Search Results (91)

Adaptive bitrate streaming over HTTP relies on carefully constructed bitrate ladders and ordered sets of bitrate–resolution pairs to deliver optimal perceptual quality under fluctuating network conditions. While content-aware methods based on convex hull optimisation have substantially improved ladder efficiency for Video-on-Demand, they require exhaustive multi-resolution pre-encoding that is computationally prohibitive under the real-time constraints of live streaming. This challenge is compounded by the H.266/Versatile Video Coding (VVC) standard, which offers approximately 50% compression gains over HEVC at 8–10× the encoding complexity. This paper presents LiveCH-VVC, a latency-aware dynamic bitrate ladder prediction framework for VVC-encoded live streaming over Low-Latency DASH (LL-DASH) with CMAF packaging. The framework introduces four integrated modules: (i) a Lightweight Dual-Path CNN (LDP-CNN), obtained via teacher–student knowledge distillation (∼5 M parameters, 148 ms GPU inference), that jointly extracts spatial–temporal features from raw frames and compression-domain statistics from a fast VVC probe encode; (ii) an adaptive scene change detector with exponential moving average thresholding (F1 = 0.925) that triggers ladder updates only upon significant complexity shifts; (iii) a temporally augmented XGBoost multi-label classifier that predicts latency-constrained Pareto-optimal bitrate–resolution pairs; and (iv) an online adaptation engine that integrates Common Media Client Data (CMCD) feedback from CDN edge servers for continuous closed-loop refinement. Comprehensive evaluation on 81 UHD sequences (∼4050 CMAF segments) from three benchmark datasets demonstrates an average BD-Rate of $+0.68\%$ relative to the per-segment oracle convex hull $5.4\times$ better than the state-of-the-art ARTEMIS framework ($+3.67\%$) while achieving 73.3% encoding time savings, 2.37 s end-to-end latency, and a QoE score of 81.6 in live simulation with 100 concurrent clients. Ablation analysis confirms that the dual-path compression-domain branch ($+0.44$ pp) and temporal context augmentation ($+0.35$ pp) are the primary performance drivers, while the online adaptation mechanism provides $42\%$ relative improvement over extended streaming sessions. Full article

Structural health monitoring (SHM) is essential for assessing the safety and serviceability of bridge structures. Identifying progressive and concurrent damage remains challenging due to the complex and continuous nature of structural deterioration. This study proposes a hybrid one-dimensional convolutional neural network and long short-term memory (1D-CNN–LSTM) framework for vibration-based damage localisation and severity estimation in reinforced concrete bridges. Operational modal analysis is applied to field-measured vibration data from an in-service bridge. A finite element model is updated using particle swarm optimisation, reducing frequency discrepancies from 7–17% to within ±3%. Progressive single-, double-, and triple-element damage scenarios are simulated through systematic stiffness degradation. The resulting modal frequency data are used to train 1D-CNN–LSTM models using Pareto front optimisation. The proposed framework achieves coefficients of determination above 0.80 with low prediction errors (MSE and MAE < 2) for single- and double-element damage scenarios. The results support the use of the proposed framework for screening-level assessment of bridge damage under controlled simulated conditions. Full article

Background: Standard consumer theory treats preferences as fixed primitives and demand as the solution to an individual optimisation problem; we instead model consumption styles as heritable strategies whose prevalence is shaped by selection and experimentation, and ask when status competition produces an over-consumption trap. Methods: We embed a reference-dependent payoff—private utility concave in own consumption, a positional benefit proportional to consumption relative to the social mean, a financial-fragility cost, and a loss-averse relative-deprivation term—into replicator–mutator dynamics over three strategies (frugal, balanced, conspicuous). Results: Status concern induces strategic complementarity, so that a rising consumption norm penalises moderate consumers and makes imitation self-reinforcing. For intermediate status weight, the system is bistable: an efficient balanced equilibrium and a Pareto-inferior conspicuous trap are separated by a tipping threshold, and the width of the bistable window equals the deprivation weight, producing hysteresis in the consumption norm. The trap persists even though the positional benefit nets to zero in any monomorphic state. Mutation—behavioural experimentation—shrinks the bistable window and can dissolve the lock-in. Conclusions: Reference-dependent demand is better captured by evolutionary dynamics than by static equilibrium, and positional externalities can lock a population into self-defeating over-consumption that interventions on the deprivation or fragility channel may unlock. Full article

