Search Results (158)

In Industrial Internet of Things (IIoT) deployments, mobile edge computing (MEC) offloads computation-intensive tasks, a constrained biobjective problem trading time delay against energy consumption (MTOP). We show that this benchmark is exactly separable across micro base-station (MiBS) regions: its delay and energy objectives are additive over regions, and the only coupling, intra-cell interference, stays within a region. Exploiting this, we propose CR-MTMEMTO-D, a structure-aware decomposition multitasking method that treats each region as an independent subtask, solves it with a feasibility-repaired NSGA-II, and reconstructs the global feasible Pareto front as the non-dominated subset of the Minkowski sum of the regional fronts, an exact composition that adds no global evaluations. Across 12 instances (45–432 variables, 20 seeds), it attains the best hypervolume and IGD on every instance (mean HV 0.9340 vs. 0.8021 for a plain NSGA-II baseline; average rank 1.00), with the margin widening as the problem scales, and it is unchanged under total-evaluation matching because every evaluation is a regional main task. A feasibility-priority acceptance gate keeps the population fully feasible. Under matched budgets, a prior cheap-task pool with bandit-controlled transfer adds no significant gain, which motivates the structural approach. Full article

This paper presents a resilience-driven probabilistic multiobjective framework for reactive power planning in islanded microgrids under extreme contingencies, explicitly integrating sustainability objectives and alignment with the United Nations Sustainable Development Goals (SDGs). The proposed planning framework simultaneously optimizes technical reliability, economic viability, environmental sustainability, and social resilience using the IEEE 33-bus distribution system as a representative test network. Uncertainties associated with solar irradiance, wind speed, and load demand are modeled using the Two-Point Estimation Method (2PEM), while the Non-dominated Sorting Genetic Algorithm II (NSGA-II) determines Pareto optimal planning solutions for five reactive power support strategies. The results demonstrate that planning solutions optimized for grid-connected operation are not necessarily the most effective under islanded conditions. Within the adopted multi-criteria evaluation framework, the dedicated D-STATCOM strategy achieves the highest overall normalized performance, providing 87.2% load preservation, 93.7% critical-load protection, and an 8.7 h representative survival time, while reducing total load shedding to 12.8% and eliminating high-risk shedding events (>30%). Furthermore, it decreases event-related economic losses by more than 75% and achieves the lowest environmental impact, with a 62.5% reduction in life-cycle CO₂ emission intensity relative to the conventional grid baseline. A normalization sensitivity analysis confirms that the comparative ranking of the investigated strategies remains unchanged under alternative normalization methods, demonstrating the robustness of the proposed evaluation framework. From a sustainability perspective, the proposed framework contributes to SDG 7 (Affordable and Clean Energy) through reliable low-carbon microgrid operation, SDG 9 (Industry, Innovation and Infrastructure) through resilient power system planning, SDG 11 (Sustainable Cities and Communities) by enhancing the continuity of critical urban services, SDG 13 (Climate Action) through reduced life-cycle emissions, and SDG 8 (Decent Work and Economic Growth) by supporting local employment associated with distributed energy deployment. Full article

This paper introduces an integrated multi-objective evolutionary framework for synthesis of heterogeneous regression ensembles featuring localized, learner-specific feature selection. Rather than enforcing global feature spaces, the proposed paradigm simultaneously optimizes base estimator activation patterns, customized variable subsets tailored to each active learner, and continuous voting weights within a unified mixed-variable optimization process. The evolutionary pipeline minimizes two conflicting axes: predictive error, quantified via Root Mean Squared Error, and structural complexity, modeled as the average cardinality of selected features across active estimators. To prevent data leakage, the framework is validated under a rigorous nested cross-validation architecture using five real-world application benchmarks and comprehensively evaluated against 16 baseline configurations, including standalone learners, static voting ensembles, and isolated wrapper multi-objective feature selection pipelines. The empirical findings demonstrate that our joint evolved-weight variant achieves the dominant global predictive ranking across both non-parametric Wilcoxon and absolute mean trajectories. Concurrently, it maintains exceptional structural parsimony by yielding competitive dimensionality reductions, effectively balancing execution runtimes against Pareto-optimal generalization. Furthermore, a systematic ablation analysis isolates the continuous weighting mechanism as a pivotal driver for discovering significantly more compact ensemble topologies. Full article

