Search Results (72)

The flying-wing configuration offers inherent advantages in aerodynamic efficiency and stealth; however, conventional fixed-wing designs face fundamental performance trade-offs when tasked with multi-role missions. This paper introduces a multidisciplinary design optimization (MDO) framework for a morphing wing unmanned aerial vehicle (UAV) to overcome this limitation. The proposed UAV integrates four complementary morphing strategies—shear-type variable sweep, variable span, morphing wingtip, and a continuously variable camber trailing edge—to adapt its geometry for different flight phases. An automated parametric modeling platform is developed, enabling the dynamic generation of 3D CAD models driven by design variables. This geometry is coupled with a suite of analysis modules for aerodynamics, propulsion, weight estimation, flight performance, and radar cross-section. The multi-mission profile, including takeoff, climb, cruise, turning, and landing, is decomposed into several phase-specific single-objective optimization subproblems, which are solved using an elitist real-coded genetic algorithm. The results quantify the optimal morphing configurations for each phase, demonstrating significant performance gains over the baseline, such as a 17% increase in range. Critically, the study analyzes the trade-off between aerodynamic benefits and the weight penalty of morphing mechanisms, revealing that both range and maneuverability are the most sensitive to the added weight. The proposed framework uses mission-phase-specific optimum geometries to define the required morphing envelope, actuation ranges, and net performance benefit of a candidate morphing flying-wing UAV after considering mechanism-induced mass penalties. This framework provides a quantitative basis for mission-driven morphing decisions and establishes a viable approach for designing highly adaptive next-generation UAVs. Full article

In many industrial applications, the significant differences in flow rates and physical properties between the hot and cold media of plate heat exchangers (PHEs) often lead to differentiated performance requirements. Asymmetric structural design is an effective approach to addressing these specific needs. In this paper, a novel fish-scale corrugated asymmetric plate heat exchanger (APHE) was designed and multi-objective optimization was performed based on the objectives of minimizing the water side pressure drop, ΔP, and maximizing the overall heat transfer coefficient, K. Numerical simulations of the fish-scale corrugated APHE were conducted with the Box–Behnken Design (BBD) in the Response Surface Methodology (RSM). The corrugation angle, corrugation pitch, and protrusion ratio were selected as geometric variables. Through Analysis of Variance (ANOVA), significant regression models were established for the two competing performance indicators. Subsequently, Pareto optimal solutions were identified using the fast and elitist non-dominated sorting genetic algorithm (NSGA-II). A comparison of the performances reveals that the novel APHE reduces ΔP by 47.13% and increases K by 5.77% compared to the original chevron-type PHE. Further analysis of the simulation data reveals that the convective heat transfer coefficient on the refrigerant side is increased by 24.06%. These findings substantiate the benefits of the asymmetric feature of the fish-scale protrusion and offer a comprehensive and effective design strategy for APHEs. Full article

Wireless power transfer (WPT), characterized by its excellent insulation properties and ease of maintenance, has recently emerged as a promising solution to the power supply challenges faced by online monitoring equipment on high-voltage transmission towers in complex environments. Existing research primarily relies on regular, closely wound solenoids to power these monitoring devices; however, this approach often makes it difficult to optimize the magnetic field distribution to maximize mutual inductance, thereby limiting transmission efficiency and power and hindering lightweight design. To address these issues, this paper proposes an optimized design scheme for variable-pitch (non-uniform) domino WPT coils based on insulator string structures. First, a parameter calculation model utilizing segmented current analysis is constructed to accurately determine the inductance of non-uniform solenoids, with simulations confirming an error rate below 5%. Subsequently, by integrating domino multi-coil theory into an elitist non-dominated sorting genetic algorithm (NSGA-II), dual-objective optimization is performed. Targeting maximum transmission efficiency and output power under spatial and insulation constraints, a set of Pareto optimal solutions is derived. Ultimately, a 113.7 W insulator domino coil WPT system prototype is constructed to validate the design’s stability. The proposed system achieves a maximum efficiency of 85.73%, with a single-stage load delivering up to 97.48 W. Full article

