Search Results (249)

As an important transportation hub connecting the two sides of the Yellow River, the Yellow River Grand Bridge is of great significance for strengthening regional exchanges and promoting the high-quality development of the Yellow River Basin. However, due to the complex terrain, changeable climate, high sediment concentration, long construction duration, complicated process, strong dynamic, and many factors affecting construction. It often brings many problems, including low quality, waste of resources, and environmental pollution, which makes it difficult to achieve the balance of multiple objectives at the same time. Therefore, it is very important to carry out multi-objective optimization research on the construction of the Yellow River Grand Bridge. This paper takes the Yellow River Grand Bridge on a highway as the research object and combines the concept of “green construction” and the national policy of “carbon neutrality and carbon peaking” to construct six major construction projects, including construction time, cost, quality, environment, resources, and carbon emission. Then, according to the multi-attribute utility theory, the objectives of different attributes are normalized, and the multi-objective equilibrium optimization model of construction time-cost-quality-environment-resource-carbon emission of the Yellow River Grand Bridge is obtained; finally, in order to avoid the shortcomings of a single algorithm, the particle swarm optimization algorithm and the simulated annealing algorithm are combined to obtain the simulated annealing particle swarm optimization (SA-PSO) algorithm. The multi-objective equilibrium optimization model of the construction of the Yellow River Grand Bridge is solved. The optimization result is 108 days earlier than the construction period specified in the contract, which is 9.612 million yuan less than the maximum cost, 6.3% higher than the minimum quality level, 11.1% lower than the maximum environmental pollution level, 4.8% higher than the minimum resource-saving level, and 3.36 million tons lower than the maximum carbon emission level. It fully illustrates the effectiveness of the SA-PSO algorithm for solving multi-objective problems. Full article

This study proposes an RBF-PSO hybrid framework for efficient inversion analysis of hydration heat parameters in mass concrete temperature fields, addressing the computational inefficiency and accuracy limitations of traditional methods. By integrating a Radial Basis Function (RBF) surrogate model with Particle Swarm Optimization (PSO), the method reduces reliance on costly finite element simulations while maintaining global search capabilities. Three objective functions—integral-type (F₁), feature-driven (F₂), and hybrid (F₃)—were systematically compared using experimental data from a C40 concrete specimen under controlled curing. The hybrid F₃, incorporating Dynamic Time Warping (DTW) for elastic time alignment and feature penalties for engineering-critical metrics, achieved superior performance with a 74% reduction in the prediction error (mean MAE = 1.0 °C) and <2% parameter identification errors, resolving the phase mismatches inherent in F₂ and avoiding F₁’s prohibitive computational costs (498 FEM calls). Comparative benchmarking against non-surrogate optimizers (PSO, CMA-ES) confirmed a 2.8–4.6× acceleration while maintaining accuracy. Sensitivity analysis identified the ultimate adiabatic temperature rise as the dominant parameter (78% variance contribution), followed by synergistic interactions between hydration rate parameters, and indirect coupling effects of boundary correction coefficients. These findings guided a phased optimization strategy, as follows: prioritizing high-precision calibration of dominant parameters while relaxing constraints on low-sensitivity variables, thereby balancing accuracy and computational efficiency. The framework establishes a closed-loop “monitoring-simulation-optimization” system, enabling real-time temperature prediction and dynamic curing strategy adjustments for heat stress mitigation. Robustness analysis under simulated sensor noise (σ ≤ 2.0 °C) validated operational reliability in field conditions. Validated through multi-sensor field data, this work advances computational intelligence applications in thermomechanical systems, offering a robust paradigm for parameter inversion in large-scale concrete structures and multi-physics coupling problems. Full article