Higher construction costs (CCs) linked to carbon reduction methods have hindered the adoption of low-carbon approaches in the built environment. The simultaneous minimisation of upfront embodied carbon (EC) and CCs has not received much attention in building design optimisation (BDO) research; most studies prioritise operational energy, operational carbon, and operational cost reduction. This paper develops an integrated conceptual framework for low-carbon, cost-effective BDO, particularly targeting upfront EC and CCs, to fill this research gap and meet industry demands. A systematic literature review was conducted following PRISMA guidelines, synthesising 41 peer-reviewed articles published between 2015 and 2026. Thematic and content analyses were employed to extract and categorise key methodological components, including optimisation problem characterisation, objective-driven design variable selection, constraint modelling, algorithm selection, and evaluation and validation approaches. Subsequently, the developed conceptual framework was validated through semi-structured expert interviews with participants comprising BDO researchers and building designers in the construction field. A cross-mapping of optimisation objectives, optimised parameters, and design variables was developed to clarify their interrelationships, alongside structured criteria for optimisation algorithm selection. Based on these insights, a conceptual framework named “ICCO-BD (Integrated Upfront Carbon and Construction Cost Optimisation for Building Design) framework” is proposed and validated, integrating problem formulation, parametric modelling, multi-objective optimisation, and systematic Pareto-based evaluation into a coherent end-to-end workflow, enabling improved time efficiency through reduced redesign iterations, enhanced solution quality via better pareto front exploration, and more robust decision-making through clearer trade-off interpretation. While expert feedback indicated strong conceptual relevance and practical applicability, the framework remains conceptual in nature and requires further empirical verification through real-world case studies and optimisation applications before broader industry implementation. Full article

In this paper, a multi-objective optimisation method based on the Non-dominated sorting genetic algorithm II (NSGA-II) is proposed, which proves to be effective in solving the computer-aided molecular design (CAMD) problem in the design of solvents for cooling crystallisation. A multi-objective optimisation model has been developed for the CAMD problem of solvents in the crystallisation process with the toxicity, solubility parameters, and potential recovery of the solvents as objective functions and the feasibility of the molecular structure as constraints. The properties involved are to be calculated by the group contribution method, and the solubility parameters of the solute in the solvent are calculated based on the Universal Quasichemical Functional-group Activity Coefficients (UNIFAC) model. Based on this method, cooling crystallisation solvents for 2-mercaptobenzothiazole (MBT) and sebacic acid were designed. The results indicate that the proposed multi-objective CAMD framework exhibits a certain degree of generality. Even when the optimisation parameters and methods differ from those of other existing frameworks, it does not overlook the optimal solutions under specific design conditions. Furthermore, clustering of the Pareto front for MBT revealed that, since multi-objective optimisation does not aim to obtain a single optimal solution, it can identify multiple candidate solvents that balance potential yield and toxicity. This approach avoids the issue of single-objective optimisation, which tends to overemphasise potential yield at the expense of toxicity. Full article

This paper presents a decision-support framework for analysing the trade-off between total operational cost and customer service level in multi echelon inventory systems. The model integrates fixed-order-quantity replenishment policies, lead-time dynamics and multi objective optimisation to generate a detailed Pareto frontier of efficient solutions. A real multi echelon distribution network is used to demonstrate the model’s applicability and managerial relevance. The results indicate that raising the service level from 46% to the sector standard of 96% increases total cost by approximately 19%, while achieving full demand satisfaction requires an additional 5% cost increase for only marginal service improvement. This pattern reveals a clear cost–service turning point around the 96% service level, beyond which additional gains exhibit sharply diminishing returns. The framework, therefore, provides a transparent and analytical mechanism for identifying replenishment strategies that balance cost efficiency with service performance. By decomposing total cost into ordering, holding, transport and lost-sales components, the model enhances managerial visibility and supports targeted policy adjustments. The paper also discusses limitations of the current formulation and outlines avenues for future research, including alternative replenishment policies, multi-product extensions and richer uncertainty modelling. Full article

