Search Results (91)

Under the current context of the large-scale integration of wind and solar power, the coupling of hydropower with wind and solar energy brings significant impacts on grid stability. To fully leverage the regulatory capacity of hydropower, this paper develops a multi-objective optimization scheduling model for hydropower, wind, and solar that balances generation-side power generation benefit and grid-side peak-regulation requirements, with the latter quantified by the mean square error of the residual load. To efficiently solve this model, Latin hypercube initialization, hybrid distance framework, and adaptive mutation mechanism are introduced into the Strength Pareto Evolutionary Algorithm II (SPEAII), yielding an improved algorithm named LHS-Mutate Strength Pareto Evolutionary Algorithm II (LMSPEAII). Its efficiency is validated on benchmark test functions and a reservoir model. Typical extreme scenarios—months with strong wind and solar in the dry season and months with weak wind and solar in the flood season—are selected to derive scheduling strategies and to further verify the effectiveness of the proposed model and algorithm. Finally, K-medoids clustering is applied to the Pareto front solutions; from the perspective of representative solutions, this reveals the evolutionary trends of different objective trade-off schemes and overall distribution characteristics, providing deeper insight into the solution set’s distribution features. Full article

Glass powder, a non-degradable waste material, offers significant potential to reduce cement consumption and carbon emissions in concrete production. However, existing mix design methods for glass powder concrete (GPC) fail to systematically balance economic efficiency, environmental sustainability, and mechanical performance. To address this gap, this study proposes an AI-assisted framework integrating machine learning (ML) and Multi-Objective Optimization (MOO) to achieve a sustainable GPC design. A robust database of 1154 experimental records was developed, focusing on five key predictors: cement content, water-to-binder ratio, aggregate composition, glass powder content, and curing age. Seven ML models were optimized via Bayesian tuning, with the Ensemble Tree model achieving superior accuracy (R² = 0.959 on test data). SHapley Additive exPlanations (SHAP) analysis further elucidated the contribution mechanisms and underlying interactions of material components on GPC compressive strength. Subsequently, a MOO framework minimized unit cost and CO₂ emissions while meeting compressive strength targets (15–70 MPa), solved using the NSGA-II algorithm for Pareto solutions and TOPSIS for decision-making. The Pareto-optimal solutions provide actionable guidelines for engineers to align GPC design with circular economy principles and low-carbon policies. This work advances sustainable construction practices by bridging AI-driven innovation with building materials, directly supporting global goals for waste valorization and carbon neutrality. Full article

In Global Navigation Satellite System (GNSS)-denied environments, opportunistic positioning using non-cooperative Low Earth Orbit (LEO) satellite signals has shown strong potential. However, dynamic platforms face challenges in maintaining sufficient satellite counts and favorable geometric distributions due to limited signal quality and short observation windows. To address this, we propose a fast satellite selection algorithm based on the Non-Dominated Sorting Whale Optimization Algorithm (NSWOA) for dynamic, multi-constellation LEO opportunistic navigation. By introducing Pareto non-dominated solutions, the algorithm balances Doppler Geometric Dilution of Precision (DGDOP), signal strength, residual visibility time, and receiver sensitivity. Through iterative optimization, it constructs a subset of satellites with minimal DGDOP while reducing computational burden, enabling real-time fusion and switching at the receiver end. We validate the algorithm through UAV-based experiments in dynamic scenarios. Compared to GWO, PSO, and NSGA-II, the proposed method achieves computation time reductions of 27.06%, 27.05%, and 68.57%, respectively. It also reduces the overall navigation solution time to 54.96% of that required when using all visible satellites, significantly enhancing real-time responsiveness and system robustness. These results demonstrate that the NSWOA-based satellite selection algorithm outperforms existing intelligent methods in both computational efficiency and optimization accuracy, making it well-suited for real-time, multi-constellation LEO dynamic opportunistic navigation. Full article

