Search Results (815)

In post-disaster relief operations, emergency fleets typically consist of vehicles with varying load capacities, travel speeds, and operating costs. These heterogeneous vehicles are prone to unexpected breakdowns during delivery, which can severely disrupt supply chains and delay urgent aid. Existing scheduling approaches, however, rarely account for fleet heterogeneity, real-time breakdowns, and the trade-off between delivery speed and cost within a unified framework. This paper addresses this gap by formulating the dynamic scheduling of heterogeneous emergency fleets as a two-stage mixed-integer programming model, where total transportation time and cost are simultaneously minimized. The key algorithmic contribution is a fuzzy robust adaptive multi-objective hybrid algorithm (FR-AMOHA) with three interconnected design components. First, a fuzzy evaluation-based pre-matching strategy uses entropy-weighted multi-criteria assessment to generate high-quality initial solutions. Second, a failure-resilient rescheduling module freezes system state upon breakdown detection and selects recovery plans via multi-dimensional resilience scoring to prevent cascading failures. Third, a Pareto-guided adaptive neighborhood search dynamically adjusts operator selection to balance time and cost optimization. Tests on 40 real-world instances with 50 to 1000 demand nodes show that FR-AMOHA achieves optimal inverted generational distance values on 17 out of 40 instances, improves hypervolume by 15% to 35% on average compared with other metaheuristics, and keeps computation times between 40 and 250 s, which is within acceptable limits for emergency decision-making. FR-AMOHA outperforms Gurobi and six leading metaheuristics in solution quality with comparable computational cost. Full article

To address the multi-objective optimization of stamping process parameters for vehicle components, a hybrid framework coupling Dynaform simulations, Orthogonal Experimental Design, BPNN, NSGA-II, and TOPSIS was proposed. Taking car door accessories as a case study, OED was utilized to investigate the interacting effects of blank holder force, friction coefficient, stamping speed, and die clearance on the maximum thinning and thickening rates. The simulated data trained a BPNN to construct the highly non-linear mapping relationships. Subsequently, the NSGA-II algorithm generated the Pareto optimal frontier, and TOPSIS objectively selected the best compromise process parameters: a blank holder force of 43,113 N, a friction coefficient of 0.13, a stamping speed of 2496 mm/s, and a die clearance of 1.0 mm. Applying this combination effectively constrained the maximum thinning and thickening rates to 41.4% and 16.8% respectively. The BPNN relative prediction errors against numerical validation were merely 0.0691% and 6.9930%, fully verifying the high fidelity and effectiveness of the proposed optimization methodology. Full article

Battery management systems (BMSs) in electric vehicles (EVs) are instrumented with an increasing number of heterogeneous sensors, many of which contribute redundant or noisy measurements that increase computational cost without improving diagnostic accuracy. This paper proposes a Binary Hybrid Particle Whale Optimization Algorithm (BHPWOA) for multi-objective feature selection targeting three-class BMS fault diagnosis: OK, Warning, and Critical. The method is evaluated using an 18-feature EV charging dataset with $n=500$ samples. BHPWOA encodes candidate feature subsets as binary masks in a continuous $\left[\mathrm{0,1}\right]$ position space. It executes a Binary Particle Swarm Optimization (BPSO) phase during the first 50 iterations to rapidly identify a promising subset region, then transfers the global-best mask as the Whale Optimization Algorithm (WOA) leader for the remaining 50 iterations of bubble-net exploitation. A multi-objective fitness function simultaneously penalises classifier error and subset size, directly optimising the accuracy–cost trade-off. BHPWOA selects four features out of 18, corresponding to a $77.8\mathrm{\%}$ reduction, and achieves accuracy $=0.710$ and macro-F1 $=0.4455$ on the held-out test set. It outperforms all-feature KNN $\left(F_1=0.2997\right)$, standalone BPSO with six selected features $\left(F_1=0.4603\right)$, BWOA with two selected features $\left(F_1=0.4026\right)$, and BSFSA with five selected features $\left(F_1=0.4216\right)$ on the Pareto-dominant combined fitness objective. The selected subset $\left\{{\mathrm{C}\mathrm{e}\mathrm{l}\mathrm{l}\mathrm{V}\mathrm{o}\mathrm{l}\mathrm{t}\mathrm{a}\mathrm{g}\mathrm{e}}_V,{\mathrm{C}\mathrm{h}\mathrm{a}\mathrm{r}\mathrm{g}\mathrm{e}\mathrm{C}\mathrm{u}\mathrm{r}\mathrm{r}\mathrm{e}\mathrm{n}\mathrm{t}}_A,{\mathrm{S}\mathrm{O}\mathrm{C}}_{\mathrm{\%}},{\mathrm{C}\mathrm{h}\mathrm{a}\mathrm{r}\mathrm{g}\mathrm{e}\mathrm{P}\mathrm{o}\mathrm{w}\mathrm{e}\mathrm{r}}_{kW}\right\}$ achieves the best fitness score of $-0.5555$, enabling a $77.8\mathrm{\%}$ sensor-cost reduction while improving fault detection. Stability analysis across five independent random seeds confirms a mean feature count of $4.0\pm 0.7$ and a mean macro-F1 of $0.441\pm 0.021$, demonstrating algorithmic robustness. Full article