Shared immersive environment sports venues, virtual classrooms, and collaborative workspaces require multiple users to stream 360° videos simultaneously over the same edge network, yet every existing adaptive bitrate system optimises each viewer in isolation. This self-interested behaviour triggers a bandwidth auction that chronically starves the most uncertain viewers: Jain’s Fairness Index for ten independently optimised agents routinely falls below 0.85. We present FairEdge360, a hierarchical multi-agent reinforcement learning framework that reformulates multi-user 360° streaming as a Decentralised Partially Observable Markov Decision Process (Dec-POMDP) and proves, formally, that fairness and quality are complementary rather than competing objectives. Three tightly coupled innovations make this possible. First, a Lightweight Uncertainty Estimator (LUE) a compact 8385-parameter four-layer MLP evaluates per-device viewport prediction confidence $c_t^i=\sigma \left(w_4^{\top }{h}_3\right)$ in under approximately 2.1 ms on commodity smartphones (95th percentile, iPhone 12 A14 Bionic), enabling selective edge offloading that reduces device energy consumption by 38.9%. Second, a variational Graph Neural Network compresses each agent’s 256-dimensional GRU state into a 32-byte INT8 latent, transmitted over a dynamic RTT-gated neighbourhood graph at 96 bytes per agent per 500 ms 75% less overhead than competing approaches. Third, the edge coordinator maximises the Nash social welfare objective $\mathrm{NSW}={\left({\prod }_{i=1}^N{Q}_i\right)}^{1/N}$, whose gradient $\partial \mathrm{NSW}/\partial Q_i\propto 1/Q_i$ automatically prioritises the most disadvantaged viewer; a formal proof guarantees that every Pareto-optimal policy satisfies $Q_i/{\sum }_j{Q}_j\ge 1/N$. Counterfactual advantage estimation correctly attributes each agent’s marginal contribution to the global reward, eliminating the credit-assignment ambiguity inherent in standard multi-agent baselines. Evaluated on 284 users, 52 omnidirectional videos, and 10,000 real network traces spanning 4G LTE, 5G mmWave, HSDPA, and campus WiFi, FairEdge360 raises Jain’s Fairness Index from 0.934 to 0.976 (+4.5%), improves worst-case user quality-of-experience from MOS 2.54 to MOS 3.21 (+26.4%), and halves rebuffering rate from 2.1% to 1.1%, all within a 20 ms motion-to-photon budget on a commodity smartphone. Full article

The rapid advancement of beyond-5G and 6G services is creating computational challenges that classical optimisation methods for Elastic Optical Networks (EONs) cannot effectively handle. Specifically, the multi-objective Routing and Spectrum Assignment (RSA) problem—aimed at minimising blocking probability, maximising spectral efficiency, and reducing fragmentation—poses significant challenges and is NP-hard, particularly in dynamic traffic. This paper introduces a hybrid framework that combines quantum and classical computing, dividing the optimisation tasks into classical pre-processing, a quantum optimisation core, and classical post-processing with Pareto frontier management. The RSA problem is modelled using a Quadratic Unconstrained Binary Optimisation (QUBO) formulation that accounts for blocking, efficiency, and a quadratic fragmentation metric. Simulations conducted on NSFNET and UBN topologies under Poisson traffic conditions revealed that even in realistic, noisy quantum environments, this hybrid method reduces the blocking probability by 14% and improves fragmentation by 7.3% compared to the top classical heuristics. A scaling analysis indicates a key point of around 220 variables where this hybrid strategy surpasses traditional meta-heuristics in both solution quality and execution time, emphasising its significant potential in the current NISQ era. Full article

Across the Global South, energy inequity disproportionately affects women in off-grid communities. However, existing optimisation models for rural electrification rarely incorporate explicit gender constraints. This study develops and validates a multi-objective optimisation framework for balancing environmental sustainability, economic viability, and gender equity in energy access. The model’s objective functions are environmental impact, unsatisfied energy demand, total system cost, and gender inequality. Optimal values for these objectives were generated based on allocation of energy across solar PV, generators, and firewood sources. The Non-dominated Sorting Genetic Algorithm II (NSGA II), particle swarm optimisation (PSO), and a hybrid NSGA-PSO II approach were used to solve the developed model. A remote Nigerian community (Olooji) with 600 households and a population of 7000, classified as Tier 1 energy consumers, was used as a case study. The hybrid NSGA-PSO II method demonstrated superior performance. It achieved the lowest fitness value (4,461,024) by combining the exploration capabilities of NSGA II with the Pareto-optimal convergence strengths of PSO. Over the 25-year planning horizon, the model projects solar energy share to increase from 19.05% to 47.79%, firewood to decrease from 61.90% to 35.45%, and generator share to increase from 14.3% to 14.7%. The community’s energy demand coverage improves from 95.24% to 97.92%. The community maintains a stable male-to-female energy consumption ratio of approximately 1.18:1, while the energy equity gap decreases from 0.2000 to 0.0800 kWh/person/quarter over the planning period. Results demonstrate that the hybrid NSGA-PSO II effectively manages the complexity of multi-objective energy distribution while promoting energy equity and environmental sustainability in rural electrification. Full article

