Search Results (839)

High penetration and output volatility of island wind and photovoltaics (PV) pose challenges to energy consumption and supply–demand balance, and cost-effective energy storage configuration. A coupled dispatch model for a wind–PV–storage system is proposed, which treats multiple canal units as virtual ‘loads’ that switch between generation and pumping under constraints of power balance and available water head model. Considering the variable reservoir–irrigation feature, a multi-objective model framework is developed to minimize both economic cost and storage capacity required. An augmented Lagrangian–Nash product enhanced NSGA-II (AL-NP-NSGA-II) algorithm enforces constraints of irrigation shortfall and overflow via an augmented Lagrangian term and allocates fair benefits across canal units through a Nash product reward. Moreover, updates of Lagrange multipliers and reward weights maintain power balance and accelerate convergence. Finally, a case simulation (3.7 MW wind, 7.1 MW PV, and 24 h rural load) is performed, where 440.98 kWh storage eliminates shortfall/overflow and yields 1.5172 × 10⁴ CNY. Monte Carlo uncertainty analysis (±10% perturbations in load, wind, and PV) shows that increasing storage to 680 kWh can stabilize reliability above 98% and raise economic benefit to 1.5195 × 10⁴ CNY. The dispatch framework delivers coordination of irrigation and power balance in island microgrids, providing a systematic configuration solution. Full article

This study proposes an artificial intelligence-integrated Six Sigma framework for reducing multiple critical defects in plastic injection molding using real industrial production data from a washing-machine control-panel manufacturing line. Predictive models were developed under severe class imbalance conditions and combined with SHAP-based interpretability to identify the most influential process parameters. A multi-objective NSGA-II optimization strategy was then employed to simultaneously minimize major defect types, including gas-trapped burn (GTB), short shot (SS), sink mark (SK), and flash (FL). The proposed framework was validated through on-site continuous trial production of 300 parts after process stabilization, demonstrating substantial and consistent defect reduction. The results indicate that the integration of data-driven modeling, explainable artificial intelligence, and evolutionary multi-objective optimization provides a practical and scalable approach for quality improvement in industrial injection molding processes. Full article

In response to the significant threat posed by low-frequency P-band anti-stealth radar to aircraft stealth capabilities, this paper examines the inverse design of electromagnetic parameters for a single-layer, thin P-band radar absorbing material. An efficient computational model is constructed by integrating impedance boundary conditions with the characteristic basis function method. The NSGA-II genetic algorithm is employed to accomplish multi-objective co-optimization of electromagnetic parameters and material thickness. Results demonstrate that the optimized single-layer RAM, with a relative permittivity of μ_r = 3.3078 + j3.9018 and permeability of ε_r = 2.3522 + j6.9519, exhibits outstanding P-band absorption characteristics within a thickness constraint of only 1 mm. Applying this RAM to aircraft wing components’ leading/trailing edges, intake duct cavities, and lip areas effectively suppresses edge diffraction and cavity scattering. The target achieves a maximum forward average RCS reduction of −13.97 dB and a maximum rearward average RCS reduction of −5.03 dB, maintaining stable performance within a pitch angle range of 0° ± 5°. Full article

