Search Results (713)

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

The optimization of low-Reynolds-number airfoils for micro unmanned aerial vehicles (UAVs) is challenging due to strong geometric nonlinearities, tight endurance requirements, and the need to maintain performance across multiple operating conditions. Classical surrogate-assisted optimization (SAO) methods combined with genetic algorithms become increasingly expensive and less reliable when class–shape transformation (CST)-based geometries are coupled with several flight conditions. Although deep learning surrogates have higher expressive power, their use in this context is often limited by insufficient local feature extraction, weak adaptation to changes in operating conditions, and a lack of robustness analysis. In this study, we construct a task-specific convolutional neural network–long short-term memory (CNN–LSTM) surrogate that jointly predicts the power factor, lift, and drag coefficients at three representative operating conditions (cruise, forward flight, and maneuver) for the same CST-parameterized airfoil and integrate it into an Non-dominated Sorting Genetic Algorithm II (NSGA-II)-based three-objective optimization framework. The CNN encoder captures local geometric sensitivities, while the LSTM aggregates dependencies across operating conditions, forming a compact encoder–aggregator tailored to low-Re micro-UAV design. Trained on a computational fluid dynamics (CFD) dataset from a validated SD7032-based pipeline, the proposed surrogate achieves substantially lower prediction errors than several fully connected and single-condition baselines and maintains more favorable error distributions on CST-family parameter-range extrapolation samples (±40%, geometry-valid) under the same CFD setup, while being about three orders of magnitude faster than conventional CFD during inference. When embedded in NSGA-II under thickness and pitching-moment constraints, the surrogate enables efficient exploration of the design space and yields an optimized airfoil that simultaneously improves power factor, reduces drag, and increases lift compared with the baseline SD7032. This work therefore contributes a three-condition surrogate–optimizer workflow and physically interpretable low-Re micro-UAV design insights, rather than introducing a new generic learning or optimization algorithm. 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

Under the “dual carbon” goals, enhancing the energy supply for communication base stations is crucial for energy conservation and emission reduction. An individual base station with wind/photovoltaic (PV)/storage system exhibits limited scalability, resulting in poor economy and reliability. To address this, a collaborative power supply scheme for communication base station group is proposed. This paper establishes a capacity optimization configuration model for such integrated system and introduces a hybrid solution methodology combining random scenario analysis, Nondominated Sorting Genetic Algorithm II (NSGA-II), and Generalized Power Mean (GPM). Typical scenarios are solved using NSGA-II to generate a candidate solution set, which is then refined under operational constraints. The GPM method is applied to determine the final configuration by accounting for attribute correlations. A case study on a Chinese base station group, considering uncertainties in renewable generation, demonstrates the feasibility and effectiveness of the proposed approach. Full article

To mitigate the impact of the conductor’s inherent tension–torsion coupling effect on conductor quality during tension stringing, a method for tension–torsion analysis and structural parameter optimization of conductors is proposed based on the radial basis function neural network (RBFNN) surrogate model. The layer-wise lay ratios of conductors are selected as the structural parameters. Using the tension–torsion coupling computational method for conductors, the layer-wise lay ratios are sampled by Latin hypercube sampling (LHS) to construct the sample data by computing conductor torque under different combinations. The RBFNN surrogate model is trained with the data, and its shape parameter is optimized through Leave-One-Out Cross-Validation (LOOCV), achieving a coefficient of determination R² close to 1 with minimal errors. Targeting torque minimization, the Non-Dominated Sorting Genetic Algorithm II (NSGA-II) is employed to identify the optimal combination of conductor lay ratio parameters, reducing conductor torque by approximately 18% under the same axial tension. For practical applications, prioritize the optimal combination for JL/G1A-630/45-45/7 and analogous conductors, and adopt the RBFNN model for rapid torque prediction. The proposed method also serves as a reference for design optimization of conductor structural parameters. Full article