As early detection of voice disorders can significantly improve patients’ situation, the automated detection using Artificial Intelligence techniques can be crucial in various applications in this scope. This paper introduces a multi-objective bio-inspired, AI-based optimization approach for the automated detection of voice disorders. Different multi-objective evolutionary algorithms (the Non-dominated Sorting Genetic Algorithm (NSGA-II), Strength Pareto Evolutionary Algorithm (SPEA-II), and the Multi-Objective Evolutionary Algorithm based on Decomposition (MOEA/D)) have been compared to detect voice disorders by optimizing two conflicting objectives: error rate and the number of features. The optimization problem has been formulated as a wrapper-based algorithm for feature selection and multi-objective optimization relying on four machine learning algorithms: K-Nearest Neighbour algorithm (KNN), Random Forest (RF), Multilayer Perceptron (MLP), and Support Vector Machine (SVM). Three publicly available voice disorder datasets have been utilized, and results have been compared based on Inverted-Generational Distance, Hypervolume, spacing, and spread. The results reveal that NSGA-II with the MLP algorithm attained the best convergence and performance. Further, the conformal prediction is leveraged to quantify uncertainty in the feature-selected models, ensuring statistically valid confidence intervals for predictions. Full article

In this paper, we address the problem of obtaining bias-free and complete finite size approximations of the solution sets (Pareto fronts) of multi-objective optimization problems (MOPs). Such approximations are, in particular, required for the fair usage of distance-based performance indicators, which are frequently used in evolutionary multi-objective optimization (EMO). If the Pareto front approximations are biased or incomplete, the use of these performance indicators can lead to misleading or false information. To address this issue, we propose the Reference Set Generator (RSG), which can, in principle, be applied to Pareto fronts of any shape and dimension. We finally demonstrate the strength of the novel approach on several benchmark problems. Full article

Climate-change-induced extreme environments exacerbate pavement degradation in arid regions, where traditional concrete incurs 23~40% higher life-cycle costs due to premature cracking. Particularly in the Gobi Desert, concrete pavements suffer from conflicting performance requirements—high flexural-to-compressive strength ratio (R_f/R_c), low shrinkage, and controlled porosity—with traditional design methods failing to address multi-objective trade-offs. Existing optimization methods have proven insufficient for such complex environments, with conventional approaches addressing only individual parameters or employing subjective weighting techniques that fail to capture the interrelated nature of critical performance indicators. This study develops an integrated optimization framework combining Response Surface Methodology (RSM), Non-dominated Sorting Genetic Algorithm III (NSGA-III), Criteria Importance Through Intercriteria Correlation (CRITIC) weighting, and VIšekriterijumsko KOmpromisno Rangiranje (VIKOR) decision-making to optimize the mix proportions water–cement ratio (W/C), sand ratio, and an air-entraining agent (AEA) for sustainable pavement concrete. Response Surface Methodology (RSM) analysis via Box–Behnken design revealed distinct parameter dominance: AEA exhibited the strongest non-linear effects on R_f/R_c and porosity, while W/C primarily governed shrinkage. NSGA-III generated 73 Pareto-optimal solutions, with CRITIC selecting an optimal mix (W/C = 0.35), sand ratio = 36%, AEA = 0.200%) validated experimentally (R_f/R_c = 0.141), shrinkage = 0.0446%, porosity = 2.82%. Microstructural characterization using scanning electron microscopy and low-field nuclear magnetic resonance (SEM/LF-NMR) demonstrated refined pore distribution and enhanced compactness. This framework effectively resolves trade-offs between performance indicators, providing a scientifically robust method for designing durable pavement concrete that reduces shrinkage by 13.0% and porosity by 13.5% compared to conventional mixes, lowering maintenance costs in arid regions. Full article