The growing penetration of electric logistics vehicles (ELVs) poses a significant challenge to electric utility site selection. This paper addresses the problem of joint site selection for electric logistics vehicle charging and swapping stations (CSSs). First, a joint site selection model is introduced to characterize the problem, and an improved genetic algorithm (IGA) is designed to solve this model. Derived from the standard genetic algorithm (SGA), the IGA incorporates local search operations, evolutionary inversion operations, and an elitist preservation strategy to enhance performance. On this basis, small-scale numerical simulations are conducted to determine the optimal parameters, thereby guaranteeing optimal algorithmic efficiency. Subsequently, large-scale numerical simulations are performed, with key indicators recorded including the optimal routing length, battery replenishment frequency, number of stations, number of ELVs, and solution time. Finally, analysis across three efficiency levels demonstrates that joint siting improves distribution efficiency by 39.38%, increases grid electricity sales by 46.89%, and reduces total transportation costs by 26.28%, with the optimization scheme validated across six different numerical scenarios. Overall, the joint site selection proposed in this paper has enhanced the benefits of relevant stakeholders and provided a reference for building a low-carbon transportation chain. Full article

K-Means is a well-known algorithm for unsupervised clustering, very often used due to its simplicity and efficiency. Its long-time widespread use has stimulated researchers to investigate its properties further. A critical property concerns K-Means’s strong dependence on the seeding method adopted to initialize centroids. Poor initialization causes K-Means to get stuck in a local sub-optimal solution. This paper proposes DPCCs—Density Peaks of Candidate Centroids—a novel seeding method for K-Means. DPCC rests on genetic concepts and density peaks to define an initialization solution close to the optimal one. First, a population of $J$ elitist candidate solutions, that is, solutions capable of yielding a reduced clustering cost, is built. Although none of these particular solutions can be near the optimal one, candidate centroids, as experimentally confirmed, tend to thicken around ground truth centroids. Therefore, subsequent generations of the population are created by repeating the k-nearest neighbors (kNNs) procedure for different values of the k parameter, and estimating density through the reverse nearest neighbors (RNNs) relationship of each centroid. Centroid density peaks are then exploited to rearrange the population solutions toward extracting a candidate solution, which is finally optimized by K-Means. The paper describes the design and operation of DPCC, which is currently implemented in parallel Java. The clustering effectiveness of DPCC is demonstrated by applications to both benchmark and real-world datasets. Results are compared with those of other competing algorithms. Full article

Newton–Raphson-Based Optimizer (NRBO) is a recently proposed metaheuristic that combines mathematical search rules with population-based optimization; however, it still suffers from an insufficient balance between global exploration and local exploitation, limited local refinement accuracy, and weak adaptability in complex optimization scenarios. To address these limitations, this paper proposes an Improved Newton–Raphson-Based Optimizer (INRBO), which enhances the original framework through a multi-strategy augmentation mechanism. Specifically, INRBO integrates three complementary strategies: (1) an adaptive differential operator with a linearly decaying scaling factor to dynamically regulate exploration and exploitation throughout the search process; (2) a quadratic interpolation strategy that exploits high-quality individuals to improve local search directionality and precision; and (3) an elitist population genetic strategy that preserves superior solution characteristics while maintaining population diversity and preventing premature convergence. The performance of INRBO is systematically evaluated on the CEC2017 benchmark suite under multiple dimensions and compared with several state-of-the-art metaheuristic algorithms. Experimental results demonstrate that INRBO achieves superior optimization accuracy, convergence efficiency, and robustness across unimodal, multimodal, hybrid, and composite functions, which is further confirmed by statistical significance tests. In addition, INRBO is applied to mobile robot path planning in grid-based environments of different scales, where it consistently generates shorter, smoother, and safer paths than competing algorithms. Overall, the proposed INRBO provides an effective and robust optimization framework for global continuous optimization problems and real-world engineering applications, demonstrating both strong theoretical value and practical applicability. Full article