Isolated microgrids with green hydrogen storage offer a promising solution for supplying electricity to remote communities where conventional grid expansion is infeasible. Designing such systems requires balancing two conflicting objectives: minimizing installation and operation costs while maximizing supply reliability. This paper proposes a multi-objective optimization methodology, based on the Non-dominated Sorting Genetic Algorithm II, to determine the optimal sizing of multiple microgrid components. This sizing explicitly addresses both the power capacities (kW) (for photovoltaic panels, wind turbines, electrolyzers, and fuel cells) and the energy storage capacities (kWh and kg) (for batteries and hydrogen tanks, respectively), aiming to generate Pareto-optimal solutions that explore this trade-off. The proposed method evaluates the trade-off by minimizing two objectives: the Net Present Value, which includes investment, replacement, and maintenance costs, and the total expected interruption hours, derived from an hourly energy balance analysis. The methodology’s effectiveness is validated using four distinct case studies. Three of these are based on real locations with specific load profiles and climate data. To test the method’s robustness, a fourth case study uses a fictitious load profile, designed with pronounced seasonal variations and a clear distinction between weekday and weekend consumption. Our results demonstrate the method’s ability to identify efficient hybrid renewable topologies combining photovoltaic and/or wind generation, batteries, and hydrogen systems (electrolyzer, storage tank, and fuel cell). The obtained cost–reliability curves provide practical decision-support tools for system planners. Full article

This study develops a fuzzy linear multi-objective programming (FLMOP) model to optimize Taiwan’s online food delivery (OFD) systems by jointly considering time, cost, quality, and carbon emissions (TCQCE) under strict Hazard Analysis and Critical Control Point (HACCP) safety constraints. By integrating fuzzy set theory with triangular fuzzy numbers (TFN) and employing centroid defuzzification, this model effectively addresses uncertainties in delivery time, cost, and quality. Empirical results demonstrate that controlled delivery-time extension and order batching reduce carbon emissions by 20%, maintain food quality at 89.3%, and lower delivery costs by 15% under large-scale operations. Statistical validation (p = 0.002) and sensitivity analysis confirm robustness and low variability. Comparative benchmarking highlights FLMOP’s superiority over mixed-integer linear programming (MILP) and genetic algorithms/non-dominated sorting genetic algorithm II (GA/NSGA-II), achieving higher hypervolume (0.904 vs. 0.836 and 0.743) and near-optimal solutions within 11 s, making it suitable for real-time decision-making. This study establishes a benchmark for sustainable last-mile OFD and offers practical guidelines for Taiwan’s OFD platforms. Full article

Drones have emerged as critical infrastructure for enhancing logistics efficiency in the emerging low-altitude economy, particularly in collaborative vehicle–drone research. However, existing research often neglects the impact of fair task allocation on workload balance among formations in large-scale routing scenarios, which compromises service quality. To address this gap, we introduce the Multi-vehicle with drones Collaborative Routing Problem with Large-scale Packages (MCRPLP), formulated as a bi-objective model aiming to minimize both operational cost and workload imbalance. A Hybrid Strategy-assisted Multi-objective Optimization Algorithm (HSMOA) is developed to overcome the limitations of existing methods, which struggle with balancing solution quality and computational efficiency in solving large-scale routing. Based on a Non-dominated Sorting Genetic Algorithm (NSGA-II), the HSMOA integrates a heuristic task assignment strategy that greedily reassigns packages between adjacent clusters. Then, by integrating a Pareto-front superiority evaluation model, an elite individual supplement strategy is designed to dynamically prune sub-optimal solution subspaces while enhancing the search within high-quality Pareto-front subspaces in HSMOA. Extensive experiments demonstrate the effectiveness of HSMOA in terms of solution quality and computational efficiency compared to multiple state-of-the-art methods. Further sensitivity analysis and managerial insights derived from a real-world case are also provided to support practical logistics implementation. Full article

The proposed work introduces a sizing algorithm to achieve a desired linear transconductance in the optimization of operational transconductance amplifiers (OTAs) by applying the $g_m/I_D$ method to find the initial width (W) and length (L) sizes of the transistors. These size values are used to run the non-dominated sorting genetic algorithm (NSGA-II) to perform a multi-objective optimization of three OTA topologies. The $g_m/I_D$ method begins with transistor characterization using MATLAB R2024a generated look-up tables (LUTs), which map normalized transconductance of the transistor channel dimensions, and key performance metrics of a complementary metal–oxide–semiconductor (CMOS) technology. The LUTs guide the initial population generation within NSGA-II during the optimization of OTAs to achieve not only a desired transconductance but also accuracy alongside linearity, high DC gain, low power consumption, and stability. The feasible W/L size solutions provided by NSGA-II are used to enhance the CMOS design of a memristor emulator, where the OTA with the desired transconductance is adapted to tune the behavior of the memristor, demonstrating improved pinched hysteresis loop characteristics. In addition, process, voltage and temperature (PVT) variations are performed by using TSMC 180 nm CMOS technology. The memristor-based on optimized OTAs is used to design a Leaky Integrate-and-Fire (LIF) neuron, which produces identical spike counts (seven spikes) under the same input conditions, though the time period varied with a CMOS inverter scaling. It is shown that increasing transistor widths by 100 in the inverter stage, the spike quantity is altered while changing the spiking period. This highlights the role of device sizing in modulating LIF neuron dynamics, and in addition, these findings provide valuable insights for energy-efficient neuromorphic hardware design. Full article