Recycled aggregate concrete (RAC) mix design requires simultaneous consideration of mechanical performance, environmental impacts, and economic costs, yet these objectives are often evaluated separately. This study developed an integrated framework combining machine-learning-based strength prediction, life-cycle assessment, life-cycle cost analysis, constrained three-objective optimization, and preference-sensitive decision analysis. Using 407 RAC mixtures, Optuna-tuned Random Forest, XGBoost, and LightGBM models were compared, and SHAP was applied for interpretation. LightGBM achieved the best test performance, with an R² of 0.8822, an RMSE of 4.0276 MPa, and an MAE of 2.8865 MPa. The water-to-cement ratio, sand ratio, and superplasticizer dosage were the three leading predictors, together accounting for 68.5% of the normalized SHAP importance. A 100-generation NSGA-II optimization produced 150 feasible Pareto solutions spanning 33.87–75.25 MPa in compressive strength, 456.80–616.38 CNY/m³ in life-cycle cost, and 248.84–395.00 kg CO₂e/m³ in net carbon emissions. Higher-strength solutions generally required more cement and lower water-to-cement and recycled aggregate replacement ratios. Equal-weight TOPSIS selected P006, whereas the SMAA–TOPSIS simulation identified P007 as the alternative with the highest first-rank acceptability of 35.92%. By treating compressive strength as an explicit objective rather than a predefined constraint, the framework maps the continuous strength–cost–carbon trade-off within a volumetrically feasible mix-design space and identifies preference-dependent RAC design strategies. Full article

Hardware sizing and energy management for hybrid energy storage systems are usually designed sequentially, hiding the interactions between them. This paper proposes an MPC-informed dynamic screening framework in which every candidate configuration is simulated under one model predictive control law over a complete driving cycle, so that operational behaviour, not static metrics, determines selection. A fully documented post-evaluation criterion aggregates tracking, battery electrical stress, soft constraint violations and design overhead into one score normalised against an exact baseline anchor. Because one evaluation costs about 60 ms, the complete exact Pareto front of an electric transit bus case study is screened, not a sample. The static design cost proves almost uninformative regarding dynamic performance: the rank correlation between the two orderings is statistically indistinguishable from zero, the sets that they rank highest share no member, and the statically cheapest design falls far down the dynamic ranking, ending below the baseline. The cause is structural opposition on the pack voltage, which improves the dynamic performance but raises the static cost. The framework returns a leading design family that improves on the baseline overall, quantifies the battery stress that its leaner supercapacitor incurs, and shows the verdict to be robust to controller tuning but dependent on the duty and control strategy. Full article

We report a two-layer computational workflow for designed heterocyclic polymer dimers as candidates for organic solar cell (OSC) materials. The workflow integrates geometry-optimized B3LYP/6-31G(d) quantum-chemical descriptors (HOMO, LUMO, gap, dipole moment) computed for five fully disclosed monomer–dimer pairs (1m–5m; 1d–5d), with a verified, literature-curated 17-entry OSC dataset (PCE 3.6–19.9%, years 2016–2024) modeled by a non-tautological ridge regression baseline (Model A; predictors Year + source_block + log10 hole mobility). All five dimers were computed under uniform neutral closed-shell conditions. Pareto-front analysis in the gap–dipole descriptor space identifies dimer 2d (difluorinated thiophene–diazine D-A dimer; gap 1.74 eV, dipole 16.59 D) as the Tier I lead candidate, with 3d (bis(thiophene–triazine) dimer) and 1d (bis-thiophene–thiazole dimer) as additional Tier I candidates. Model A yields R²(LOOCV) = 0.660, MAE = 2.36%, and RMSE = 3.67%, surviving a 500-shuffle permutation null at empirical p < 0.001. A descriptor-augmented Model B (Eg + HOMO added) demonstrates that the present literature dataset cannot support a deployable molecular-descriptor regression without expansion. The combined DFT–regression workflow provides a transparent screening framework that identifies 2d as the priority synthesis target. Full article

