Search Results (1,161)

This paper compares two multi-objective optimization strategies for spiral-grooved dynamic gas thrust bearings. The first optimization is carried out using an analytical model, which is valid under the assumption of a high number of grooves. The second one is carried out by using a numerical model based on a finite difference (FD) technique, which is valid also in case of a limited number of grooves. The FD model was validated with data from the literature, then it was compared with the analytical model. The multi-objective optimization is based on a genetic algorithm and it is aimed at maximizing the load-carrying capacity (LCC) of the thrust bearing while minimizing its friction torque. It was found that the analytical model overestimates both the friction torque and the load capacity compared to the FD model, and that the Pareto front optimizations reveal almost identical trends in the optimized parameters. Full article

The increasing frequency of climate- and cyber-induced blackouts in modern distribution networks calls for restoration strategies that are both resilient and control-aware. Traditional black-start schemes, based on predefined energization sequences from synchronous machines, are inadequate for inverter-dominated grids characterized by high penetration of distributed energy resources and limited system inertia. This paper proposes a novel multi-layered black-start planning framework that explicitly incorporates the dynamic capabilities and operational constraints of grid-forming energy storage systems (GFESs). The approach formulates a multi-objective optimization problem solved via the Non-Dominated Sorting Genetic Algorithm III (NSGA-III), jointly minimizing total restoration time, voltage–frequency deviations, and maximizing early-stage load recovery. A graph-theoretic partitioning module identifies restoration subgrids based on topological cohesion, critical load density, and GFES proximity, enabling localized energization and autonomous island formation. Restoration path planning is embedded as a mixed-integer constraint layer, enforcing synchronization stability, surge current thresholds, voltage drop limits, and dispatch-dependent GFES constraints such as SoC evolution and droop-based frequency support. The model is evaluated on a modified IEEE 123-bus system with five distributed GFES units under multiple blackout scenarios. Simulation results show that the proposed method achieves up to 31% faster restoration and 46% higher voltage compliance compared to MILP and heuristic baselines, while maintaining strict adherence to dynamic safety constraints. The framework yields a diverse Pareto frontier of feasible restoration strategies and provides actionable insights into the coordination of distributed grid-forming resources for decentralized black-start planning. These results demonstrate that control-aware, partition-driven optimization is essential for scalable, safe, and fast restoration in the next generation of resilient power systems. Full article

To reduce the high computational cost and lengthy design cycles of traditional solid rocket motor (SRM) development, this paper proposes an efficient surrogate-assisted multi-objective optimization approach. A comprehensive performance model was first established, integrating internal ballistics, grain structural integrity, and cost estimation, to enable holistic assessment of the coupled effects of key motor components. A parametric analysis framework was then developed to automate the model, facilitating seamless data exchange and coordination among sub-models through chain coupling. Leveraging this framework, a large-scale, high-fidelity dataset was generated via uniform sampling of the design space. The Kriging surrogate model with the highest global fitting accuracy was subsequently employed to replicate the integrated model’s complex responses and reveal underlying design principles. Finally, an enhanced NSGA-III algorithm incorporating a phased hybrid crossover operator was applied to improve global search performance and guide solution evolution along the Pareto front. Applied to a specific SRM, the proposed method achieved a 4.72% increase in total impulse and a 6.73% reduction in cost compared with the initial design, while satisfying all constraints. Full article

Tool structure design methodologies predominantly rely on trial-and-error approaches or single-objective optimization but fail to achieve coordinated enhancement of multiple performance metrics while lacking thorough investigation into complex cutting coupling mechanisms. This study proposes a multi-objective optimization framework integrating joint simulation approaches. First, a finite element model for orthogonal turning was developed, incorporating the hyperbolic tangent (TANH) constitutive model and variable coefficient friction model. The cutting performance of four micro-groove configurations is comparatively analyzed. Subsequently, parametric modeling coupled with simulation–data interaction enables multi-objective optimization targeting minimized cutting force, reduced cutting temperature, and decreased wear rate. The Non-dominated Sorting Genetic Algorithm II (NSGA-II) explores Pareto-optimized solutions for arc micro-groove geometric parameters. Finally, optimized tools manufactured via powder metallurgy undergo experimental validation. The results demonstrate that the optimized tool achieves significant improvements: a 19.3% reduction in cutting force, a 14.2% decrease in cutting temperature, and tool life extended by 33.3% compared to baseline tools. Enhanced chip control is evidenced by an 11.4% reduction in chip curl radius, accompanied by diminished oxidation/adhesive wear and superior surface finish. This multi-objective optimization methodology effectively overcomes the constraints of conventional single-parameter optimization, substantially improving comprehensive tool performance while establishing a reference paradigm for cutting tool design under complex operational conditions. Full article