Open-channel irrigation systems often face constraints due to water supply uncertainty and insufficient gate control precision. This study proposes an integrated framework for canal water allocation and gate control that combines interval-based uncertainty analysis with intelligent optimization to address these challenges. First, we predict the inflow process using an Auto-Regressive Integrated Moving Average (ARIMA) model and quantify the range of water supply uncertainty through Maximum Likelihood Estimation (MLE). Based on these results, we formulate a bi-objective optimization model to minimize both main canal flow fluctuations and canal network seepage losses. We solve the model using the Non-dominated Sorting Genetic Algorithm II (NSGA-II) to obtain Pareto-optimal water allocation schemes under uncertain inflow conditions. This study also designs a Fuzzy Proportional–Integral–Derivative (Fuzzy PID) controller. We adaptively tune its parameters using the Particle Swarm Optimization (PSO) algorithm, which enhances the dynamic response and operational stability of open-channel gate control. We apply this framework to the Chahayang irrigation district. The results show that total canal seepage decreases by 1.21 × 10⁷ m³, accounting for 3.9% of the district’s annual water supply, and the irrigation cycle is shortened from 45 days to 40.54 days, improving efficiency by 9.91%. Compared with conventional PID control, the PSO-optimized Fuzzy PID controller reduces overshoot by 4.84%, and shortens regulation time by 39.51%. These findings indicate that the proposed method can significantly improve irrigation water allocation efficiency and gate control performance under uncertain and variable water supply conditions. Full article

In hardware/software (HW/SW) partitioning, the most commonly established objectives are execution time, power consumption, and hardware area. Surprisingly, memory usage, a critical resource in embedded systems, has received limited attention as a primary optimization objective. Moreover, the few studies that consider memory rarely provide an explicit, design-time estimation method. This work proposes a methodology for obtaining memory usage as a design metric, along with an objective function tailored to evaluate memory usage in systems-on-chip featuring a hard processor core and a Field-Programmable Gate Array suitable for a HW/SW partitioning problem. To validate the proposed methodology, HW/SW partitioning was carried out for a PD-type fuzzy control algorithm targeting a DC motor. The optimization problem was solved using the Non-dominated Sorting Genetic Algorithm II. The results demonstrate the feasibility and accuracy of the proposed approach, achieving more than 97.5% accuracy in predicting memory and hardware resource consumption. Additionally, the functional performance of the selected partition configuration was validated in real-time, where the tracking of different reference signals for the velocity of the motor was successfully achieved. Full article

In U-shaped automated container terminals (U-shaped ACTs), automated guided vehicles (AGVs) need to frequently interact with yard cranes (YCs), and separate scheduling of the two devices will affect terminal efficiency. Therefore, this study explores the coordinated scheduling problem between the two devices. To solve this problem, a high-precision simulation model of the U-shaped ACTs is established, which incorporates real operational logic. Second, an Improved Non-dominated Sorting Genetic Algorithm II based on Proximal Policy Optimization (INSGAII-PPO) is proposed. The algorithm uses PPO to realize dynamic genetic operator selection and makes related improvements, which improve the multi-objective optimization ability of NSGAII, and solve the collaborative scheduling problem by combining simulation. Finally, a hybrid weighted Technique for Order Preference by Similarity to Ideal Solution with preferences is proposed to select the final solution. The experimental results show that the scheme obtained by INSGAII-PPO exhibits better convergence and diversity, and offers significant advantages compared with the comparison algorithms. Moreover, the energy consumption and waiting time of the final solution selected by the proposed method are reduced by 3.42% and 4.87% on average. The proposed method has the capability of providing a theoretical reference for the AGVs and YCs collaborative scheduling of U-shaped ACTs. Full article