In-Mold Electronics (IMEs) present a highly promising monolithic integration strategy for manufacturing miniaturized 3D MEMS optical tweezers, offering exceptional environmental adaptability and structural compactness. However, the precision of such optical systems is heavily constrained by the injection molding process. During the molding phase, high-pressure melt scouring and severe thermo-mechanical coupling frequently induce geometric misalignment, manifesting as node displacement, localized warpage, and residual stress accumulation in the embedded circuits. This displacement critically alters the cross-sectional area of conductive traces, leading to resistance fluctuations that can destabilize the driving current. According to American Wire Gauge (AWG) standards, ensuring the geometric fidelity of this sensor-CPU interconnect pathway is fundamental to maintaining signal integrity. To address these manufacturing bottlenecks, this study systematically investigates the process stability of IME circuits Cyclic Olefin Copolymer (COC) is strategically selected as the substrate material over Polycarbonate (PC) and Liquid Silicone Rubber (LSR) due to its ultra-high light transmittance, extremely low water absorption, and superior thermomechanical stability. Based on finite element simulation, a data-driven intelligent optimization framework is developed. Latin Hypercube Sampling (LHS) is first utilized to efficiently sample the multi-dimensional process space, comprising melt temperature, packing pressure, and packing time. To handle the non-stationary nature of process feedback signals, wavelet analysis is introduced to decouple high-frequency noise, extracting Wavelet Energy Entropy (WEE) as a highly robust dynamic metric for process stability. Subsequently, a hybrid NSGA-II-MOPSO multi-objective algorithm is deployed to cooperatively optimize the injection parameters. The simulation-based optimization results demonstrate a substantial enhancement in manufacturing precision. Under the optimal parameter configuration, the average node displacement of the embedded circuits decreases significantly from 0.034 mm to 0.014 mm, achieving a 58.82% reduction. Simultaneously, volumetric shrinkage drops from 5.755% to 4.832% (a 16.04% reduction), while residual stress is maintained well within the structural safety threshold of optical-grade polymers. By clarifying the deformation control mechanism during the manufacturing phase, this study provides a highly reliable, data-driven methodological framework for the precision mass production of micro-nano optical systems. Full article

In this study, a sustainable advanced manufacturing technology was developed using a holistic approach for finishing heat-treated 42CrMo4 steel cylindrical parts. The proposed technology is based on a hybrid combined process (HCP) involving cool-assisted dry hard turning and subsequent cool-assisted dry diamond burnishing (DB). A cold-air cooling (without lubrication) condition was achieved using a special device with a cold-air nozzle based on the principle of vortex tubes. The study was conducted in two stages. In the first stage, only the hard turning process was investigated using variance analysis to determine the significant governing factors (feed rate and cutting insert radius). The second stage involved studying and optimising the HCP. This approach incorporated the two significant turning process factors, along with three additional DB process factors: the radius of the diamond insert, burnishing force and feed rate. The selected objective functions were the average roughness, skewness, kurtosis, surface microhardness, residual surface axial stress and fatigue limit. The fatigue limit was determined using the accelerated Locati method. Mathematical models of the objective functions were obtained using experiments and regression analyses. Using multi-objective optimisation, the HCP was optimised based on two criteria: (1) maximum wear resistance under boundary lubrication conditions and (2) maximum fatigue limit. The optimisation tasks were solved by searching for the Pareto optimal solution approach using QStatLab and the NSGA II algorithm. The compromise optimal values of the governing factors, maximising the fatigue limit (690 MPa), are as follows: feed rate in turning and DB of 0.05 mm/rev, radius of the cutting insert of 0.8 mm, diamond insert radius of 2 mm, and burnishing force of 50 N. Experimental verification showed a good agreement with the optimised solutions for surface integrity and fatigue limit characteristics. Full article