The increasing integration of distributed photovoltaic (PV) systems in urban environments requires planning frameworks that simultaneously address economic viability, environmental sustainability, and power system performance. This study develops a simulation-based techno-economic and environmental assessment framework for evaluating hybrid rooftop photovoltaic (PV) and building-integrated photovoltaic (BIPV) deployment under harsh climatic conditions. Detailed system modelling using PVsyst and ETAP is conducted to analyse energy production, economic performance, environmental impact, and grid interaction characteristics, including voltage deviation and harmonic distortion. To support deployment planning and operational decision-making, the simulation outputs are incorporated into a multi-objective optimisation framework that evaluates trade-offs among levelized cost of energy (LCOE), net present value (NPV), carbon emission reduction, and power quality indicators. Three deployment configurations including rooftop PV only, BIPV only, and a hybrid PV–BIPV system are assessed using structured trade-off analysis and Pareto optimality principles. Results indicate that the hybrid configuration provides the most balanced performance across technical, economic, and environmental objectives. The system achieves an average performance ratio of 77.36% and generates approximately 2075 MWh of annual energy while maintaining grid voltages within acceptable limits and harmonic distortion well below IEEE 519 thresholds. Economic analysis shows strong financial feasibility with an LCOE of approximately 0.05 USD/kWh, a payback period of 8.1 years, a net present value of about 2.88 million USD, and a return on investment exceeding 145%. Loss analysis further identifies temperature effects and dust accumulation as the dominant performance constraints under harsh environmental conditions. Moreover, Pareto-based evaluation confirms the hybrid PV–BIPV configuration as the preferred deployment strategy among the evaluated alternatives. The proposed framework demonstrates how integrated simulation and multi-objective optimization can serve as a practical decision-support tool for planners and policymakers seeking to optimise distributed renewable energy deployment under climatic and operational uncertainties. Full article

Understanding the structure of multi-objective optimisation problems (MOPs) is essential for analysing search difficulty and supporting informed decision-making. In single-objective optimisation, fitness landscapes offer a spatial view of a problem, but extending such visualisations to MOPs is challenging due to the vector-valued nature of objectives. In this work, we introduce Pareto landscape, a fitness landscape visualisation technique for multi-objective optimisation on the basis of the Pareto dominance relation. We illustrate the main characteristics of a Pareto landscape, relate it to the classical fitness landscape, and examine its behaviour across benchmark suites, constrained problems, multimodal problems and real-world cases. We also show how it captures problem landscape structures relevant to optimisation difficulty. A comparison with gradient field heatmaps, PLOT, cost landscape, and constrained cost landscape further demonstrates that Pareto landscape offers complementary insight by highlighting structural patterns not visible with existing visualisation methods. Overall, the results indicate that the Pareto landscape provides a consistent way to observe problem structure across different classes of multi-objective optimisation problems. Full article

Tritium self-sufficiency is a fundamental design requirement of a fusion fuel cycle, necessitated by the limited global availability of tritium relative to the fuelling demands of a fusion reactor. Minimising tritium losses within a fuel cycle is therefore essential. The Hydrogen Isotope Separation System (HISS) employs cryogenic distillation technology to remove excess protium and deuterium while rebalancing the deuterium–tritium (DT) mixture required for reactor operation. However, the HISS design involves a trade-off between reduced tritium emissions and increasing internal tritium inventory, both contributing to the overall tritium losses. In this work, a multi-objective Bayesian Optimisation (BO) framework based on an ε-constraint formulation is developed to construct Pareto-optimal solutions to compare alternative HISS architectures. Gaussian Process surrogate models derived from physics-based Aspen Plus simulations are used to resolve the non-linear relationships between design variables and performance metrics, including tritium inventory, tritium emission losses, and bottom-product purity. Application of the framework to representative case studies demonstrates that tritium emission losses significantly exceed tritium decay losses associated with internal inventory hold-ups across the investigated operating conditions. The proposed framework enables quantitative comparison of equilibrator integration strategies to compare HISS architectures and assess their impact on tritium losses within the fusion fuel cycle. Full article

