Search Results (967)

Protective forests are vital to ecological security in arid desert regions, but their spatial distribution is often inefficient. This study aims to optimize the spatial distribution of protective forests in Alaer City using a combination of the Non-dominated Sorting Genetic Algorithm-II (NSGA-II) and the Future Land Use Simulation (FLUS) model. The optimization focuses on three objectives: economic benefits, ecological benefits, and food security. A neural network model is applied to analyze forest distribution suitability based on spatial factors. The results show that the optimized distribution significantly enhances GDP, carbon sequestration, water yield, and food production, while reducing soil erosion. The forest area is mainly concentrated along rivers, agricultural fields, and desert edges, with increased coverage at the Taklamakan Desert’s periphery improving wind and sand resistance. The FLUS model is validated with high accuracy (90.73%). This study provides a theoretical foundation for the sustainable development of protective forests, balancing ecological and economic goals in Alaer City. Full article

With the increasing presence of large-scale new energy sources, such as wind and photovoltaic (PV) systems, integrating traditional hydropower with wind and PV power into a hydro–wind–PV complementary system in economic dispatch can effectively mitigate wind and PV fluctuations. In this study, Markov chains and the Copula joint distribution function were adopted to quantize the spatiotemporal relationships among hydro, wind and PV, whereby runoff, wind, and PV output scenarios were generated to simulate their uncertainties. A dual-objective optimization model is proposed for the long-term hydro–wind–PV co-scheduling (LHWP-CS) problem. To solve the model, a well-tailored evolutionary multi-objective optimization method was developed, which combines multiple recombination operators and two different dominance rules for basic and elite populations. The proposed model and algorithm were tested on three annual reservoirs with large wind and PV farms in the Hongshui River Basin. The proposed algorithm demonstrates superior performance, with average improvements of 2.90% and 2.63% in total power generation, and 1.23% and 0.96% in minimum output expectation compared to BORG and NSGA-II, respectively. The results also infer that the number of scenarios is a key parameter in achieving a tradeoff between economics and risk. Full article

In simulations of elastic flow using the lattice Boltzmann method (LBM), the steady-state behavior of the flow at low capillary numbers is typically poor and prone to the formation of bubbles with inhomogeneous lengths. This phenomenon undermines the precise control of heat transfer, mass transfer, and reactions within microchannels and microreactors. This paper establishes an LBM multiphase flow model enhanced by machine learning. The hyperparameters of the machine learning model are optimized using the particle swarm algorithm. In contrast, the non-dominated sorting genetic algorithm (NSGA-II) is incorporated to optimize bubble lengths and stability. This results in a coupled multiphase flow numerical simulation model that integrates LBM, machine learning, and the particle swarm algorithm. Using this model, we investigate the influence of elastic flow parameters on bubble length and stability in a T-shaped microchannel. The simulation results demonstrate that the proposed LBM multiphase flow model can effectively predict bubble elongation rates under complex conditions. Furthermore, multi-objective optimization determines the optimal gas–liquid two-phase inlet flow rate relationship, significantly mitigating elastic flow instability at low capillary numbers. This approach enhances the controllability of the elastic flow process and improves the efficiency of mass and heat transfer. Full article

The hemispherical resonant gyroscope is the highest-precision solid-state vibration gyroscope, widely applied in aviation, aerospace, marine, and other navigation fields. As the core component of the hemispherical resonant gyroscope, the design of its structural parameters directly influences the key performance parameters of the resonator—specifically, the thermoelastic damping quality factor and the minimum frequency difference from interference modes—affecting the operational accuracy and lifespan of the gyroscope. However, existing research, both domestic and international, has not clarified the effect of structural parameters on performance laws. Thus, studying the mapping relationship between the resonator’s performance and structural parameters is essential for optimization. In this study, a hemispherical resonator with a midplane radius of 10 mm serves as the research object. Based on a high-precision finite element simulation model of an ideal hemispherical resonator, the mechanism of thermoelastic damping and the influence of structural parameters on performance are analyzed. A PSO-BP neural network mapping model is then developed to relate the resonator’s structural and performance parameters. Subsequently, the NSGA-II algorithm is applied to perform multi-objective mapping of these parameters, achieving an optimized resonator with a 4.61% increase in the minimum frequency difference from interference modes and a substantial improvement in thermoelastic damping of approximately 70.41%. The comprehensive, performance-oriented multi-objective optimization method for the structural parameters of hemispherical resonators proposed in this paper offers a cost-effective approach to high-performance design and optimization, and it can also be applied to other manufacturing processes under specific conditions. Full article