Addressing the challenges of low resource-use efficiency and supply–demand mismatch in Hami melon production, this study investigated the interactive effects of irrigation and fertilization to identify an optimal regime that balances yield, water conservation, and resource-use efficiency (i.e., water use efficiency and fertilizer partial factor productivity). A greenhouse experiment was conducted in Hami, Xinjiang, employing a two-factor design with five irrigation levels (W1–W5: 60–100% of full irrigation) and three fertilization levels (F1–F3: 80–100% of standard rate), replicated three times. Growth parameters, yield, water use efficiency (WUE), and partial factor productivity of fertilizer (PFP) were evaluated and comprehensively analyzed using the entropy-weighted TOPSIS method, regression analysis, and the NSGA-II multi-objective genetic algorithm. Results demonstrated that irrigation volume was the dominant factor influencing growth and yield. The W4F3 treatment (90% irrigation with 100% fertilization) achieved the optimal outcome, yielding 75.74 t ha⁻¹—a 9.71% increase over the control—while simultaneously enhancing WUE and PFP. Both the entropy-weighted TOPSIS evaluation (C = 0.998) and regression analysis (optimal irrigation level at w = 0.79, ~90% of full irrigation) identified W4F3 as superior. NSGA-II optimization further validated this, generating Pareto-optimal solutions highly consistent with the experimental optimum. The model-predicted optimal regime for greenhouse Hami melon in Xinjiang is an irrigation amount of 3276 m³ ha⁻¹ and a fertilizer application rate of 814.8 kg ha⁻¹. This regime facilitates a 10% reduction in irrigation water and a 5% reduction in fertilizer input without compromising yield, alongside significantly improved resource-use efficiencies. Full article

With advancements in simulation technology, fluid–thermal interaction (FTI) has become a vital tool in machinery powertrain development. Traditional engine cooling systems, with mechanically coupled components like water pumps and fans, lack adaptive cooling control. Electronic cooling systems, however, use variable-speed components to enhance performance. Combining FTI simulations with intelligent optimization algorithms offers a novel approach to designing control strategies for these systems. This study establishes a multi-objective optimization model for pump and fan speed control in electronic cooling systems. Using MATLAB/Simulink 2018 and Fluent 2022R1, co-simulations were performed, and an elite-strategy-based NSGA-II algorithm was implemented. Different weights were assigned to optimization objectives based on engine performance requirements. The results provide fitted functions for heat exchange capacity and cylinder liner temperature versus flow rates, along with optimal solutions for a 65 kW engine under three weight configurations. These findings support control strategy design and demonstrate the integration of FTI with genetic algorithms. Full article

Feed formulation is a typical multi-objective optimization problem that aims to minimize cost while satisfying multiple nutritional constraints. However, existing methods often suffer from limitations in handling nonlinear constraints, high-dimensional decision spaces, and solution feasibility. To address these challenges, this study proposes a multi-objective feed formulation method based on an improved Non-dominated Sorting Genetic Algorithm II (NSGA-II). A hybrid Dirichlet–Latin Hypercube Sampling (Dirichlet-LHS) strategy is introduced to generate an initial population with high feasibility and diversity, together with an iterative normalization-based dynamic repair operator to efficiently handle ingredient proportion and nutritional constraints. In addition, an adaptive termination mechanism based on the hypervolume improvement rate (Hypervolume Termination, HVT) is designed to avoid redundant computation while ensuring effective convergence of the Pareto front. Experimental results demonstrate that the Dirichlet–LHS strategy outperforms random sampling, Dirichlet sampling, and Latin hypercube sampling in terms of hypervolume and solution diversity. Under identical nutritional constraints, the improved NSGA-II reduces formulation cost by 1.52% compared with multi-objective Bayesian optimization and by 2.17% relative to conventional feed formulation methods. In a practical application to meat sheep diet formulation, the optimized feed cost is reduced to 1162.23 CNY per ton, achieving a 4.83% cost reduction with only a 1.09 s increase in computation time. These results indicate that the proposed method effectively addresses strongly constrained multi-objective feed formulation problems and provides reliable technical support for precision feeding in intelligent livestock production. Full article