This study introduces an objective Bayesian approach for estimating the reliability of a multicomponent stress–strength model based on the Pareto distribution under an adaptive progressive Type-II censoring scheme. The proposed method is developed within a Bayesian framework, utilizing a reference prior with partial information to improve the accuracy of point estimation and to ensure the construction of a credible interval for uncertainty assessment. This approach is particularly useful for addressing several limitations of a widely used likelihood-based approach in estimating the multicomponent stress–strength reliability under the Pareto distribution. For instance, in the likelihood-based method, the asymptotic variance–covariance matrix may not exist due to certain constraints. This limitation hinders the construction of an approximate confidence interval for assessing the uncertainty. Moreover, even when an approximate confidence interval is obtained, it may fail to achieve nominal coverage levels in small sample scenarios. Unlike the likelihood-based method, the proposed method provides an efficient estimator across various criteria and constructs a valid credible interval, even with small sample sizes. Extensive simulation studies confirm that the proposed method yields reliable and accurate inference across various censoring scenarios, and a real data application validates its practical utility. These results demonstrate that the proposed method is an effective alternative to the likelihood-based method for reliability inference in the multicomponent stress–strength model based on the Pareto distribution under an adaptive progressive Type-II censoring scheme. Full article

Cement-based thermoelectric materials are gaining popularity among materials scientists due to their robust mechanical characteristics and suitability for thermal energy harvesting in building applications. However, despite advancements in the development of these materials, a significant knowledge gap persists regarding their mechanical characterisation. This research aimed to enhance the thermoelectric performance of cement composites through the incorporation of graphene oxide (GO) and manganese dioxide (MnO₂), while ensuring adequate compressive strength was maintained. An experimental investigation was conducted to simultaneously assess both properties of cement composites using identical specimens. Additionally, microstructural analysis of the samples was performed to further understand the integrated development of these two properties. To evaluate the integrative properties, a Pareto analysis was performed to identify the Pareto-optimal solutions for specific applications. Additionally, a new index, termed the Thermoelectric Strength Index (TSI), was developed to compare materials in applications where both thermoelectric efficiency and mechanical robustness are important. The findings indicated that while both GO and MnO₂ enhanced the thermoelectric properties of cement, their reactions with the cement phases produced distinct relationships with compressive strength, especially when GO and MnO₂ were added together. The TSI demonstrated that MnO₂ was superior for simultaneously enhancing mechanical strength and thermoelectric performance, with the 7.5 wt.% formulation yielding the best results. This study demonstrates the complex interrelationship between the mechanical strength and thermoelectric properties of the investigated fillers, underscoring the necessity for a holistic approach in the development of thermoelectric cement composites. Full article

This study presents a comprehensive analysis and optimization methodology for bridge-connected modulators in coaxial magnetic gears. A novel harmonic modeling method incorporating magnetic saturation through permeability convolution matrices and multiple-layer radial subdivision is developed, achieving computational efficiency 20 times greater than finite element analysis with comparable accuracy (deviation < 3.2%). The research establishes an electromagnetic–structural coupling framework that captures the complex interactions between the magnetic field distribution and mechanical deformation, revealing critical trade-offs between electromagnetic performance and structural integrity. Multi-objective optimization using an improved NSGA-II algorithm identifies Pareto-optimal solutions balancing torque density, structural safety, efficiency, and thermal stability. Experimental testing validates that bridge width ratios between 0.05 and 0.07 provide optimal performance, delivering torque densities exceeding 80 kNm/m³ while maintaining stress ratios below 0.65 of material yield strength. Thermal analysis demonstrates that optimized configurations maintain operating temperatures below 70 °C with reduced thermal gradients. Vibration characteristics exhibit a strong correlation with bridge width, with wider bridges providing enhanced stability at higher speeds. The findings establish practical design guidelines for high-performance magnetic gears with improved reliability and manufacturability, advancing the fundamental understanding of electromagnetic–structural interactions in field-modulated magnetic gear systems. Full article