In this paper, a dynamic energy-efficient hybrid flow shop (TDEHFSP) scheduling model is proposed, considering random arrivals of new jobs and transport by transfer vehicles. To simultaneously optimise the maximum completion time and the total energy consumption, a co-evolutionary approach (DDQCE) using a double deep Q-network (DDQN) is introduced, where global and local search tasks are assigned to different populations to optimise the use of computational resources. In addition, a multi-objective NEW heuristic strategy is implemented to generate an initial population with enhanced convergence and diversity. The DDQCE incorporates an energy-efficient strategy based on time interval ‘left shift’ and turn-on/off mechanisms, alongside a rescheduling model to manage dynamic disturbances. In addition, 36 test instances of varying sizes, simplified from the excavator boom manufacturing process, are designed for comparative experiments with traditional algorithms. The experimental results demonstrate that DDQCE achieves 40% more Pareto-optimal solutions compared to NSGA-II and MOEA/D while requiring 10% less computational time, confirming that this algorithm efficiently solves the TDEHFSP problem. Full article

Aiming at the problem of optimizing the composition of manufacturing resources in part-level outsourcing of sewing machine case manufacturing, this paper proposes a service composition optimization method based on bi-level programming. We analyze the structure and production process of sewing machine cases, determine the required service composition of manufacturing resources, and establish the evaluation indicator system of manufacturing service composition in line with the interests of multiple parties. We also introduce the idea of bi-level programming, construct the optimization model of manufacturing service composition of sewing machine cases based on bi-level planning, analyze the characteristics of NSGA-Ⅱ (Non-dominated Sorting Genetic Algorithm II) algorithm and the improvement strategy, and complete the solution of the optimization model of manufacturing service composition of sewing machine cases. The experimental results show that the composition optimization method and the algorithm improvement strategy can well solve the bi-level programming model of sewing machine cases in the case of complex feature information and avoid falling into the local optimal solution. Full article

Aiming at the problem that drilling parameters are difficult to adjust in time for the driller due to the complex geological environment in underground coal mines, a drilling parameter control method based on online identification of drillability and multi-objective optimization of drilling parameters is proposed. A drillability grade identification model is established, with rotational speed and torque as input parameters, which can accurately identify the current drilling state. A multi-objective optimization model of the optimal drilling parameters is established with the mechanical specific energy and drilling speed prediction model as the objective functions, and the NSGA-II algorithm and TOPSIS algorithm are used for solutions and decision-making. A fuzzy PID controller is established. For the control of rotational speed parameters and drilling pressure parameters, the advantages and disadvantages of the fuzzy PID control method and the traditional PID control method are compared through simulation and experiments. A control method based on the drillability identification model and the multi-objective optimization model is established. According to the different drillability grades, the drilling parameters are adjusted in time to ensure the normal drilling state. By constantly approaching the optimal parameters through the drilling parameters, the drilling efficiency is improved. Through experimental verification, this control method effectively prevents the occurrence of drilling speed reduction and intermittent sticking and can adjust the drilling parameters to continuously optimize. Full article

Climate change poses significant challenges to energy efficiency and occupant comfort in residential buildings. This study introduces a simulation-based multi-objective optimization approach for architectural design, aimed at addressing these challenges and enhancing environmental sustainability. Utilizing EnergyPlus for energy simulations and jEPlus to identify objective functions and design parameters, the research employed the NSGA-II algorithm through jEPlus + EA for multi-objective optimization. A Morris sensitivity analysis assessed the impact of 25 design variables—including heating and cooling setpoints, air infiltration rates, insulation types, window selections, airflow rates, and HVAC systems—on key objective functions. Applied to a residential building in Sari, Iran, the study analyzed various climate change scenarios to minimize five main objectives: primary energy consumption, greenhouse gas emissions, indoor air quality, predicted percentage of dissatisfied, and visual discomfort hours. The weighted sum method was used to select optimal solutions from the Pareto front. Results demonstrated that the recommended energy retrofit strategies could reduce primary energy consumption by up to 60%, greenhouse gas emissions by 60%, predicted thermal dissatisfaction by 65%, and visual discomfort hours by 83%, while also achieving indoor air quality levels that meet ASHRAE recommended standards. However, the implementation of these energy-efficient solutions may require careful consideration of trade-offs in design decisions when addressing climate change challenges. Full article

