Search Results (517)

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

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

To address the voltage instability in distribution networks with a high penetration of renewable energy, a multi-objective optimal scheduling method is proposed based on an enhanced static voltage stability margin ratio (SVSMR). The SVSMR_d index suitable for complex distribution networks is constructed by analytical derivation and equivalent impedance correction, and the distributed access characteristics of distributed power generation are considered. Based on the simulation analysis of the PS_CAD simulation platform, the effectiveness and engineering applicability of the SVSMR_d index are compared in the multi-energy station distribution network scenario, and the calculation results of SVSMRF and SDSCR are used to verify it. A multi-objective mixed-integer optimisation model is constructed, with the objective function encompassing electricity purchase cost, network loss cost, and energy storage revenue, and the lowest value of the SVSMR_d index of various new energy nodes is used as the optimisation object to carry out stability targets. Based on the epsilon constraint method, a Pareto frontier solution set is generated through example analysis, which has non-dominant characteristics. The results of the example analysis show that the proposed method can effectively reduce the operating cost, ensure the voltage stability margin of the system, and realise the collaborative optimisation of source–network–load–storage resources. This paper provides a new idea and method for the optimal operation of the distribution network, and optimises the distribution network under a high proportion of new energy access in the distribution network. Full article

Introduction: Extreme precipitation events in the Ayeyarwady River Basin, Myanmar, exhibit pronounced spatiotemporal heterogeneity and non-stationarity, yet their linkages to large-scale climate oscillations remain poorly understood. Objective: This study aimed to characterize distinct rainfall regimes, quantify non-stationary extreme event dynamics, and identify teleconnections with oceanic-atmospheric variability over 66 years (1958–2023). Materials and Methods: A hybrid analytical framework integrating K-means clustering, non-stationary Generalized Pareto Distribution modeling, and wavelet coherence analysis was applied to gridded monthly precipitation data from TerraClimate. Results: Four spatiotemporal rainfall clusters were delineated, exhibiting fundamentally different monsoonal characteristics with mean seasonal peaks ranging from 188 mm to 686 mm. Extreme precipitation behavior demonstrated substantial heterogeneity, with 100-year return periods varying from 501 mm in subdued northern zones to 983 mm in hyper-intense coastal regions. Wavelet coherence analysis revealed regime-specific teleconnections: Cluster 2 exhibited the strongest ENSO influence (0.536 coherence strength, 64-month median duration, 1960 peak), while Cluster 4 demonstrated unique IOD dominance (0.479 strength, 74-month duration) extending beyond annual timescales. Teleconnection effectiveness varied substantially across regimes (0.428–0.536 strength) with significant decadal non-stationarity. Limitations and Perspectives: Basin-wide precipitation averages obscure critical regional variations in extreme event magnitudes and climate forcing mechanisms, necessitating regime-differentiated approaches for flood risk assessment and climate-informed water resources management in Myanmar’s most vital river basin. 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

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

This paper introduces a novel approach for designing a Fractional Order Proportional-Integral-Derivative (FOPID) controller for the Hydraulic Turbine Regulating System (HTRS), aiming to overcome the challenge of tuning its five complex parameters ($K_p,K_i,K_d$, λ and μ). The design is formulated as a multi-objective optimization problem, minimized using the Multi-Objective Imperialist Competitive Algorithm (MOICA). The goal is to minimize two key transient performance metrics: the Integral of Squared Error (ISE) and the Integral of the Time Multiplied Squared Error (ITSE). MOICA efficiently generates a Pareto-front of non-dominated solutions, providing control system designers with diverse trade-off options. The resulting optimal FOPID controller demonstrated superior robustness when evaluated against simulated variations in key HTRS parameters (mg, eg and Tw). Comparative simulations against an optimally tuned integer-order PID and established literature methods (FOPID-GA, FOPID-MOPSO and FOPID-MOHHO) confirm the enhanced dynamic response and stable operation of the MOICA-based FOPID. The MOICA-tuned FOPID demonstrated superior performance for Setpoint Tracking, achieving up to a 26% faster settling speed (ITSE) and an 8% higher accuracy (ISE). Furthermore, for Disturbance Rejection, it showed enhanced robustness, leading to up to a 23% quicker recovery speed (ITSE) and an 18.9% greater error suppression (ISE). Full article

Plastic pollution in aquatic environments is a major ecological problem requiring scalable autonomous solutions for cleanup. This study addresses the coordination of multiple Autonomous Surface Vehicles by formulating the problem as a Partially Observable Markov Game and decoupling the mission into two tasks: exploration to maximize coverage and cleaning to collect trash. These tasks share navigation requirements but present conflicting goals, motivating a multi-objective learning approach. The proposed multi-agent deep reinforcement learning framework involves the utilisation of the same Multitask Deep Q-network shared by all the agents, with a convolutional backbone and two heads, one dedicated to exploration and the other to cleaning. Parameter sharing and egocentric state design leverages agent homogeneity and enable experience aggregation across tasks. An adaptive mechanism governs task switching, combining task-specific rewards with a weighted aggregation and selecting tasks via a reward-greedy strategy. This enables the construction of Pareto fronts capturing non-dominated solutions. The framework demonstrates improvements over fixed-phase approaches, improving hypervolume and uniformity metrics by 14% and 300%, respectively. It also adapts to diverse initial trash distributions, providing decision-makers with a portfolio of effective and adaptive strategies for autonomous plastic cleanup. 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