Effective supply chain planning increasingly requires balancing cost-efficiency with environmental responsibility, particularly as organisations face growing pressure to reduce the carbon footprint of logistics operations. This study develops a mixed-integer linear programming model to optimise inventory and transportation decisions in a multi-echelon distribution network comprising a central warehouse, regional warehouses, and retailers. The model integrates a continuous-review (r,Q) replenishment policy, stochastic demand, safety stock requirements, transportation lead times, and stockout behaviour, enabling a detailed representation of operational dynamics under uncertainty and environmental concerns. Unlike most sustainable inventory models—which typically treat environmental impacts and replenishment control separately or rely on simplified service assumptions—this study provides an integrated framework that jointly embeds (r,Q) policies, stochastic demand, stockouts and distance-based CO₂ metrics within a unified optimisation structure. The model advances prior work by explicitly integrating continuous-review (r,Q) replenishment policies with distance-based CO₂ metrics under stochastic demand, a combination rarely addressed in sustainable multi-echelon inventory models. A multi-objective formulation captures the trade-off between economic performance and CO₂ emissions, allowing the identification of Pareto-efficient strategies that reconcile financial and environmental goals. Reducing emissions by over 90% requires an additional cost of only about 4%, demonstrating that substantial emission reductions can be achieved at relatively low additional cost. The findings offer practical insights for managers seeking to design more sustainable and cost-effective distribution policies, highlighting the value of integrated optimisation approaches in contemporary logistics systems. Full article

Isothermal dispatch models for battery energy storage systems (BESSs) systematically underestimate degradation costs because dispatch-induced Joule heating elevates cell temperature and accelerates ageing through Arrhenius-type kinetics. This paper proposes three integrated contributions. First, a thermal–electrochemical coupling loop embeds a first-order lumped thermal model within the dispatch simulation: cell temperature is updated from I²R heat generation and Newton cooling at each time step, and the resulting temperature trajectory feeds into the Arrhenius stress factors of a semi-empirical degradation model combining $\sqrt{\Delta t}$-based calendar ageing with Rainflow-based cycle ageing, enabling the optimiser to discover thermally self-regulating strategies. This coupling is critical because, as the results demonstrate, ignoring it leads to systematic underestimation of degradation costs by up to 13%. Second, the resulting four-objective problem (negative profit, thermally coupled degradation cost, SOC deviation, and CVaR imbalance penalty) is solved by a hypervolume-contribution-enhanced NSGA-III (HVC-NSGA-III), which augments reference-point selection with an archive pruned by removing the solution of the smallest individual hypervolume contribution, concentrating Pareto resolution in the knee region. Third, an SOH-adaptive knee-point selection assigns the degradation weight as a monotone function of ageing degree $(1-\mathrm{SOH})/(1-{\mathrm{SOH}}_{\mathrm{EOL}})$, automatically tightening dispatch conservatism as remaining useful life diminishes. Simulations on ENTSO-E data over 96 h show the following: (i) thermal coupling shifts the Pareto front by 8–15% in the degradation dimension with temperature excursions up to 7 K; (ii) HVC-NSGA-III improves hypervolume by 8.7% over standard NSGA-III; (iii) SOH-adaptive selection reduces capacity loss by 27.4% at only 9.1% revenue cost; and (iv) ablation confirms Rainflow (24.8%) and thermal coupling (13.1%) as the two largest contributors. Full article

Climate change is intensifying hydroclimatic extremes and agricultural water scarcity, sharpening trade-offs among yield stability, water saving, and farm incomes in major grain regions. Existing studies often optimise cropping patterns or irrigation schedules separately, seldom embedding yield robustness and policy instruments in one decision framework. We propose an integrated Machine-learning–System-dynamics–Non-dominated-sorting-genetic-algorithm-II (ML–SD–NSGA-II) framework linking long-horizon meteorological scenario generation, crop–water–economy feedback and multi-objective optimisation of crop areas and irrigation depths. ML models generate daily climate sequences to drive an SD model of soil moisture, yield formation, basin-scale allocable water, and farm returns; NSGA-II searches Pareto-optimal strategies that maximise profit and irrigation water productivity while minimising yield deviation. Applied to a rice–wheat irrigation system in the middle Yangtze River Basin, knee-point solutions lift irrigation water productivity by about 14%, maintain near-baseline profits, and reduce yield deviation. Scenario tests with block tariffs, quota-based subsidies, and extreme drought show pricing mainly curbs low-value water use in normal years, while under drought, physical scarcity dominates and economic tools offer limited buffering. This reveals the existence of a scarcity-regime threshold beyond which economic instruments become second-order relative to binding biophysical constraints. The framework supports transparent ex ante testing of tariff–subsidy packages for irrigation governance and adaptation. Full article