In order to solve the problem of the insufficient vibration isolation performance of passenger cars in the suspension matching process, the six-degree-of-freedom (6-DOF) model, including three translational (x, y, z) and three rotational (roll, pitch, yaw) degrees of freedom, is established to comprehensively analyze the dynamic behavior of the powertrain mounting system. A 6-DOF dynamic model was established to analyze the decoupling rate and frequency distribution in its inherent characteristics, calculate the dynamic reaction of the suspension system, set the decoupling rate and the dynamic reaction of the suspension as optimization objectives, and use the NSGA II (Non-dominated Sorting Genetic Algorithm II) optimization algorithm to optimize the stiffness of the suspension. The 6-DOF decoupling of the whole suspension system is optimized and the dynamic reaction transmitted to the body is minimized. At the same time, this ensures that each suspension has enough static load support stiffness, and that its static deformation and amplitude are within the limit allowed under various working conditions, avoiding premature fatigue damage. The vibration isolation capability of the optimized system has been significantly improved, and the centroid acceleration has been significantly reduced under start–stop and road excitation conditions. The optimization method was effectively verified. Compared with existing studies focusing on single-objective optimization, the proposed NSGA II-based approach achieves a 93.4% decoupling rate in the critical Rx direction (vs. 59% pre-optimization) and reduces dynamic reaction forces by 8.3% (from 193 N to 177 N), demonstrating superior engineering applicability compared with traditional methods. Finally, the robustness analysis of the optimized stiffness met the requirements of production and manufacturing, indicating that the improvement of the decoupling rate of the suspension system and the optimization of the dynamic reaction force can effectively improve the vibration isolation performance, thereby improving the ride comfort of the vehicle. Full article

It is particularly important to improve the emergency rescue response ability of heavy-haul railways to ensure the safety of personnel and the efficiency of material transportation. The current research has achieved some results for multi-objective material dispatching, but it does not consider the impact of accident response level and material type on material dispatching scheme. In this study, a heavy-haul railway in China was selected as the research object. By designing a dual-objective material scheduling model, an optimal material scheduling scheme was obtained, and the optimal solution was solved by a non-dominated sorting genetic algorithm (NSGA-II). Under the condition of keeping the station unchanged and ensuring that the total amount of materials remained unchanged, an optimization scheme of relief material reserves that match the risk characteristics of the line is proposed. The results show that, based on the utility theory, the minimum distance of the improved dual-objective material dispatching is reduced by 34.8% (single accident point) and 62.99% (multiple accident points), and the total distance of material dispatching is reduced by 37.92% and 70.57%, respectively, indicating that the optimized reserve scheme can effectively shorten the response time and improve the rescue efficiency. The material reserve optimization scheme for emergency rescue stations proposed in this study has important reference value for improving the emergency rescue efficiency of heavy-haul railways. Full article

LED solar simulators currently face limitations in their spectral simulation capabilities, especially in terms of accurately incorporating AM0G and AM1.5G solar spectra. To this end, this study introduced a framework for an LED solar spectrum simulation algorithm that considers both AM0G and AM1.5G. This study examined the principle of solar spectrum discretization and reconstruction, established a foundation for analyzing the quality of solar spectrum reconstruction, and developed a non-dominated sorting genetic algorithm II (NSGA-II)-assisted long short-term memory (LSTM)-based solar spectrum simulation strategy. This strategy integrates a multi-objective genetic algorithm to generate training datasets and a neural network for solar spectrum simulation. A dataset generation method using the NSGA-II algorithm was implemented, which leveraged the 6500 K standard blackbody spectral curve, the spectral curve offset coefficients, and the spectral distributions of various narrowband LEDs. An LSTM-based neural network for solar spectrum simulation was developed, with the RMSE serving as the evaluation function. The analysis and selection of 29 narrowband LEDs produced 5000 solar spectrum simulation training datasets. The trained LSTM model achieved spectral matching accuracies within ±10.5% and ±9.3% for AM0G and AM1.5G, respectively, meeting the A+ level simulation standard for solar spectrum reconstruction considering AM0G and AM1.5G. These findings provide a theoretical foundation and technical advancements for high-precision solar spectrum reconstruction, which has practical implications for improving the efficiency and accuracy of solar energy systems, as well as supporting further research on solar spectrum utilization, and is expected to influence the development of more efficient solar simulators. Full article