This study explores how the integration of generative artificial intelligence, multi-objective evolutionary optimization, and reinforcement learning can enable sustainable and cost-effective decision-making in supply chain strategy. Using real-world retail demand data enriched with synthetic sustainability attributes, we trained a Variational Autoencoder (VAE) to generate plausible future demand scenarios. These were used to seed a Non-Dominated Sorting Genetic Algorithm (NSGA-II) aimed at identifying Pareto-optimal sourcing strategies that balance delivery cost and CO₂ emissions. The resulting Pareto frontier revealed favorable trade-offs, enabling up to 50% emission reductions for only a 10–15% cost increase. We further deployed a deep Q-learning (DQN) agent to dynamically manage weekly shipments under a selected balanced strategy. The reinforcement learning policy achieved an additional 10% emission reduction by adaptively switching between green and conventional transport modes in response to demand and carbon pricing. Importantly, the agent also demonstrated resilience during simulated supply disruptions by rerouting decisions in real time. This research contributes a novel AI-based decision architecture that combines generative modeling, evolutionary search, and adaptive control to support sustainability in complex and uncertain supply chains. Full article

This study investigates the multi-objective path planning problem for unmanned surface vehicles (USVs), aiming to optimize both travel distance and energy consumption in maritime environments with obstacles, sea winds, and ocean currents. The proposed method accounts for practical constraints, including collision avoidance, kinematic boundaries, and speed limitations. The problem is formulated as a nonlinear multi-objective optimization model with generalized constraints and is solved using an improved particle swarm optimization algorithm enhanced by a vector-weighted fusion strategy. The algorithm adaptively balances exploration and exploitation to obtain diverse Pareto-optimal solutions. Simulation results under varying environmental conditions, along with real-world sea trials, validate the effectiveness of the proposed approach. The outcomes demonstrate that the method enables USVs to generate energy-efficient, smooth trajectories while maintaining robustness and adaptability, offering practical value for intelligent marine navigation. Full article

To enhance the overall operational efficiency of heavy haul railway port stations, which serve as critical hubs in rail–water intermodal transportation systems, this study develops a novel scheduling optimization method that integrates operation plans and resource allocation. By analyzing the operational processes of heavy haul trains and shunting operation modes within a hybrid unloading system, we establish an integrated scheduling optimization model. To solve the model efficiently, a dual-agent advantage actor–critic with Pareto reward shaping (DAA2C-PRS) algorithm framework is proposed, which captures the matching relationship between operations and resources through joint actions taken by the train agent and the shunting agent to depict the scheduling decision process. Convolutional neural networks (CNNs) are employed to extract features from a multi-channel matrix containing real-time scheduling data. Considering the objective function and resource allocation with capacity, we design knowledge-based composite dispatching rules. Regarding the communication among agents, a shared experience replay buffer and Pareto reward shaping mechanism are implemented to enhance the level of strategic collaboration and learning efficiency. Based on this algorithm framework, we conduct experimental verification at H port station, and the results demonstrate that the proposed algorithm exhibits a superior solution quality and convergence performance compared with other methods for all tested instances. Full article

This study investigates the asymmetric trade-off between cost and reliability in the optimal sizing of stand-alone Hybrid Renewable Energy Systems (HRESs) composed of photovoltaic panels (PV), wind turbines (WT), battery storage, a diesel generator (DG), and an inverter. The optimization is formulated as a multi-objective problem with Cost of Energy (CoE) and Loss of Power Supply Probability (LPSP) as conflicting objectives, highlighting that those small gains in reliability often require disproportionately higher costs. To ensure practical feasibility, the installation roof area limits both the number of PV panels, wind turbines, and batteries. Two metaheuristic algorithms—NSGA-II and MOPSO—are implemented in a Python-based framework with an Energy Management Strategy (EMS) to simulate operation under real-world load and resource profiles. Results show that MOPSO achieves the lowest CoE (0.159 USD/kWh) with moderate reliability (LPSP = 0.06), while NSGA-II attains a near-perfect reliability (LPSP = 0.0008) at a slightly higher cost (0.179 USD/kWh). Hypervolume (HV) analysis reveals that NSGA-II offers a more diverse Pareto front (HV = 0.04350 vs. 0.04336), demonstrating that explicitly accounting for asymmetric sensitivity between cost and reliability enhances the HRES design and that advanced optimization methods—particularly NSGA-II—can improve decision-making by revealing a wider range of viable trade-offs in complex energy systems. Full article

