study, therefore, aims to construct models for the multi-objective mixed-model assembly line balancing problem with hierarchical worker assignment (MO-MALBP-HW). The goal is to generate a suitable plan for a mixed-model assembly line balancing problem considering the constraint of a hierarchical workforce, the cost of a hierarchical workforce, and production cycle time. When the problem is simple, it can be solved by a mixed integer programming (MIP) model. When the problem becomes complex, it can be solved by a multi-objective genetic algorithm (MOGA) and a non-dominated sorting genetic algorithm II (NSGA-II) to obtain a near-optimal solution. The implementation of this model can effectively manage the multi-objective mixed-model assembly line balancing plan, thereby improving plant efficiency and reducing cost.

Rolling schedule design for the ESP rolling process based on NSGA-II-DE.

Multiple processes connected closely during the endless strip production (ESP) rolling, it is difficult to obtain the global optimal solution by multi-objective modelling of a single process, and the parameters to be optimized coupled with each other. To obtain the optimal solution, a multi-objective optimization model combining the power consumption, product quality, and loading balance was proposed for the design of an ESP rolling schedule. The thickness and heating temperature were simultaneously taken as the decision variables for coupling the temperature and loading in the rolling process, and the non-dominated sorting genetic algorithm-II (NSGA-II) based on differential evolution (NSGA-II-DE) was applied to obtain the Pareto solutions. To select an optimal solution, a satisfaction function was designed and applied to fully utilize the Pareto solutions. Furthermore, to prove the precision and efficiency of the method, the online schedule and that obtained by the NSGA-II method were compared. The results proved that the final selected solution had better quality and a more balanced loading force than the other two types, which could provide guidance for the actual production process.

A new multi-objective optimization algorithm for separation processes

This paper proposes a new algorithm, named guided population adaptive genetic algorithm (GAGA), to solve optimization problem of the complex separation processes. In GAGA, the strategies of adaptive crossover and mutation, leader selection, boundary random walk, neighborhood guidance and spiral updating position are introduced to enhance the guidance of population evolution. The capability of GAGA is investigated by 8 multi-objective benchmark problems. The results are compared with four well-known multi-objective optimization algorithms. The average values of inverted generational distance (IGD) and generational distance (GD) are 0.1626 and 0.5363, respectively, which is proved to be a robust and reliable model. Moreover, GAGA is validated through methacrylic acid (MAA) separation process with multi-cycle flows. The optimization efficiency of GAGA has accelerated by 2 times compared with non-dominated sorting genetic algorithm-II (NSGA-II), with results of 4.7% reduction in total annual cost (TAC) and 4.3% reduction in global energy consumption (GEC).

Optimization of design parameters and operation conditions of solar-air source heat pump coupled system for rural buildings in cold and severe cold regions

Integrating solar energy utilization into air source heat pump heating systems can effectively cut down on energy consumption. However, the complexity of coupled systems poses a challenge to system performance optimization. In this paper, a solar-air source heat pump coupled system designed for heating in cold (Beijing) and severe cold (Changchun) regions is developed and analyzed by TRNSYS software. Response surface methodology (RSM) is employed to establish regression models for different performance indicators, and then the non-dominated sorting genetic algorithm-II (NSGA-II) is adopted to solve the multi-objective optimization of interactive performance parameters through Pareto optimal solution set. The regression models of the performance indicators, i.e., the energy consumption (P), the solar energy assurance rate (Ar) and the ratio of heating supply from solar energy storage tank to building load (Hs), are found to be closely related to the area of solar collectors (A) and the volumes of storage tanks (Vl for solar collector loop and Vs for ASHP loop). The obtained Pareto set successfully balances the optimal values of interactive performance indicators. In cold regions, P, Ar, Hs of the coupled system can be improved respectively by 31.79%, 33.00% and 40.07% compared to the single air source heat pump system. While in severe cold regions, these improvements are 16.25%, 15.35% and 28.38%, respectively. The design method and optimization procedure of this study provide a basis for the optimization of the solar and auxiliary heat source coupled heating systems.

Empirical Study of Multi-Objective Risk Portfolio Optimization Based on NSGA-II

The purpose of the article. The application of multi-objective optimization in portfolio management has gained significant attention in asset management. This study aims to uncover the potential advantages of dynamic portfolio optimization using a multi-objective genetic algorithm to address the challenges of ever-changing market conditions. Methodology. By incorporating multi-objective optimization, this paper comprehensively examines three key portfolio objectives: minimizing two risk types and maximizing returns. The approach involves constructing portfolios, initializing the population using the Non-Dominated Sorting Genetic Algorithm II (NSGA-II), and employing crossover and mutation steps to achieve Pareto optimality. Additionally, this study compares the performance of two risk minimization strategies through traditional portfolio backtesting. Results of the research. The results indicate that the multi-objective risk genetic algorithm not only effectively explores the portfolio space but also handles conflicting optimization objectives, thereby enhancing the comprehensiveness and flexibility of investment decisions. However, its performance depended on the chosen risk measurement methods, and the backtesting returns were unstable.