The traditional construction industry is characterized by high energy consumption and significant carbon emissions, primarily due to its reliance on on-site manual labor and wet operations, which are not only low in mechanization but also result in low material efficiency and substantial construction waste. Prefabricated construction offers a new solution with its efficient production methods, significantly enhancing material utilization and construction efficiency. This paper focuses on the production scheduling optimization of prefabricated components. The production scheduling directly affects the construction speed and cost of prefabricated buildings. Given the complex modeling and numerous constraints faced by the production of prefabricated components, we propose an improved Non-dominated Sorting Genetic Algorithm II (NSGA-II) for multi-objective optimization. The algorithm incorporates adaptive operators and greedy concepts for local search, enhancing solution exploration and diversity. We segment the production of prefabricated components into six stages, analyzing dependencies and constraints, and form a comprehensive scheduling model with objectives of minimizing contract penalties, storage costs, and production time. Extensive experiments demonstrate that the improved NSGA-II provides a more balanced and larger set of solutions compared to baseline algorithms, offering manufacturers a wider range of options. This research contributes to the optimization of production scheduling in the prefabricated construction industry, supporting coordinated, sustainable, automated, and transparent production environments. Full article

Project schedules are typically the primary concern for most clients, yet the corresponding schedule risks have not received sufficient attention from project managers during multi-objective trade-offs. Therefore, to select the most valuable schedule plan in a complex risk environment, reveal the objective impact of schedule risks on multi-objective trade-offs, and reflect the decision-maker’s risk mitigation behaviour, this study aims to develop a Time–Cost–Quality Trade-off (TCQT) model constrained by both schedule risks and resources based on risk-driven principles. Furthermore, sensitivity analysis steps for identifying key risk factors are proposed. The Non-dominated Sorting Genetic Algorithm II (NSGA-II) and Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) methods are introduced to solve the TCQT model and make optimal solution decisions. Case study results demonstrate that the optimal scheduling plan derived from the model improves the total project time, cost, and quality by 11.48%, 4.52%, and 7.05%, respectively, compared to the initial plan formulated by the project team. Additionally, the model identifies the main risk factors affecting the achievement of each objective within the TCQT decision framework, providing crucial insights for decision-makers in formulating effective mitigation strategies. Full article

Battery energy storage systems (BESSs) can play a significant role in overcoming the challenges in Distribution Systems (DSs) with a high level of penetration from renewable energy sources (RESs). In this paper, the goal is to determine the optimal location, size, and charging/discharging dispatches of BESSs in DSs with a high level of photovoltaic (PV) installations. The problem of the location and size of BESSs is solved with multi-criteria optimization using Non-dominated Sorting Genetic Algorithm-II (NSGA-II). The criteria of the multi-criteria optimization are minimal investment costs for BESS and improvement of the network performance index. The network performance index includes the reduction in annual losses of active energy in DSs and the minimization of voltage deviations. The dispatch of a BESS is determined using auxiliary optimization. Linear Programming (LP) is used for auxiliary optimization, with the aim of dispatching the BESS to smooth the load profile in DS. The proposed optimization method differs from previous studies because it takes in its calculations all days of the year. This was performed using the K-means clustering technique. The days of one year are classified by the level of consumption and PV production. The optimization was performed for five different levels of PV penetration (60%, 70%, 80%, 90%, and 100%) and for two scenarios: the first with one BESS and the second with two BESSs. The proposed methodology is applied to the IEEE 33 bus balanced radial distribution system. The results demonstrate that with an optimal choice of location and parameters of the BESS, significant improvement in network performance is achieved. This refers to a reduction in losses of active power, improvement of voltage profile, smoothing the load diagram, and reducing the peak load. For the scenario with one BESS and PV penetration of 100%, the reduction in daily energy losses reaches a value of up to 10% compared to the base case (case without a BESS). The reduction in peak load goes to 20%. Further, the highest voltage during the day is significantly lower in all buses compared to the base case. Similarly, the lowest voltage during the day is considerably higher. The methodology from this paper can be applied to any radial distribution network with a variable number of BESSs. The testing results confirm the effectiveness of the proposed method. Full article

This paper proposes a cutting trajectory planning method for boom-type roadheaders using an improved Nondominated Sorting Genetic Algorithm II (NSGA-II) with an elitist strategy. Existing methods often overlook constraints related to cutterhead dimensions and target sections, affecting section formation quality. We develop a kinematic model for coordinate transformations and design a simplified cutterhead and constraint model to generate feasible cutting points. Bi-objective functions—minimizing cutting trajectory length and turning angle—are formulated as a bi-objective traveling salesman problem (BO-TSP) with adjacency constraints. NSGA-II is adapted with enhancements in adjacency constraint handling, population initialization, and genetic operations. Simulations and experiments demonstrate significant improvements in convergence speed and computation time. Virtual cutting experiments confirm trajectory feasibility under varying postures, achieving high formation quality. A comparison of planned and tracked trajectories shows a maximum deviation of 23.879 mm, supporting autonomous cutting control. This method advances cutting trajectory planning for roadway section formation and autonomous roadheader control. Full article