Given the fast-evolving context of electrical vertical takeoff and landing vehicles (eVTOL) based on the concept of tiltrotor aircraft, this work describes a framework aimed at the preliminary aerodynamic design and optimization of innovative lifting surfaces of such rotorcraft vehicles. In particular, a multiobjective optimization process was applied to the design of a wing extension representing an innovative feature recently investigated to improve the aerodynamic performance of a tiltrotor aircraft wing. The wing/proprotor configurations, selected using a Design Of Experiment (DOE) approach, were simulated by the mid-fidelity aerodynamic code DUST, which used a vortex-particle method (VPM) approach to model the wing/rotor wakes. A linear regression model accounting for nonlinear interactions was used by an evolutionary algorithm within a multiobjective optimization framework, which provided a set of Pareto-optimal solutions for the wing extension, maximizing both wing and rotor efficiency. Moreover, the present work highlighted how the use of a fast and reliable numerical modeling for aerodynamics, such as the VPM approach, enhanced the capabilities of an optimization framework aimed at achieving a more accurate preliminary design of innovative features for rotorcraft configurations while taking into account the effects of the aerodynamic interaction between wings and proprotors. Full article

A pervasive cognitive asymmetry exists between designers and users, and contemporary furniture form design often struggles to accommodate and balance multi-dimensional user affective requirements. To address these challenges, this study proposes an intelligent optimization design method for furniture form that enhances the universality of user research and the balance of design decision-making. First, representative URs are extracted from online user review texts collected through web crawling. These URs are then classified into three-dimensional quality attributes using the refined Kano’s model, thereby identifying the key URs. Second, a decomposition table of furniture design characteristics (DCs) is constructed. Third, the multi-objective red-billed blue magpie optimizer (MORBMO) is employed to automatically generate a Pareto solution set that satisfies the multi-dimensional key URs, from which the final optimal solution is determined. The proposed method improves the objectivity and granularity of user research, assists furniture enterprises in prioritizing product development, and enhances user satisfaction across multiple affective dimensions. Furthermore, it provides enterprises with flexible choices among diverse alternatives, thereby mitigating the asymmetry inherent in furniture form design. Full article

This study aims to address the challenge of balancing historical preservation and sustainable material selection in ancient building renovations, particularly in regions with unique climatic conditions like Hunan Province. The research proposes a hybrid model integrating Genetic Algorithm-optimized Extreme Gradient Boosting (GA-XGBoost) and Long Short-Term Memory (LSTM) networks. The GA-XGBoost component optimizes hyperparameters to predict material performance, while the LSTM network captures temporal dependencies in environmental and material degradation data. A multi-objective optimization framework is developed to simultaneously prioritize preservation integrity and green performance. The methodology is validated through a case study on an ancient architectural complex in Rucheng, Hunan Province. Key results demonstrate that the hybrid model achieves superior accuracy in material selection, with an 18–23% reduction in embodied energy (compared to conventional AHP-TOPSIS methods) and a 21.9% improvement in prediction accuracy (versus standalone XGBoost with default hyperparameters). A multi-objective optimization framework is developed to simultaneously prioritize preservation integrity and green performance, with Pareto-optimal solutions identifying material combinations that balance historical authenticity (achieving 92% substrate compatibility) with substantial sustainability gains (18–23% embodied energy reduction). The model also identifies optimal material combinations, such as lime-pozzolan mortars with rice husk ash additives, which enhance moisture buffering capacity by 28% (relative to traditional lime mortar benchmarks) while maintaining 92% compatibility with original substrates (based on ASTM C270 compatibility tests). The findings highlight the model’s effectiveness in bridging heritage conservation and modern sustainability requirements. The study contributes a scalable and interpretable framework for green material selection, offering practical implications for cultural heritage projects worldwide. Future research directions include expanding the model’s applicability to other climate zones and integrating circular economy principles for broader sustainability impact. Preliminary analysis indicates the framework’s adaptability to other climate zones through adjustment of key material property weightings. Full article

Wireless sensors serve as a critical means of information perception and collection, profoundly influencing human life and production. In order to optimize the problem of excessive energy drain caused by the selection of cluster heads and the transmission of paths in the network, this study proposes an energy-efficient clustering–routing algorithm that combines K-means++ initialization with the multi-objective Chaotic Mapping Walrus Optimization Algorithm (CM-WaOA). The CM-WaOA employs chaotic mapping and Pareto front optimization to balance node residual energy, cluster-head-to-base-station distance, inter-cluster-head distance, and intra-cluster node count variance when selecting cluster heads. Subsequently, the Sparrow Search Algorithm (SSA) refines routing paths through adaptive population sizing and elite retention, thereby reducing transmission path loss. The simulation results over 1000 rounds demonstrate that the CM-WaOA surpasses LEACH, EEUC, CGWOA, and EBPT-CRA in terms of energy drain, node survival, and latency; it achieves the highest average residual energy, the fewest dead nodes, the most surviving nodes, and the shortest network delay. These findings confirm that the CM-WaOA can still maintain good energy utilization and low-latency characteristics under different sensor densities, effectively extending the network lifetime. Full article