Composite sandwich structures play a significant role in various engineering applications due to their excellent strength-to-weight ratio, durability, fatigue life, acoustic performance, damping characteristics, stealth performance, and energy absorption capabilities. The optimization of these structures results in enhancing their mechanical performance, weight reduction, cost-effectiveness, and sustainability. This review provides a comprehensive analysis of recent advancements in the optimization techniques applied in the case of composite sandwich structures, focusing on structural configuration, facesheets, and innovative cores design, loading conditions, analysis methodologies, and practical applications. Various optimization procedures, single- and multi-objective algorithms, Genetic Algorithms (GAs), Particle Swarm Optimization (PSO), and Machine Learning (ML)-based optimization frameworks, as well as Finite Element (FE)-based numerical simulations, are discussed in detail. It highlights the role of core material and geometry, face sheet material selection, and manufacturing limitations in achieving optimal performance under static, dynamic, thermal, and impact loads under various boundary conditions. Furthermore, challenges such as computational efficiency, experimental validation, and trade-offs between structural weight and performance are examined. The findings of this review offer insights into the recent and future research directions of optimizing sandwich constructions, emphasizing the integration of advanced numerical techniques for analysis and efficient structural optimization. Full article

To enhance energy efficiency and reduce emissions in public transportation systems, this study proposes a novel electro-hydraulic energy-regenerative suspension system for urban buses. A comprehensive co-simulation framework was established to evaluate system performance. Targeting ride comfort and energy regeneration performance as dual optimization objectives, we conducted systematic parameter analysis through design-of-experiments methodology to identify critical structural parameters. To streamline multi-objective optimization processes, a particle swarm optimization–back propagation (PSO-BP) neural network surrogate model was developed to approximate the complex co-simulation system. Subsequent non-dominated sorting genetic algorithm II (NSGA-II) implementation enabled effective multi-objective optimization of key suspension parameters. Comparative simulations revealed that the optimized configuration achieves the following: (1) maintains ride comfort within human perception thresholds despite slight performance reduction, (2) enhances energy recovery efficiency, and (3) improves roll stability characteristics. These findings demonstrate the proposed system’s capability to balance passenger comfort with energy conservation and safety requirements. Full article

The paper introduces a new hybrid optimization algorithm, HWCAPSO, for optimal distributed generator (DG) placement and soft-open point (SOP) size determination along with network reconfiguration. The hierarchical algorithm combining the Water Cycle Algorithm (WCA) and Particle Swarm Optimization (PSO) is introduced to solve this nonconvex problem. WCA excels in global exploration due to its water-cycle-inspired diversification, while PSO’s velocity-based update mechanism ensures rapid local convergence. Their hybrid synergy mitigates premature convergence in challenging problems. The proposed HWCAPSO algorithm uniquely integrates the global exploration capability of WCA with the local exploitation strength of PSO in a hierarchical framework, addressing the mixed-integer nonlinear programming (MINLP) challenges of simultaneous DG/SOP allocation and reconfiguration gap in existing hybrid methods. It aims to optimize total active power losses while fulfilling operational constraints such as voltage limits, thermal capacities, and radiality. The efficiency of the HWCAPSO is confirmed by exhaustive case studies from the 33-bus test system and the 69-bus test system, where its performance is compared with that of individual WCA and PSO. Findings show that HWCAPSO yields better loss reduction (up to 92.4% for the 33-bus network as and 92.7% for the 69-bus network), enhanced voltage profiles, as well as satisfactory convergence characteristics. Results are statistically validated over 30 independent runs, with 95% confidence intervals confirming robustness. The versatility of the algorithm to deal with intricate, multi-objective optimization applications make it an efficient option for real distribution network planning and operation. Full article