Surface congestion and taxiing delays at high-density airports increasingly constrain aviation sustainability, as ground-phase fuel consumption and emissions constitute a significant share of total airport emissions. Existing studies typically decouple air traffic flow management from ground resource scheduling, hindering coordinated optimization of punctuality, environmental benefits, and resource utilization. This paper proposes a multi-objective optimization method for runway-taxiway systems oriented toward air–ground collaborative decision-making, integrating Calculated Take-Off Time (CTOT) compliance constraints. A tri-objective mixed-integer programming model is formulated to minimize CTOT deviation, total taxiing time, and runway workload imbalance. A hybrid intelligent algorithm, SSA-SCA-NSGA-II, is designed with a bidirectional elite feedback mechanism to address this NP-hard problem. Validation uses real operational data of 58 departure flights during a peak period at Beijing Daxing International Airport. The results demonstrate that the proposed method achieves effective trade-offs on the Pareto front: CTOT compliance rate increased from 77.6% to 89.7–96.6%; total taxiing time decreased from 692 min to 551–635 min; and dual-runway utilization imbalance declined from 5.2% to 1.7–3.8%. These improvements translate into quantifiable sustainability gains: fuel consumption is reduced by 1425–3525 kg and CO₂ emissions by 4503–11,139 kg per peak hour, alongside a 19-percentage point improvement in punctuality that lowers passenger delay costs and reduces controller coordination workload. By simultaneously advancing environmental sustainability (carbon footprint reduction), economic sustainability (fuel and operational cost savings), and social sustainability (service punctuality and labor efficiency), the framework provides a measurable, monitorable, and policy-relevant decision-support tool for green airport surface operations aligned with sustainable development goals (SDGs). Full article

Using low-Earth orbit (LEO) mobile communication constellations to transmit remote sensing satellite data represents an emerging paradigm for overcoming the bottleneck in downloading massive amounts of Earth observation data. However, dense concurrent access across multiple satellites triggers intense resource competition, severe visible-window fragmentation, and strict resource-exclusivity constraints. To address the complex scheduling challenges caused by high laser link establishment overhead and the high-dynamic motion between remote sensing satellites and LEO communication nodes, this paper proposes an Adaptive Multi-Objective Reconstruction Evolutionary Algorithm (AMOREA). The algorithm incorporates a hybrid initialization strategy to improve the quality of the initial solution set and designs a mission-level topology reconstruction mechanism that uses four complementary decomposition operators and a multi-strategy reconstruction pool to achieve effective resource aggregation. Furthermore, an adaptive weight feedback mechanism is introduced to dynamically adjust search priorities and balance global exploration with local exploitation. Simulation results show that, under the simulation settings of this study, AMOREA reaches a 100.0% completion rate for urgent high-priority tasks and an overall average task completion rate of 89.2%. In terms of multi-objective optimization performance, AMOREA obtains the highest mean hypervolume (HV) value among the compared algorithms, improving the mean HV by approximately 19.1% over NSGA-II, 17.6% over MOEA/D, and 67.6% over the Greedy baseline. These results indicate that AMOREA can generate higher-quality Pareto solution sets and improve the efficiency of high-dynamic inter-satellite transmission scheduling under the tested simulation settings. Full article

With the continuous expansion of unmanned aerial vehicle (UAV) applications, generating near-optimal paths and achieving effective obstacle avoidance in complex environments remain highly challenging tasks. To address the problems of multi-objective path planning and obstacle detection for UAV flight missions in orchard environments, this paper proposes a novel hybrid algorithmic framework named NeuroJPS-A. The main scientific contribution is the synergistic integration of neural combinatorial optimization, 3D-JPS, and adaptive APF, enabling task-aware obstacle avoidance and closed-loop trajectory adjustment. This method introduces neural combinatorial optimization from the TSP into the 3D-JPS algorithm, optimizing the search mechanism of the traditional JPS and further shortening the UAV’s globally planned path length. In addition, this study integrates the proposed algorithm with the APF to solve the local dynamic obstacle avoidance problem. Quantitative results show that NeuroJPS-A reduces path length by 10% and the number of turns by 47.8% in 2D, and achieves a 24.9% shorter path and 22% of A*’s computation time in 3D. To verify the performance of the proposed method, comprehensive simulation experiments were conducted. The experimental results demonstrate that the NeuroJPS-A algorithm enables UAVs to quickly and effectively generate optimal planned routes, ensuring safe navigation in complex orchard environments and preventing collisions during flight missions. Full article