Optimal and reliable design of stand-alone hybrid renewable energy systems: A multi-objective approach

ABSTRACT This study presents solutions for multi-objective, multi-constraint optimization for both photovoltaic (PV) and photovoltaic/thermal (PV/T) stand-alone hybrid renewable energy systems (SA-HRES). The main objective is to optimize the size and improve the reliability of SA-HRES, which relies exclusively on renewable resources for hydrogen production. To solve the problem, an optimization algorithm based on Non-Dominated Sorting Genetic Algorithm-II (NSGA-II) was proposed and a new power management strategy (PMS) was developed. The reliability index, known as Power Loss Probability Percentage (LPSP), serves as both an objective function and a constraint. The waste energy percentage (PEW) is evaluated as a constraint to ensure that system efficiency remains within specified limits. Among the Pareto optimal solutions that satisfying the constraints of 10% LPSP and 5% PEW, the lowest PEW solutions for PV SA-HRES are $9388.5 with a PEW of 4.32% and LPSP of 6.0%. For PV/T SA-HRES, these values are $9211.6, 3.85%, and 6.9%, respectively. Despite similar annualized cost of system (ACS) values, the PV/T solution, which uses fewer PV/T, has an LPSP with a negligible difference and a lower PEW compared to the PV solution. This suggests the potential benefits of using PV/T instead of PV in HRES, offering improved efficiency and reliability.

Enhanced NSGA-II algorithm based on novel hybrid crossover operator to optimise water supply and ecology of Fenhe reservoir operation

Reservoir-operation optimisation is a crucial aspect of water-resource development and sustainable water process management. This study addresses bi-objective optimisation problems by proposing a novel crossover evolution operator, known as the hybrid simulated binary and improved arithmetic crossover (SBAX) operator, based on the simulated binary cross (SBX) and arithmetic crossover operators, and applies it to the Non-dominated Sorting Genetic Algorithms-II (NSGA-II) algorithm to improve the algorithm. In particular, the arithmetic crossover operator can obtain an optimal solution more precisely within the solution space, whereas the SBX operator can explore a broader range of potential high-quality solutions. Considering the advantages of both operators, this study introduces an improved arithmetic operator to reduce the risk of local convergence inherent in conventional arithmetic operators. Subsequently, two strategies for the SBAX operator are discussed: SBX operator + new arithmetic operator and new arithmetic operator + SBX operator. The convergence of the bi-objective Pareto solution set is evaluated based on the generation and inverted generational distances. This method is used for the collaborative optimisation of the water supply and ecological operation of the Fenhe Reservoir, where its effectiveness is demonstrated. A comparative analysis of the bi-objective optimisation schemes obtained using different crossover operators indicates the following: (1) the NSGA-II algorithm based on the SBAX operator achieves a convergence efficiency that is 14.25–41.95% higher than that of the conventional NSGA-II algorithm; (2) the reservoir operation indices of the scheduling scheme derived from the NSGA-II algorithm based on the SBAX operator significantly outperform those obtained using the conventional NSGA-II algorithm. The optimal strategy reduces the annual average water abandonment by 11.2–14.52 million m3. This study provides a novel approach for bi-objective optimisation and sustainable reservoir management.

Application of multilayered porous media for heat transfer optimization in double pipe heat exchangers using neural network and NSGA II

This study investigates the use of multi-layered porous media (MLPM) to enhance thermal energy transfer within a counterflow double-pipe heat exchanger (DPHE). We conducted computational fluid dynamics (CFD) simulations on DPHEs featuring five distinct MLPM configurations, analyzed under both fully filled and partially filled conditions, alongside a conventional DPHE. The impact of various parameters such as porous layer arrangements, thickness, and flow Reynolds numbers on pressure drop, logarithmic mean temperature difference (LMTD), and performance evaluation criterion (PEC) was assessed. The results demonstrate that the PEC improves by approximately three times with the optimal MLPM configuration, where the porous layers decrease from the interfacial wall outward under fully filled conditions. To further optimize performance, we trained a neural network using 507 simulations to establish a continuous correlation between input variables and output results. A multi-objective optimization was then implemented using the non-dominated sorting genetic algorithm II (NSGA-II) to identify the optimal operating conditions. The Pareto front of the optimal points was established, allowing designers to select specific points based on their operational requirements. This research provides valuable insights into the potential of MLPM to significantly enhance the thermohydraulic performance of DPHEs and establishes a framework for future optimization studies.

Dual-objective optimization of prefabricated component logistics based on JIT strategy

Prefabricated construction involves manufacturing components in a factory and then transporting them to a construction site for assembly, yielding resource savings and improved efficiency. However, the large size and weight of prefabricated components, along with strict delivery requirements, introduce logistical challenges, such as increased carbon emissions during transport and site congestion. This study addresses the dual-objective vehicle scheduling problem for prefabricated components. It proposes a dual-objective optimization model for prefabricated component logistics, guided by the Just-In-Time (JIT) strategy. The model comprehensively considers on-site and off-site logistics, accounts for uncertainties, and details the logistics process for each component. Its objectives are to reduce carbon emissions during logistics and enhance customer satisfaction. An improved Non-dominated Sorting Genetic Algorithm II (NSGA-II) is used to solve the model, offering enhanced solution diversity and local search capabilities. The model is validated through case studies, with sensitivity analyses conducted to further assess performance. Results indicate that the proposed model provides effective vehicle scheduling solutions that meet optimization objectives. Compared to traditional logistics models, the JIT logistics model demonstrates greater resilience to uncertainty, providing scientifically based decision support for logistics management in prefabricated construction.