This work addresses the simultaneous prediction of Marshall Stability (MS) and Indirect Tensile Strength (ITS) by integrating machine learning models with multi-objective optimization for the preliminary design of asphalt concrete. Based on 389 experimental samples, 15 variables were selected to describe asphalt properties, aggregate gradation, volumetric parameters and fiber characteristics, and four dual-output prediction models were developed. The models were evaluated using 50 Monte Carlo splits. TabICLv2 performed slightly better for MS prediction, with an RMSE of 1.49 ± 0.22 kN and an R² of 0.85 ± 0.04, whereas TabPFN showed a slight advantage for ITS prediction, achieving an RMSE of 0.23 ± 0.08 MPa and an R² of 0.91 ± 0.06. Furthermore, Pareto filtering identified nine non-dominated mixtures, and TOPSIS ranking selected the highest-ranked equal-weight compromise mixture, with MS = 15.23 kN and ITS = 3.90 MPa. The results indicate that mineral fibers are more suitable for improving the balanced performance of MS and ITS, carbon fibers are more favorable for improving MS, and plastic fibers are more effective in improving ITS. Finally, a Streamlit-based graphical user interface was developed to enable real-time prediction and MS–ITS trade-off visualization, providing a reference for preliminary mix design of asphalt concrete. Full article

Facility location decisions directly affect operational costs, service quality, and customer allocation. However, minimising total cost may result in an imbalanced distribution of customers among open facilities, requiring both objectives to be considered simultaneously. This study proposes a novel non-dominated sorting adaptive binary artificial bee colony algorithm with adaptive operator selection, called NSABC, for the bi-objective uncapacitated facility location problem. The first objective minimises facility opening and customer assignment costs, while the second minimises customer allocation imbalance among open facilities. NSABC integrates Pareto-based archiving, smart initialisation, adaptive operator selection, and diversity-preservation mechanisms to generate high-quality and diverse trade-off solutions. Computational experiments on 15 OR-Library CAP benchmark instances evaluate the algorithms using Hypervolume and Inverted Generational Distance as complementary Pareto-front performance indicators, together with paired two-sided Wilcoxon signed-rank tests and Holm correction. NSABC achieves higher mean Hypervolume values on most instances and lower mean IGD values on 14 of the 15 instances. The statistical analysis significantly favours NSABC on 11 instances according to Hypervolume and on 9 instances according to IGD, whereas NSGA-III is significantly favoured on only one instance according to IGD. The performance advantages of NSABC were observed across benchmark instances of different sizes and scales, indicating its effectiveness under varying problem structures. These findings indicate that NSABC is a competitive and statistically supported alternative to NSGA-III for bi-objective facility location problems involving both economic efficiency and balanced customer distribution. Full article

Population-based bio-inspired metaheuristics are the dominant tools for tuning hybrid decomposition–deep-learning forecasters, yet their relative behavior on a common problem is rarely assessed with a leakage-free, physically meaningful protocol. We benchmark eight metaheuristics on the joint nine-dimensional hyperparameter optimization of a discrete-wavelet-transform bidirectional-LSTM (DWT–BiLSTM) architecture for short-term wind speed forecasting, using 409,152 hourly observations from eight meteorological stations. The set comprises six nature-inspired methods (Artificial Bee Colony, ABC; genetic algorithm, GA; Particle Swarm Optimization, PSO; Grey Wolf Optimizer, GWO; Hippopotamus Optimization, HO; and the Raindrop Optimizer) together with two recent metaphor-free or social variants (the Farthest-better Nearest-worse Optimizer, FNO; and the Tuckman Optimization Algorithm, TOA). A multi-stage protocol covers 30 independent runs per algorithm, a joint-versus-sequential comparison, a genuine rolling-origin out-of-sample evaluation, and component ablation. Friedman testing reveals significant differences (χ² = 49.76; p < 10⁻⁸), with the Grey Wolf Optimizer attaining the best mean rank (2.27) and Pareto-dominant run-time; ablation shows the DWT front-end is essential (Cohen’s d = 13.09) and bidirectionality negligible at the one-hour horizon (p = 0.674). Critically, evaluating forecasts in reconstructed physical units reveals that the per-component advantage does not persist: at the one-hour horizon the reconstructed forecast does not exceed a naive persistence baseline (skill ≈ −0.5 in m/s versus +0.44 in normalized component space), a discrepancy independent of decomposition leakage that we report transparently. This work thus contributes a rigorous, leakage-controlled bio-inspired benchmark and a cautionary evaluation methodology. Full article