This article describes a global structure optimization methodology for microelectromechanical system devices based on a multi-objective elitist genetic algorithm. By integrating a parameterized model with a multi-objective evolutionary framework, the approach can efficiently explore design space and concurrently optimize multiple metrics. A differential capacitive MEMS accelerometer is presented to demonstrate the method. Four key objectives, including resonant frequency, static capacitance, dynamic capacitance, and feedback force, are simultaneously optimized to enhance sensitivity, bandwidth, and closed-loop driving capability. After 25 generations, the algorithm converged to a uniformly distributed Pareto front. The experimental results indicate that, compared with the initial design, the sensitivity-oriented design achieves a 56.1% reduction in static capacitance and an 85.5% improvement in sensitivity. The global multi-objective optimization achieves a normalized hypervolume of 35.8%, notably higher than the local structure optimization, demonstrating its superior design space coverage and trade-off capability. Compared to single-objective optimization, the multi-objective approach offers a superior strategy by avoiding the limitation of overemphasizing resonant frequency at the expense of other metrics, thereby enabling a comprehensive exploration of the design space. Full article

Although the elitist genetic algorithm (EGA) is an approach for the optimal design of pixelated metasurfaces, it is necessary to convert a two-dimensional (2D) metasurface to a one-dimensional array. This ignores the effects of the mutation on neighboring data in 2D metasurfaces, and hinders the rapid convergence of the algorithms. Therefore, we propose the 2D mutation-based EGA (2DM-EGA) to optimally design the linear-to-circular (LTC) polarization conversion metasurface (PCM). Compared with EGA, 2DM-EGA can significantly improve the convergence rate. Furthermore, combined with the proposed intuitive reward-based fitness function and circular polarization discrimination pertaining to an ellipticity angle β, 2DM-EGA, programmed in Python (2023 version), is used to accomplish optimal targets. Finally, the simulated operating band of the optimized metasurface varies from 8.16 GHz to 11.5 GHz with a reduced ellipticity angle β/π ≥ 0.15 and a relative bandwidth of 33.5%, which suggests that the optimized metasurface realizes the broadband LTC polarization conversion. The measured results are in excellent accord with the simulations validating 2DM-EGA for the optimal design of transmission-type wideband LTC PCMs. Additionally, the physical mechanism of the design is expounded. Full article

This paper presents an enhanced hub location model tailored to port–hinterland logistics planning, grounded in the Capacitated Multiple-Allocation Hub Location Problem (CMAHLP). The formulation incorporates nonlinear cost structures, hub-specific operating costs, adaptive capacity constraints, and a feasibility condition based on the Social Net Present Value (NPVsocial) to support the design of intermodal freight networks under asymmetric spatial and socio-environmental conditions. The empirical case focuses on Spain, leveraging its strategic position between Asia, North Africa, and Europe. The model includes four major ports—Barcelona, Valencia, Málaga, and Algeciras—as intermodal gateways connected to the 47 provinces of peninsular Spain through calibrated cost matrices based on real distances and mode-specific road and rail costs. A Genetic Algorithm is applied to evaluate 120 scenarios, varying the number of active hubs (4, 6, 8, 10, 12), transshipment discounts (α = 0.2 and 1.0), and internal parameters. The most efficient configuration involved 300 generations, 150 individuals, a crossover rate of 0.85, and a mutation rate of 0.40. The algorithm integrates guided mutation, elitist reinsertion, and local search on the top 15% of individuals. Results confirm the central role of Madrid, Valencia, and Barcelona, frequently accompanied by high-performance inland hubs such as Málaga, Córdoba, Jaén, Palencia, León, and Zaragoza. Cities with active ports such as Cartagena, Seville, and Alicante appear in several of the most efficient network configurations. Their recurring presence underscores the strategic role of inland hubs located near seaports in supporting logistical cohesion and operational resilience across the system. The COVID-19 crisis, the Suez Canal incident, and the persistent tensions in the Red Sea have made clear the fragility of traditional freight corridors linking Asia and Europe. These shocks have brought renewed strategic attention to southern Spain—particularly the Mediterranean and Andalusian axes—as viable alternatives that offer both geographic and intermodal advantages. In this evolving context, the contribution of southern hubs gains further support through strong system-wide performance indicators such as entropy, cluster diversity, and Pareto efficiency, which allow for the assessment of spatial balance, structural robustness, and optimal trade-offs in intermodal freight planning. Southern hubs, particularly in coordination with North African partners, are poised to gain prominence in an emerging Euro–Maghreb logistics interface that demands a territorial balance and resilient port–hinterland integration. Full article