With the widespread adoption of electric vehicles, commercial charging stations (CCS) have grown rapidly as a core component of charging infrastructure. Due to the concentrated and high-power charging load characteristics of CCS, a ‘peak on peak’ phenomenon can occur in the power distribution network. Demand response (DR) serves as an important and flexible regulation tool for power systems, offering a new approach to addressing this issue. However, when CCS participates in DR, it faces a dual dilemma between operational revenue and user satisfaction. To address this, this paper proposes a bi-level, multi-objective framework that co-optimizes station profit and nonlinear user satisfaction. An asymmetric sigmoid mapping is used to capture threshold effects and diminishing marginal utility. Uncertainty in users’ charging behaviors is evaluated using a Monte Carlo scenario simulation together with chance constraints enforced at a 0.95 confidence level. The model is solved using the fast non-dominated sorting genetic algorithm, NSGA-II, and the compromise optimal solution is identified via the entropy-weighted Technique for Order Preference by Similarity to an Ideal Solution (TOPSIS). Case studies show robust peak shaving with a 6.6 percent reduction in the daily maximum load, high satisfaction with a mean of around 0.96, and higher revenue with an improvement of about 12.4 percent over the baseline. Full article

Auxiliary power demand in battery electric vehicles continues to increase as manufacturers transition toward multi-low-voltage architectures that combine 48 V and 12 V buses to improve load distribution flexibility and overall system efficiency. This paper evaluates several auxiliary power module (APM) architectures in terms of scalability, efficiency, complexity, size, and cost for supplying two low-voltage buses (e.g., 48 V and 12 V) from the high-voltage battery. Based on this assessment, a cascaded APM configuration is adopted, consisting of an isolated dual active bridge (DAB) converter followed by a non-isolated synchronous buck converter. A multi-objective optimization framework based on the NSGA-II algorithm is developed for the DAB stage to maximize efficiency and power density while minimizing cost. The optimized 13 kW DAB stage achieves a peak efficiency of 95% and a power density of 4.1 kW/L. For the 48 V/12 V buck stage, a 2 kW commercial GaN-based converter with a mass of 0.5 kg is used as the reference design, achieving a peak efficiency of 96.5%. Dedicated PI controllers are designed for both the DAB and buck stages using their respective small-signal models to ensure tight regulation of the two LV buses. The overall system stability is verified through impedance-based analysis. Experimental validation using a DAB prototype integrated with a multi-phase buck converter confirms the accuracy of the DAB loss modeling used in the design optimization framework as well as the control design implemented for the cascaded converters. Full article

In modern financial markets, decision-makers increasingly rely on quantitative methods to navigate complex trade-offs among multiple, often conflicting objectives. This paper addresses constrained multi-objective optimization (MOO) with an application to portfolio optimization for minimizing risk and maximizing return. To this end, and to address existing gaps, we propose a novel reinforcement learning (RL)-guided non-dominated sorting genetic algorithm II (NSGA-II) enhanced with gray relational coefficients (GRC), termed RL-NSGA-II-GRC, which combines an RL agent controller and GRC-based selection to improve the convergence and diversity of the Pareto-optimal fronts. The agent adapts key evolutionary parameters online using population-level metrics of hypervolume, feasibility, and diversity, while the GRC-enhanced tournament operator ranks parents via a unified score simultaneously considering dominance rank, crowding distance, and geometric proximity to ideal reference. We evaluate the framework on the Kursawe and CONSTR benchmark problems and on a NASDAQ portfolio optimization application. On the benchmarks, RL-NSGA-II-GRC achieves convergence metric improvements of about 5.8% and 4.4% over the original NSGA-II, while preserving a well-distributed set of non-dominated solutions. In the portfolio application, the method produces a smooth and densely populated efficient frontier that supports the identification of the maximum Sharpe ratio portfolio (with annualized Sharpe ratio = 1.92), as well as utility-optimal portfolios for different risk-aversion levels. The main contributions of this work are three-fold: (1) we propose an RL-NSGA-II-GRC method that integrates an RL agent into the evolutionary framework to adaptively control key parameters using generational feedback; (2) we design a GRC-enhanced binary tournament selection operator that provides a comprehensive performance indicator to efficiently guide the search toward the Pareto-optimal front; (3) we demonstrate, on benchmark MOO problems and a NASDAQ portfolio case study, that the proposed method delivers improved convergence and well-populated efficient frontiers that support actionable investment insights. Full article