Growing cooling demand and environmental concerns motivate research into alternative technologies capable of converting low-grade heat into useful cooling. This study proposes a regression-assisted multi-objective optimisation framework using the Ant Lion Optimiser and its multi-objective variant to jointly maximise the coefficient of performance (COP), cooling capacity ($Q_{cc}$) and waste-heat recovery efficiency (${\eta }_e$). Pareto-optimal solutions exhibit a one-dimensional ridge in which ${\eta }_e$ declines, and COP and $Q_{cc}$ increase simultaneously. Within the explored bounds, non-dominated ranges span COP = 0.674–0.716, $Q_{cc}=$ 18.3–27.5 kW and ${\eta }_e=$ 0.118–0.127, with a practical compromise near COP ≈ 0.695, $Q_{cc}$ ≈ 24 kW and ${\eta }_{e }\approx$0.122–0.123. Compared to the typical reported COP band for single-stage silica-gel/water ADCs, the practical compromise solution (COP ≈ 0.695) offers a conservative COP improvement of approximately 16% when benchmarked against COP = 0.6, while the compromise $Q_{cc}$ ($Q_{cc}$ ≈ 24 kW) represents a conservative increase of approximately 20% relative to the upper product-class reference (20 kW). A one-at-a-time sensitivity analysis with re-optimisation identifies the hot- and chilled-water inlet temperatures and exchanger conductance as the dominant decision variables and maps diminishing-return regions. This framework can effectively utilise low-grade heat in future low-carbon buildings and processes, supporting the configuration of ADC systems. Full article

Electrical discharge machining (EDM) is widely used for machining hard and difficult-to-cut materials; however, the complex and nonlinear nature of the process makes the accurate prediction of key performance indicators challenging, particularly when only limited experimental data are available. In this study, a combined Taguchi design and Gaussian process regression (GPR) modeling framework is proposed to predict the surface roughness (Ra), material removal rate (MRR), and overcut (OC) in die-sinking EDM. An L18 Taguchi orthogonal array was employed to efficiently design experiments involving discharge current, pulse duration, and electrode material. GPR models with an automatic relevance determination (ARD) radial basis function kernel were developed to capture nonlinear relationships and varying parameter relevance. Model performance was evaluated using strict leave-one-out cross-validation (LOOCV). The developed GPR models achieved low prediction errors, with RMSE (MAE) values of 0.54 µm (0.41 µm) for R_a, 1.56 mm³/min (1.21 mm³/min) for MRR, and 0.0065 mm (0.0055 mm) for OC, corresponding to approximately 9.8%, 5.4%, and 5.9% of the respective response ranges. These results confirm stable and reliable predictive accuracy within the investigated parameter domain. Based on the validated surrogate models, multi-objective optimization was performed to identify Pareto-optimal process conditions, revealing graphite electrodes as the dominant choice within the feasible operating region. The proposed approach demonstrates that accurate and robust prediction of EDM performance can be achieved even with compact experimental datasets, providing a practical tool for process analysis and optimization. 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

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

In this work, we evaluate the impact of numerical integration methods and perturbation models on the computational speed and position accuracy of orbit propagation techniques. With increasing numbers of satellites in orbit, space traffic management may require near real-time satellite operations, for which computational speed may play a more important part in orbit propagation than positional accuracy. The aim of this work is to identify the most suitable propagation parameters for different mission scenarios and outline the perturbations to be considered based on the target orbit characteristics. We analyze the impact of the integrators’ tolerance on accuracy and runtime, as well as quantify the dominant perturbations for each orbit type. We use a Starlink satellite as a reference case, propagating it across multiple orbital regimes. The results are presented in the form of Pareto fronts trading off runtime and positional accuracy. These Pareto fronts outline some important results, for instance, how gravitational models beyond $32\times 32$ yield no accuracy improvements while significantly increasing runtime. We also verify that drag is critical in VLEO, LEO, SSO, and HEO (Molniya), while third-body effects play a major role in HEO (Molniya and Tundra), GEO, and GSO, and solar radiation pressure becomes significant in HEO (Tundra), GEO, and GSO. These results can be incorporated into collision avoidance optimization strategies for real-time satellite operations, thereby contributing to more efficient space traffic management. Full article

For the decoding of prefix-free codes such as Huffman code, we present a tradeoff between the decoding speed and memory requirements that results in an adapted decoding algorithm. Our decoding experiments on different codes demonstrate that the Pareto front of non-dominated solutions from different parameters comprises known solutions and solutions from our adapted algorithm. Each Pareto front comprises five solutions from our adapted algorithm next to five known solutions with a single table and one known solution with all possible tables. Compared to the fastest known solution with all possible tables, our adapted algorithm can achieve only 3.8% runtime overhead with only 66% of the memory requirements. Compared to a known solution with only one table, our adapted algorithm achieves up to 10% runtime improvement with three tables instead of one table. We conclude that our algorithm gives developers more and better choices to balance decoding speed and memory requirements. Full article