The sustainability of biomass utilization as a fuel is commonly assessed through thermodynamic and ecological methods—energy, exergy, and emergy analyses—applied in isolation, each with only partial scope. Their integration through multicriteria analysis has been proposed for the sugar industry, but has not yet been formalized as a reproducible, auditable, and generalizable protocol: neither the logical sequence linking balances and decision-making, nor an explicit sustainability rule, nor the treatment of the incommensurability between thermodynamic and ecological accounting has been established. This work formalizes such a protocol in three stages: (i) definition of fuel alternatives and screening of criteria through the Delphi method; (ii) characterization of each alternative through three coupled balances—energy (quantity), exergy (quality), and emergy (environmental cost); and (iii) integration through the Analytic Hierarchy Process (AHP) into a single sustainability ranking with an explicit decision rule, supported by a robustness layer based on Monte Carlo simulation and multi-method comparison. The protocol is demonstrated in the Cuban sugar industry using two steam generators (G.V. VU-40 and Retal-type steam generator) and variants of bagasse, agricultural harvest residues (AHR), and marabou (Dichrostachys cinerea). In the demonstration, AHP weighting ranked the emergy criterion above the exergy and energy criteria (priority vectors 0.539, 0.297, and 0.164, respectively; consistency ratio 0.008), and the bagasse alternative emerged as the most sustainable in both technologies despite not being the most efficient. The robustness analysis confirmed that this verdict is stable: bagasse Pareto-dominates the independent emergy indicators and remains the best alternative in more than 95% of the weight space. The contribution of the work is methodological—the formalization and generalization of the protocol—while the case study illustrates its operation and does not constitute a statistical validation. Full article

Animal re-identification (Re-ID) asks whether two images show the same individual, a recognition task that fits naturally into applications such as reuniting lost pets with their owners. Existing methods report strong scores, but typically under a single seed, one mask granularity, and no query-time corruption analysis, leaving open whether the gains survive deployment. We propose a hierarchical framework decoupling localisation (a YOLOv8 soft-crop) from identity embedding: a dual-stream network fusing a frozen CLIP ViT-B/16 (learned projection) with a fine-tuned ViT-Base carrying L2-norm part attention, trained under ArcFace. On a combined cat+dog open-set benchmark of 173 identities, it attains Rank-1 0.9742/mAP 0.8597 over three seeds, surpassing a ViT-only ablation by +2.39 Rank-1 and +2.07 mAP. Open-set verification shows all configurations converge near 68% true acceptance at the strictest false-acceptance rate. A background-bias evaluation brackets the embedding’s background reliance between a bounding-box lower bound and a SAM-silhouette upper bound; a manual audit retains the reliable soft crop. A nine-corruption audit identifies down-sampling and motion blur as dominant. On PetFace, the architecture retrieves across 14,716 unseen identities and remains viable in a few-shot regime. A Descriptor Vector Exchange (DVE) extension is Pareto-dominated, traced to the ViT’s coarse feature map and architectural redundancy. Full article

Water resource management increasingly requires allocation methods that address rising demand, climate variability, and operational inefficiency. This study proposes an elite-transfer hybrid Genetic Algorithm–Particle Swarm Optimization framework for constrained water allocation under a weighted-sum scalarization model. The model combines four normalized objectives: minimizing water shortage, an operational cost coefficient, and allocation imbalance while maximizing utilization efficiency through an equivalent minimization term. A bidirectional elite-transfer mechanism links GA-based global exploration with PSO-based local refinement. The framework was evaluated using two capacity-constrained surrogate scenarios anchored to hydrometeorological records from Türkiye: Melekbahçe station (E21A033) in the Upper Euphrates Basin and Beşdeğirmen station (E12A003) in the Sakarya Basin. Daily streamflow records supported scenario construction, while precipitation and air-temperature data characterized local conditions. The proposed method was compared with standalone GA and standalone PSO, Differential Evolution, Grey Wolf Optimizer, a scalarized NSGA-II, adapted Kao–Zahara and Garg GA–PSO hybrids, and a no-elite ablation. All methods used the same objective formulation, normalization bounds, constraint-repair procedure, equal objective weights, tuning protocol, paired random seeds, and a budget of 10,000 objective-function evaluations. Performance was assessed through 30 paired runs per scenario. The proposed framework achieved the lowest mean scalar fitness values—0.1670 for Melekbahçe and 0.1752 for Beşdeğirmen—and reached the predefined convergence region after averages of 4587 and 4780 evaluations, respectively. All fitness and convergence improvements remained significant after Holm correction. Paired rank-biserial correlations ranged from 0.957 to 1.000 against the seven general comparators and were 0.824 and 0.781 against the no-elite ablation. The findings support the framework under the tested surrogate scenarios but do not establish Pareto-front dominance or universal superiority. Future work should examine measured operational data, additional basins, dynamic scheduling, alternative weights, and Pareto-based extensions. Full article