Active space debris removal is now integral to modern space exploration. In order to address the problem of a heterogeneous satellite swarm with different payloads carrying out the emergency active removal of space debris, this paper proposes a Multi-type Chromosome Fast Elitist Non-Dominated Sorting Genetic Algorithm (MC-NSGA-II). The algorithm is designed to enable the satellite swarm to execute multiple coupled tasks in succession with improved optimization efficiency. An arbitrary execution order may result in deadlock, where one or more satellites become trapped in an infinite waiting loop. In order to address the heterogeneous problem of satellites and task coupling constraints, a multi-type chromosome coding strategy is developed. To evaluate different allocation strategies, three optimization objectives—time consumption, fuel consumption, and task balance—are introduced. To align with the multi-type chromosome coding strategy, two distinct sorting methods are developed for crossover and mutation operations, ensuring that all offspring individuals meet the constraints. Additionally, the algorithm incorporates a dynamic parameter-setting strategy to enhance solution efficiency. Finally, comparative simulations validate the effectiveness and superiority of the proposed method. The results show that the high-quality solution search ability of the MC-NSGA-II algorithm is 23.07% higher than that of the standard NSGA-II algorithm. Full article

Energy storage technology is crucial for promoting the replacement of traditional energy with renewable energy and regulating the energy supply–demand relationship. This paper investigates a triplex-tube thermal energy unit storage to solve the intermediate heat storage and heat transfer problem of hot water supply and demand in clean heating systems. A multi-objective optimization method based on the elitist non-dominated sorting genetic algorithm (NSGA-II) was utilized to optimize the geometric dimensions (inner tube radius $r_1$, casing tube radius $r_2$, and outer tube radius $r_3$), focusing on heat transfer efficiency ($\epsilon$), heat storage rate ($P_t$), and mass (M). On this basis, the influence of the optimization variables was analyzed. The optimized configuration ($r_1=0.014$ m, $r_2=0.041$ m, and $r_3=0.052$ m) was integrated into a modular design, achieving a 2.12% improvement in heat transfer efficiency and a 73.23% increase in heat storage rate. Experimental results revealed that higher heat transfer fluid (HTF) temperatures significantly reduce heat storage time, while HTF flow rate has a minimal impact. Increasing the heat release temperature extends the phase change material (PCM) heat release duration, with the flow rate showing negligible effects. The system’s thermal supply capacity is susceptible to heat release temperature. Full article

An integrated solution that considers the shortening of scheduling and the planning of resource integration was conceived. The proposed method allocates the resources and the execution mode costs effectively in order to minimize the project duration and the cost of construction activities. Costs are managed based on the management of the costs already in place for people and those costs involved in the modes of execution of the project, trying to decrease the cost as much as possible. The proposed method is used in order to achieve the maximum potential and minimum costs during a project, including direct costs, indirect costs, and delay penalties. Furthermore, it finds a balance between the costs of acquiring and releasing human resources. The most interesting aspect of the proposed method is that it suggests addressing problems with resource planning and project scheduling simultaneously under uncertainty. FS theory is used to model project activity duration and cost uncertainty in the method. In addition, the above approach involves a genetic algorithm (GA) for schedule optimization. The optimization method utilizes a GA as an optimization approach to identify a set of non-dominated solutions. In this paper, we discuss how string-based multi-object optimization can be solved with ES using the elitist non-dominated sorting genetic algorithm (NSGA-II). The method is implemented in Python (v3.12.9), the computer programming language, as a standalone automated computational tool for schedule optimization in order to subsequently reschedule. Full article