Magnetic components serve as critical energy conversion elements in power conversion systems, with their performance directly determining overall system efficiency and long-term operational reliability. The development of accurate core loss frameworks and multi-objective optimization strategies has emerged as a pivotal technical bottleneck in power electronics research. This study develops an integrated framework combining physics-informed modeling and multi-objective optimization. Key findings include the following: (1) a square-root temperature correction model (exponent = 0.5) derived via nonlinear least squares outperforms six alternatives for Steinmetz equation enhancement; (2) a hybrid Bi-LSTM-Bayes-ISE model achieves industry-leading predictive accuracy (R² = 96.22%) through Bayesian hyperparameter optimization; and (3) coupled with NSGA-II, the framework optimizes core loss minimization and magnetic energy transmission, yielding Pareto-optimal solutions. Eight decision-making strategies are compared to refine trade-offs, while a crow search algorithm (CSA) improves NSGA-II’s initial population diversity. UFM, as the optimal decision strategy, achieves minimal core loss (659,555 W/m³) and maximal energy transmission (41,201.9 T·Hz) under 90 °C, 489.7 kHz, and 0.0841 T conditions. Experimental results validate the approach’s superiority in balancing performance and multi-objective efficiency under thermal variations. Full article

The growing need for cooling, combined with the environmental concerns surrounding conventional mechanical vapour compression (MVC) systems, has accelerated research for sustainable cooling solutions driven by low-grade heat. Single-stage dual-bed adsorption chillers (ADCs) using silica gel and water provide a promising approach due to their continuous cooling output, lower complexity, and the use of environmentally safe working fluids. However, limitations in their performance, specifically in the coefficient of performance (COP), cooling capacity (${\mathrm{Q}}_{\mathrm{c}\mathrm{c}}$), and waste heat recovery efficiency (${\mathsf{\eta }}_{\mathrm{e}}$), necessitate improvement through optimization. This study employs statistically validated regression-based objective functions to optimize ten decision variables using the single Grey Wolf Optimizer (GWO) and its multi-objective variant, Muilti-Objective Grey Wolf Optimization (MOGWO), for a silica gel–water single-stage dual-bed ADC. The results from the single-objective optimization showed a maximum coefficient of performance (COP) of 0.697, cooling capacity (${\mathrm{Q}}_{\mathrm{c}\mathrm{c}}$) of 20.76 kW, and waste heat recovery efficiency (ηe) of 0.125. The values from the Pareto-optimal solutions for the MOGWO ranged from 0.5123 to 0.6859 for COP, 12.45 to 20.73 kW for ${\mathrm{Q}}_{\mathrm{c}\mathrm{c}}$ and 8.24% to 12.48% for ${\mathsf{\eta }}_{\mathrm{e}}$, demonstrating superior performance compared to existing benchmarks. A one-at-a-time sensitivity analysis revealed non-linear and non-monotonic impacts of variables, confirming the robustness and physical realism of the MOGWO model. The developed MOGWO framework effectively enhances the performance of the single-stage dual-bed ADC and improves low-grade heat utilization, offering a robust decision-support tool for system design and optimization. Full article

This paper presents a novel approach to address the challenges of complex terrain, dynamic wind fields, and multi-objective constraints in multi-UAV collaborative path planning for forest firefighting missions. An extensible algorithm, termed Parallel Vectorized Differential Evolution-based Multi-Objective Jellyfish Search (PVDE-MOJS), is proposed to enhance path planning performance. A comprehensive multi-objective cost function is formulated, incorporating path length, threat avoidance, altitude constraints, path smoothness, and wind effects. Forest-specific constraints are modeled using cylindrical threat zones and segmented wind fields. The conventional jellyfish search algorithm is then enhanced through multi-core parallel fitness evaluation, vectorized non-dominated sorting, and differential evolution-based mutation. These improvements substantially boost convergence efficiency and solution quality in high-dimensional optimization scenarios. Simulation results on the Phillip Archipelago Forest Farm digital elevation model (DEM) in Australia demonstrate that PVDE-MOJS outperforms the original MOJS algorithm in terms of inverted generational distance (IGD) across benchmark functions UF1–UF10. The proposed method achieves effective obstacle avoidance, altitude optimization, and wind adaptation, producing uniformly distributed Pareto fronts. This work offers a viable solution for emergency UAV path planning in forest fire rescue scenarios, with future extensions aimed at dynamic environments and large-scale UAV swarms. Full article