Coordinated management of drip irrigation water demand and lateral canal supply is a critical strategy for improving water use efficiency in arid irrigation districts; however, under water-deficit conditions, the efficient and equitable allocation of limited canal water among multiple drip irrigation systems remains largely unresolved. This study developed a bi-objective cooperative water allocation and scheduling model for a lateral canal serving 11 subordinate drip irrigation systems. Subject to canal diversion flow balance and total deficit constraints, the model simultaneously minimized (i) the mean coefficient of variation (CV) of water allocation duration within rotation irrigation groups, targeting temporal uniformity, and (ii) the sum of squared deviations of the water supply satisfaction rate across systems, targeting distributional equity. Water demand inputs were derived from a localized FAO-56 Penman–Monteith irrigation schedule for Jinghe County with stage-specific crop coefficients. A hybrid binary–continuous NSGA-II encoding with a dynamic intra-group flow allocation mechanism was employed. For the baseline deficit scenario (Early May, supply-to-demand ratio β = 67.76%), the model partitioned the 11 systems into four rotation groups with a mean CV of 3.15 × 10⁻³, while the sum of squared deviations of the satisfaction rate decreased from 4.366 under the empirical scheme to 1.50 × 10⁻⁴, confining all systems to 67.38–68.45% and eliminating the coexistence of over-supply and complete deprivation (Wilcoxon signed-rank test, p < 0.001; Cohen’s d = −1.698). The Pareto front revealed a significant efficiency–equity trade-off (Spearman’s ρ = −0.9999), and NSGA-II outperformed SPEA2 by approximately 29-fold and 22-fold in the two objectives. Robustness was confirmed across three deficit scenarios and algorithm parameter sensitivity analyses (CV < 2%). The study offers methodological support for refined water allocation management of terminal canal systems in arid regions. Full article

Fused deposition modeling (FDM) of polylactic acid (PLA) is widely used, yet most parameter studies tune one mechanical property at a time and leave the conflicts between properties unresolved. This work treats three responses of FDM PLA together: ultimate tensile strength, flexural strength, and Shore D hardness. A Taguchi L9 orthogonal array varied infill density, raster angle, and layer thickness; signal-to-noise ratios and analysis of variance ranked the factors, linear regression linked the parameters to each response, and the NSGA-II algorithm mapped the trade-off surface between them. Layer thickness proved the leading factor for tensile and flexural strength, while hardness answered mainly to infill density; raster angle stayed weak for every response. The Pareto front showed that low infill favors tensile strength while high infill favors flexural strength and hardness, all at the finest layer setting, and these trends were converted into parameter guidelines for tensile-led, flexure-led, and balanced parts. The statistical limits of the screening design are stated openly, the flexural and hardness campaigns are flagged as provisional, and a replicated, standard-compliant confirmation study is set out as the next step. Full article

The explosive growth of artificial intelligence workloads has triggered unprecedented data center construction, yet environmental assessment remains focused on operational energy efficiency. This paper introduces DC-WLC3O, a framework that simultaneously optimizes three objectives across the full data center lifecycle: whole-life cost, whole-life carbon, and material circularity index. The framework integrates cradle-to-cradle lifecycle assessment with dynamic material flow analysis, employing a four-layer analytical stack: Monte Carlo simulation with Latin Hypercube Sampling and Iman–Conover rank correlation; NSGA-III tri-objective Pareto optimization; Sobol’ variance-based global sensitivity analysis; and Many-Objective Robust Decision Making (MORDM). The framework evaluates 192 design configurations for a 30 MW hyperscale facility under three carbon pricing scenarios. Results reveal a carbon rebound paradox: operational carbon dominates grid-dependent configurations (96.2%), but low-carbon energy produces an 8.5× inversion (Welch’s t = 22.2, p < 10⁻³⁹). Sobol’ analysis confirms energy strategy dominates carbon outcomes (S1 = 0.87). MORDM identifies on-site renewable energy with a full circular economy strategy as unconditionally robust (R = 0.056, stable across 94.2% of weight permutations). The cost–circularity correlation is negative (rho = −0.488, p < 10⁻¹²), overturning the assumption that circular strategies increase lifecycle costs. A post hoc stress test confirms this finding is robust to moderate perturbations in renewable LCOE (+71%), with stability declining to ≈80% under worst-case capital cost assumptions and to ≈61% when generation degradation and battery replacement are jointly considered. Full article