In the construction of grouting holes in high-mud-content layers, high-pressure jet cleaning technology effectively cuts and removes soil and sediments from the strata. This research designs the structure of a high-pressure jet cleaning device and establishes a numerical simulation model for the high-pressure jet cleaning nozzle, conducting orthogonal simulation tests. Based on the data from these tests, a Backpropagation (BP) Neural Network-based numerical prediction model for the high-pressure jet cleaning flow field is developed, enabling the prediction of cleaning flow rates and pressures for different nozzle channel structure parameters. Targeting jet fluid velocity and cleaning pressure, parametric shape optimization is performed on the nozzle channel structure: key parameters are identified via Analysis of Variance (ANOVA) and sensitivity analysis; an improved Non-dominated Sorting Genetic Algorithm II (NSGA-II) is adopted to establish a multi-objective optimization model, which exhibits superior convergence speed and solution diversity compared to the traditional algorithm. The optimal jet fluid velocity, cleaning pressure, and fluid structure parameter solution space for the high-pressure jet cleaning nozzle are obtained. Through simulation and experimental verification, it is found that with the same number of nozzles, the optimized design significantly enhances both the average cleaning flow rate and the cleaning pressure. Finally, a high-pressure jet cleaning nozzle and device are prototyped based on the simulation and optimization results and tested in the grouting test area A2W-2-III-6 of the South-to-North Water Diversion Project Xiong’an Storage Reservoir Project. This study provides a scientific basis and technical support for the application of high-pressure jet cleaning technology in complex geological formations. Full article

This paper proposes an efficiency-optimized multi-parameter collaborative non-dimensional selection method for industrial fans. Based on fan similarity theory, selection parameters are transformed into non-dimensional forms. The fan’s best working area (BWA) is defined according to stall margin, flow range, total pressure rise deviation, and minimum efficiency. The initial model selection uses the boundary equations of the defined BWA as screening criteria. Decision parameters comprise Euclidean distance, design point distance, pressure deviation, and current efficiency. These collectively form a multi-objective evaluation function. The NSGA-II algorithm determines the optimal weight distribution of decision parameters, generating a Pareto-optimal solution set. The initially selected models are subsequently subjected to secondary optimization through a comprehensive evaluation function. Selection case studies demonstrate that this method preliminarily screens 7 models that meet the target parameters from 400 candidate models. Secondary screening determines the model with the optimal efficiency and best comprehensive evaluation performance. The method effectively resolves the mismatch between fan model design points and target operational parameters in selection processes. This method integrates directly into selection software platforms and validation with 100 sets of fan selection parameters demonstrates that selected models achieve 99% accuracy. Achieving the secondary optimization function for fan model selection. Full article

Multi-objective optimization (MOO) plays a critical role in mechanical and industrial engineering, where conflicting design goals must be balanced under complex constraints. In this study, we introduce the Multi-Objective Giant Trevally Optimizer (MOGTO), a novel extension of the Giant Trevally Optimizer inspired by predatory foraging dynamics. MOGTO integrates predation-regime switching into a Pareto-based framework, enhanced with feasibility-aware archiving, knee-biased selection, and adaptive constraint handling. We benchmark MOGTO against established algorithms—NSGA-II, SPEA2, MOEA/D, and ParetoSearch—using synthetic test suites (ZDT1–3, DTLZ2) and classical engineering problems (welded beam, spring, and pressure vessel). Performance was assessed with Hypervolume (HV), Inverted Generational Distance (IGD), Spacing, and coverage metrics across 30 independent runs. The results demonstrate that MOGTO consistently achieves competitive or superior HV and IGD, maintains more uniform spacing, and generates larger feasible archives than the baselines. Particularly on constrained engineering problems, MOGTO yields more feasible non-dominated solutions, confirming its robustness and industrial applicability. These findings establish MOGTO as a reliable and general-purpose metaheuristic for multi-objective optimization in engineering design. Full article