Amid the accelerating globalization of Traditional Chinese Medicine (TCM), the export performance of Chinese Medicinal Materials (CMMs) has emerged as a critical indicator of its international competitiveness and systemic resilience. However, existing studies predominantly rely on aggregated national statistics, thereby obscuring substantial inter-provincial heterogeneity and limiting the capacity to capture underlying structural dynamics. To address this challenge, this study conceptualizes CMM export as a complex regional economic system and constructs a panel dataset encompassing 31 provinces in China. We propose a structurally interpretable semi-parametric framework, termed the SR-MOABC-FE model, which integrates symbolic regression (SR) with a fixed effects (FE) specification. Within this framework, SR is employed to flexibly characterize nonlinear relationships, while the FE component accounts for province-specific heterogeneity. To further enhance model performance, a multi-objective artificial bee colony (MOABC) algorithm is developed to simultaneously optimize goodness-of-fit, predictive accuracy, and model parsimony. Empirical results demonstrate that the proposed model achieves a test-set mean absolute percentage error (MAPE) of 9.15%, significantly outperforming both conventional econometric models and mainstream machine learning approaches. Beyond predictive gains, the model retains strong structural interpretability, enabling the identification of key driving factors, including policy support, rural residents’ per capita disposable income, and natural resource endowment. Overall, this study advances a hybrid system modeling paradigm that bridges interpretable econometric structures and data-driven symbolic learning, offering both methodological innovation and actionable insights for the analysis and optimization of complex trade systems. Full article

Addressing the challenge that, in real-world dynamic multi-objective optimization problems (DMOPs), the severity of changes between pareto optimal set (PS) varies at different times and exhibits nonlinear characteristics rather than simple translations or rotations—making them difficult for traditional prediction strategies to track accurately—this paper proposes a dynamic multi-objective optimization algorithm via trend-cycle decoupling and hybrid time-series prediction. The algorithm first applies the Hodrick-Prescott (HP) filter to decompose the time-series of historical PS centers into a smooth trend component and a fluctuating cycle component to cope with uncertainty in the severity of changes. Then, an AR(p) model is used to fit the trend sequence and infer the long-term linear direction of PS movement; a long short-term memory (LSTM) network learns the cycle sequence to capture nonlinear variation patterns. By fusing the two prediction results, the center of the PS in the new environment is located, and an initial population is constructed using a manifold-based population generation strategy. Comparative experiments on 13 standard dynamic test functions show that the proposed algorithm achieves an effective trade-off between prediction accuracy and computational cost and demonstrates strong robustness to complex time-varying environments. In particular, in scenarios where the pareto optimal front (PF) undergoes rotation, discontinuity, or time-varying shape (convexity/concavity) due to complex mappings in the decision space, the algorithm maintains notable tracking accuracy and population diversity by precisely capturing the PS evolution trajectory. Full article

This study investigates the optimal sizing of a grid-connected hybrid renewable energy microgrid. The optimization, employing a multi-objective artificial hummingbird algorithm (MOAHA) combined with fuzzy decision-making (FDM), aims to minimize the cost of energy while maximizing renewable energy utilization. MOAHA is used to generate a well-distributed Pareto front, while FDM identifies the preferred configuration under the specified decision preference. However, the preferred solution obtained is a static configuration. Most existing studies focus on such static planning, with limited attention to dynamic mapping and validation of the optimized configuration. To bridge this gap, a digital twin architecture is further proposed for hybrid renewable energy microgrids, and a corresponding digital twin system is also developed to achieve virtual representation, dynamic state mapping, operational visualization, and configuration validation. An industrial park microgrid in Urumqi is selected as the case study. The results indicate that the preferred configuration achieves a cost of energy of 0.065 $/kWh and a renewable energy utilization of 0.675. Comparative results demonstrate that the proposed framework outperforms benchmark methods in terms of convergence, solution diversity, and computational efficiency. Meanwhile, the developed digital twin system effectively supports time-series state visualization and feasibility checking of the optimized configuration. Full article