The densification of yellow poplar (Liriodendron tulipifera) has emerged as a critical area of research, driven by its desirable properties and broad potential applications. This study investigated the effects of four densification parameters using a 2⁴ full factorial design to evaluate their impact on physical and mechanical properties. Analysis of variance (ANOVA) and Pareto analyses identified the compression ratio as the most influential factor, significantly affecting bending strength, compression strength, hardness, and spring-back behavior. Pressing temperature was the second most significant factor, with higher levels positively impacting mechanical properties. However, increasing the pre-steaming treatment duration from 30 to 60 min at 130 °C had a detrimental effect on strength and spring-back performance, particularly at a 50% compression ratio. Pressing time showed no significant effect on strength properties but contributed positively to hardness and spring-back behavior at higher levels. Several significant factor interactions were observed, further influencing the outcomes. Differences in density profiles were notable across compression ratios, with higher ratios producing more uniform distributions. Under optimal parameters, densification increased compression strength by 117%, bending strength by 60%, and hardness by 154% compared to undensified control samples, demonstrating the potential of densified yellow poplar (Liriodendron tulipifera) for high-performance applications. Full article

Copper joints are indispensable in electronics and the electrical power industry due to their predominant usage in battery pack manufacturing for electric vehicle). Traditional joining methods are often limited by oxidation-related challenges. Recent efforts have focused on addressing these limitations by employing solid-state techniques like ultrasonic welding (USW) for joining similar metals. USW presents attractive advantages such as a lower processing temperature and shorter weld time. This study investigates the ultrasonic welding of Cu-Cu joints with a thickness of 0.5 mm, focusing on both mechanical and metallurgical properties. The influence of key process parameters, such as the welding time, pressure and vibration amplitude, was examined in relation to the welding energy and lap shear strength. Additionally, the relationship between the input energy and lap shear strength was explored. A Pareto chart analysis revealed the standardized effects of these parameters on the welding energy and average lap shear strength. The welding time had a significant influence on the welding energy, while the vibration amplitude had the greatest impact on the joint strength. Longer weld times of 2.50 to 4 s yielded a higher lap shear strength, averaging up to 2.30 kN. Notably, a higher lap shear strength was achieved at lower welding energy levels. Full article

The use of precast concrete pipes for water and sewage transportation systems is a very important element of a country’s infrastructure. The main aim of this study was to investigate the effects of concrete’s compressive strength and reinforcement levels on the mechanical performance of spun-cast full-scale precast concrete pipes in the local construction industries of developing countries. A test matrix was adopted using a full 3² factorial design. The studied concrete’s compressive strength was 20, 30, and 40 MPa, and reinforcement levels were 60%, 80%, and 100%, representing low, medium, and high levels, respectively. The medium level of reinforcement represented the reinforcement requirement of ASTM C76 in concrete pipes. A total of eighteen full-scale pipes of 450 mm diameter were cast in an industrial precast pipe unit using a spin-casting technique and were tested under a three-edge bearing load. The experimental results showed that the crack load and ultimate load of the tested pipes increased with higher levels of concrete strength and reinforcement levels. For example, an approximately 35% increase in the 0.30 mm crack load was observed when the concrete strength increased from 20 MPa to 30 MPa for all tested levels of reinforcement. Similarly, around a 19% increase in ultimate load was observed for pipes with 80% reinforcement compared to identical pipes with 60% reinforcement. It was found that the pipe class, as per ASTM C76, is highly dependent on the concrete strength and reinforcement levels. All of the pipes exhibited the development of flexural cracks at critical locations (crown, invert, and springlines). Moreover, concrete pipes cast with low-level strength and reinforcement also showed signs of crushing at the crown location near to the pipe failure. The analysis of variance (ANOVA) results showed that the main factors (compressive strength and reinforcement levels) were significantly affected by the cracking loads of precast pipes. No significant effect of the interaction of factors was observed on the crack load response. However, interaction factors, along with main factors, have significant effects on the ultimate load capacity of the concrete pipes, as indicated by the F-value, p-value, and Pareto charts. This study made an effort to illustrate and optimize the mechanical performance of pipes cast with various concrete strengths and reinforcement levels to facilitate the efficient use of materials for more resilient pipe infrastructure. Moreover, the exact optimization of concrete strength and reinforcement level for the desired pipe class will make the pipe design economical, leading to an increased profit margin for local spin-cast pipe fabricators without compromising the pipe’s quality. Full article