The rapid growth of electric vehicle (EV) adoption poses significant challenges for the rational planning of charging infrastructure, where economic efficiency and service quality are inherently conflicting. To support scientific decision-making in charging station siting, this study proposes an integrated multi-objective optimization and decision-support framework that combines an improved Non-dominated Sorting Genetic Algorithm II (NSGA-II) with an entropy-weighted TOPSIS method. A bi-objective siting model is developed to simultaneously minimize total operator costs and maximize user satisfaction. User satisfaction is explicitly characterized by a nonlinear charging distance perception function and a queuing-theoretic waiting time model, enabling a more realistic representation of user service experience. To enhance convergence performance and solution diversity, the NSGA-II algorithm is improved through variable-wise random chaotic initialization, opposition-based learning, and adaptive crossover and mutation operators. The resulting Pareto-optimal solutions are further evaluated using an improved entropy-weighted TOPSIS approach to objectively identify representative compromise solutions. Simulation results demonstrate that the proposed framework achieves superior performance compared with the standard NSGA-II algorithm in terms of operating cost reduction, user satisfaction improvement, and multi-objective indicators, including hypervolume, inverted generational distance, and solution diversity. The findings confirm that the proposed NSGA-II–TOPSIS framework provides an effective, robust, and interpretable decision-support tool for EV charging station planning under conflicting objectives. Full article

As the number of feature dimensions increases, the decision-making space exhibits extensive and discrete characteristics, which poses a severe challenge to multi-objective (MO) evolutionary algorithms when searching for the optimal feature subset. Many existing algorithms face the difficulty of slow convergence speed and may fall into local optimal solutions. This study proposes AF-NSGA-II (an adaptive filtering-nondominated sorting genetic algorithm II), an improved MO evolutionary algorithm for high-dimensional feature selection, in which a novel sparse generation scheme for the solution set and an innovative adaptive crossover mechanism are introduced. This sparse initialization strategy, based on three distinct filter feature selection methods, produces initial solutions closer to the optimal Pareto solution set, which is beneficial for convergence. The adaptive crossover mechanism dynamically selects between geometric crossover operators (fostering convergence) and non-geometric crossover operators (enhancing diversity) based on parent similarity, effectively balancing both aspects and helping the algorithm to escape local optima. The algorithm is compared against six renowned multi-objective evolutionary algorithms across ten complex and publicly available datasets. The comparison results demonstrate the superiority of AF-NSGA-II over other algorithms, as well as its effectiveness in identifying the optimal feature subset. Full article

In order to study the impacts of the carbon-green certificate trading mechanism and the fluctuation of feed-in tariffs on the low-carbon and economic aspects of the investment and operation of the integrated energy system, and to transform the system carbon emission into a low-carbon economic indicator, a two-layer capacity optimization allocation model is established with the objectives of the investment, operation, and maintenance cost and the operation cost, respectively. For the source-load uncertainty, the scenario reduction theory based on Monte Carlo simulation and Wasserstein distance is used to obtain the per-unit value of wind and photovoltaic output, and the K-means clustering method is used to obtain the typical day of electric-heat-cold multi-energy load. Based on the geometric Brownian motion in finance to simulate the feed-in tariffs under different volatilities, the multidimensional analysis scenarios are constructed according to different combinations of carbon emission reduction policies and tariff volatilities. The model is solved using the non-dominated sorting genetic algorithm (NSGA-II) with mixed integer linear programming (MILP) method. Case study results show that under the optimal scenario considering policy interaction and price volatility (δ = 1.0), the total annual operating cost is reduced by approximately 17.9% (from 2.80 million CNY to 2.30 million CNY) compared to the baseline with no carbon policy. The levelized cost of the energy system reaches 0.2042 CNY/kWh, and carbon-green certificate trading synergies contribute about 70% of the operational cost reduction. The findings demonstrate that carbon reduction policies and electricity price volatility significantly affect system configuration and operational economy, providing a new perspective and decision-making basis for integrated energy system planning. Full article