This paper proposes a novel method for automatically designing lightweight Convolutional Neural Network (CNN) architectures. (1) Background: Automated remote sensing for vegetation monitoring faces challenges from structural complexity and cluttered backgrounds. For detecting parasitic Phoradendron velutinum infestations, existing vision frameworks rely on handcrafted, overparameterized CNNs, limiting deployment on localized edge computing platforms. (2) Methods: To address this efficiency-accuracy trade-off, a two-phase hybrid multi-objective Neural Architecture Search (NAS) strategy is implemented. First, the Multi-Objective Evolutionary Algorithm based on Decomposition (MOEA/D) minimizes classification error and the number of trainable parameters. Second, an Iterated Local Search (ILS) metaheuristic refines promising non-dominated solutions. The approach was evaluated using cost-effective aerial RGB imagery, processing a balanced dataset of 5000 patches ($64\times 64$ pixels) under a rigorous three-way data partition to prevent data leakage. (3) Results: The discovered 10-layer CNN topology achieved high feature-extraction efficiency. On the unseen testing set, the model yielded an Accuracy and F1-Score of $0.979$, a Precision of $0.982$, a Recall of $0.976$, and a Jaccard Index of $0.958$, outperforming the compared models. Operating with only 2040 trainable parameters, the optimized architecture establishes a highly viable paradigm for real-time digital image processing on hardware-constrained monitoring devices. Full article

Namibia’s rural communities continue to experience limited and unreliable electricity access despite the potential of the country’s exceptional solar, wind, and biomass renewable energy resources. Conventional grid extension remains financially and technically impractical for dispersed off-grid settlements, underscoring the need for cost-effective, renewable-based alternatives. This paper presents a resource-driven design and multi-objective optimization framework for Hybrid Renewable Energy Systems (HRESs) tailored to Namibia’s off-grid communities. The proposed model integrates solar PV, wind turbines, biomass generators, and hydrogen-based fuel cells with a hybridized energy storage consisting of batteries, supercapacitors, and hydrogen tanks. Using the Non-dominated sorting Genetic Algorithm-II (NSGA-II), the system simultaneously minimizes Total Life Cycle Cost (TLCC), Levelized Cost of Electricity (LCOE), Loss of Power Supply Probability (LPSP), carbon dioxide (CO₂) emissions, and Wasted Renewable Energy (WRE). The framework is applied to three rural villages, Oluundje, Ombudiya, and Onguati, using high-resolution, site-specific renewable resource datasets and community-level load forecasts. The results demonstrate that resource-aligned configurations substantially improve system reliability (up to 99.28%), reduce LCOE (0.0023–0.0811 USD/kWh), and optimize dispatch behaviour across seasonal variations. Storage hybridization further enhances stability by balancing transient and long-duration deficits. Compared to existing diesel mini-grids, the optimized HRESs achieve markedly superior techno-economic and environmental performance. The proposed framework offers a scalable, adaptable, and policy-ready tool for accelerating sustainable rural electrification in Namibia. Full article

Accelerated electric vehicle (EV) adoption imposes complex requirements on grid integration and energy dispatch. Current Vehicle-to-Vehicle (V2V) trading research frequently utilizes monolithic forecasting architectures that fail to account for the stochastic nature of mobility data. Furthermore, traditional optimization strategies often prioritize financial yields at the expense of user-centric utilities, hindering global system optimality. To resolve these limitations, this paper proposes a hierarchical optimization framework, designed to reconcile the interests of stakeholders. The approach first employs a hybrid deep learning architecture, integrating long short-term memory (LSTM), gated recurrent unit (GRU), and Transformer architectures, dynamically weight predictions and refine available dwell time estimations. Then, a multi-objective optimization model is formulated to identify Pareto-optimal solutions that balance economic efficiency with user convenience. Finally, a dynamic greedy matching algorithm is introduced to facilitate rapid transaction pairing for large-scale, real-time V2V requests under multiple constraints. Simulation results demonstrate that this hierarchical framework improves trading success rates, optimizes resource distribution, and enhances overall user satisfaction. Full article

Protein–protein interactions are a fundamental component of most biological processes in living organisms. Therefore, enhancing these molecular interactions is of major importance in the life sciences field. In this paper we present the redesign of a protein–protein complex using single-elitist and multi-objective evolutionary algorithms. We focus on a structural complex composed of a bacteriophage protein interacting with a bacterial protein, emphasizing the interaction zone, which is defined by the closest distance between residues from proteins and comprises thirty-eight positions. Our approach fuses physics-based energy calculations with data-driven models to maximize the effectiveness of the search process. By simultaneously minimizing interaction energy and maximizing the log-likelihood ratio, the proposed algorithms show a balance between thermodynamic stability and biological sequence plausibility. This hybrid strategy guides the mutation of specific residues, enabling the identification of optimal solutions that are both physically robust and evolutionarily relevant. Results demonstrate that the evolved Pareto optimal set exhibits a significant improvement in binding affinity, with mean interface energy decreasing from +32 to −60 REU (Rosetta Energy Units). Furthermore, the analysis identifies key conserved residue positions, validating the capability of the framework to produce energetically favorable and biologically consistent protein designs. Full article