This study develops a novel Lyrebird Optimization Algorithm (LOA), a technique inspired by the wild behavioral strategies of lyrebirds in response to potential threats. In a two-area interconnected power system that includes non-reheat thermal stations, this algorithm is applied to handle load frequency control (LFC) by optimizing the parameters of a Proportional–Integral–Derivative controller with a filter (PIDn). This study incorporates generation rate constraints (GRCs). The efficiency of the provided LOA-PIDn is evaluated through simulations under various disturbance scenarios and is compared against other well-established optimization techniques, including the Ziegler–Nichols (ZN), genetic algorithm (GA), Bacteria Foraging Optimization Algorithm (BFOA), Firefly Approach (FA), hybridized FA and pattern search (hFA–PS), self-adaptive multi-population elitist Jaya (SAMPE-Jaya)-based PI/PID controllers, and Teaching–Learning-Based Optimizer (TLBO) IDD/PIDD controllers. The results demonstrate the LOA’s ability to minimize the integral of time multiplied by absolute error (ITAE) and achieve significantly lower settling times for the two-area frequencies and transferred power variances in comparison with other methods. The comprehensive comparison and the inclusion of real-world constraints validate the LOA as a robust and effective tool for addressing complex optimization challenges in modern power systems. Full article

Vs30, the average shear wave velocity in the uppermost 30 m, is a critical parameter in seismic hazard analysis. In the Philippines, the Refraction Microtremor (ReMi) survey is the standard method for Vs30 Estimation. This study evaluates the efficiency of using an elitist Genetic Algorithm (GA) to invert Horizontal-to-Vertical Spectral Ratio (HVSR) data as an alternative approach. Unlike ReMi surveys, which require geophone arrays, HVSR surveys use a single-unit three-component microtremor seismograph, enabling faster and broader data collection. Analysis of 174 HVSR and 52 ReMi datasets from the Greater Metro Manila Area (GMMA) and Leyte Province revealed strong correlations between estimated and measured Vs30 values. The overall match rates for soil profile classification under the National Structural Code of the Philippines (NSCP 2015) were 76% in GMMA and 81% in Leyte, with R-squared values of 0.885 and 0.806, respectively. Additionally, the relationship between the fundamental site period and estimated Vs30 values was explored. The R-squared values of 0.772 for GMMA and 0.707 for Leyte indicate a strong correlation and demonstrate the expected inverse relationship between the two variables. Given the Philippines’ high seismic activity, this method provides an efficient means to enhance seismic hazard mapping, improving earthquake preparedness and mitigation. Full article

This paper proposes a cutting trajectory planning method for boom-type roadheaders using an improved Nondominated Sorting Genetic Algorithm II (NSGA-II) with an elitist strategy. Existing methods often overlook constraints related to cutterhead dimensions and target sections, affecting section formation quality. We develop a kinematic model for coordinate transformations and design a simplified cutterhead and constraint model to generate feasible cutting points. Bi-objective functions—minimizing cutting trajectory length and turning angle—are formulated as a bi-objective traveling salesman problem (BO-TSP) with adjacency constraints. NSGA-II is adapted with enhancements in adjacency constraint handling, population initialization, and genetic operations. Simulations and experiments demonstrate significant improvements in convergence speed and computation time. Virtual cutting experiments confirm trajectory feasibility under varying postures, achieving high formation quality. A comparison of planned and tracked trajectories shows a maximum deviation of 23.879 mm, supporting autonomous cutting control. This method advances cutting trajectory planning for roadway section formation and autonomous roadheader control. Full article