The utilization of Harris Hawks Optimization (HHO) for Multi-Objective Optimal Power Flow (MaO-OPF) challenges presented in this paper is both novel and compelling, as this approach has not been previously applied to these types of optimization problems. HHO, which shares characteristics with ant behavior, demonstrates significant strength in addressing high-dimensional, nonlinear optimization issues within power systems. In this study, HHO is implemented on an IEEE 30-bus power system, optimizing six competing objectives: minimizing total fuel cost, emissions, active power loss, reactive power loss, reducing voltage deviation, and enhancing voltage steady state. The effectiveness of HHO is assessed by comparing its performance to two alternative methods, MOEA/D-DRA and NSGA-III. Experimental results reveal that solutions derived from HHO exhibit superior convergence, enhanced diversity maintenance, and higher quality Pareto-optimal solutions compared to the MOEA/D trail algorithms. The research breaks new ground in the application of the Harris Hawks Optimization (HHO) algorithm to the Multi-Objective Optimal Power Flow (MaO-OPF) problem. The restructuring not only incorporates self-adaptive constraint-handling techniques and dynamic exploration exploitation strategies, but also addresses the more pressing requirements of modern power systems with even better convergence, and both sequential and global computational efficiency than existing skill. This approach proves to be a powerful and effective solution for addressing the complex challenges associated with MaO, enabling power systems to manage multiple conflicting objectives more efficiently. Full article

Stope structural parameters are of great significance for the safe production of mines. To efficiently and safely mine steeply dipping ultra-thick ore bodies, the K. Kegel strength design formula and limit analysis method were used to calculate a reasonable range of stope parameters. Considering the actual mining conditions, the mechanical responses under different structural parameters were obtained through numerical simulations based on a central composite experimental design. A regression model for maximum tensile stress, maximum compressive stress, and maximum vertical displacement was established using the second-order response surface method. The regression model was then used as the objective function, and multi-objective optimization was performed using a simulated annealing algorithm to obtain the Pareto optimal solution set. Based on practical engineering needs, a stope span of 15.0 m, a pillar width of 10.0 m, and a roof thickness of 11.9 m were determined as the optimal structural parameters, achieving a balance between safety and economic efficiency. Full article

The design of high-aspect-ratio wings enhances the flight efficiency of UAVs but also introduces significant aeroelasticity challenges. The efficient optimization of wing structures in complex environments has become critical. To address the current challenges in balancing wing strength with lightweight structural designs, this study proposed an intelligent solution method for optimizing wing dimensions and structural layout. Driven by mechanical simulation data, the method established a mapping relationship between the structural layout and dimensions of the wing and its bending stiffness. This approach was further enhanced by the mind evolution algorithm (MEA) to optimize the solution performance of the surrogate model. The wing structure optimization model was established using the multi-objective grey wolf optimizer (MOGWO) based on the surrogate model for search and optimization. This study focused on the composite material wing of a long-endurance unmanned aerial vehicle (UAV). The established MEA-BP surrogate model demonstrated high computational efficiency, with the prediction error standard deviation (STD) of wing deflection not exceeding 0.495 mm. The optimization model required 175 s to calculate the Pareto front solutions. The optimized structure resulted in a 28.32% increase in wing equivalent stiffness, and weight only increased by 6.67% compared to the original structure. These results showcased the effectiveness of the proposed method and validated the feasibility of integrating intelligent optimization algorithms and machine learning in the field of aircraft design. Full article