Photovoltaic systems are increasingly recognized as one of the most advanced, efficient, and rapidly developing methods of electricity generation, utilizing the limitless potential of solar radiation while offering environmentally sustainable solutions to contemporary energy challenges. However, despite their clear benefits, issues such as high initial investment costs and relatively low energy efficiency must be carefully addressed during the design phase. Key considerations include the quantity and type of panels, battery capacity and number, environmental conditions, site-specific factors, and the mathematical models and interconnection strategies of system components. This study proposes a two-stage optimization approach for standalone photovoltaic systems, employing three distinct optimization algorithms—NSGA-II, DEMO, and Particle Swarm Optimization—to minimize both the Loss of Load Probability (LLP) and the life cycle cost (LCC). In the second stage, optimal solutions from the Pareto front are evaluated using three multi-criteria decision-making techniques: the hybrid AHP-TOPSIS method, VIKOR, and PROMETHEE. The proposed framework is applied to systems with storage batteries designed for deployment in three Cypriot cities, aiming to meet energy demands of 10, 15, and 20 kWh. The findings reveal a strong correlation between economic and energy performance and the degree of load coverage, with the combination of the DEMO algorithm and the AHP-TOPSIS method emerging as the most effective solution. Full article

Choosing the optimal path in planning is a complex task due to the numerous options and constraints; this is known as the trip design problem (TTDP). This study aims to achieve path optimization through the weighted sum method and multi-criteria decision analysis. Firstly, this paper proposes a weighted sum optimization method using a comprehensive evaluation model to address TTDP, a complex multi-objective optimization problem. The goal of the research is to balance experience, cost, and efficiency by using the Analytic Hierarchy Process (AHP) and Entropy Weight Method (EWM) to assign subjective and objective weights to indicators such as ratings, duration, and costs. These weights are optimized using the Lagrange multiplier method and integrated into the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) model. Additionally, a weighted sum optimization method within the Traveling Salesman Problem (TSP) framework is used to maximize ratings while minimizing costs and distances. Secondly, this study compares seven heuristic algorithms—the genetic algorithm (GA), particle swarm optimization (PSO), the tabu search (TS), genetic-particle swarm optimization (GA-PSO), the gray wolf optimizer (GWO), and ant colony optimization (ACO)—to solve the TOPSIS model, with GA-PSO performing the best. The study then introduces the Lagrange multiplier method to the algorithms, improving the solution quality of all seven heuristic algorithms, with an average solution quality improvement of 112.5% (from 0.16 to 0.34). The PSO algorithm achieves the best solution quality. Based on this, the study introduces a new variant of PSO, namely PSO with Laplace disturbance (PSO-LD), which incorporates a dynamic adaptive Laplace perturbation term to enhance global search capabilities, improving stability and convergence speed. The experimental results show that PSO-LD outperforms the baseline PSO and other algorithms, achieving higher solution quality and faster convergence speed. The Wilcoxon signed-rank test confirms significant statistical differences among the algorithms. This study provides an effective method for experience-oriented path optimization and offers insights into algorithm selection for complex TTDP problems. Full article

Virtual coupling (VC) technology, which determines the safe interval between trains based on relative braking distance, offers a promising solution by enabling tighter yet safe train-following intervals through advanced communication and control strategies. This paper focuses on addressing the virtually coupled train set (VCTS) control problem within the framework of distributed model predictive control (DMPC), in which train dynamics model incorporates uncertainties in basic resistance and control inputs, with an adaptive mechanism (ADM) designed to limit errors caused by external disturbances. A multi-objective cost function is established, considering position error, speed error, and ride comfort, while constraints such as actuator saturation, speed limits, and safe tracking distance are enforced. Particle swarm optimization (PSO) is employed to solve the non-convex optimization problem globally. Simulation experiments validate the effectiveness of the proposed method, demonstrating stable operation of VCTS under various initial conditions and the ability to handle uncertainties through the adaptive mechanism. The results show that the proposed DMPC approach significantly reduces tracking errors and improves ride comfort, highlighting its potential for enhancing railway capacity and operational efficiency. Full article

This paper introduces a novel framework for designing wideband antenna arrays using self-similar Eisenstein fractal geometries combined with multi-objective evolutionary optimization techniques. The approach employs multi-objective binary differential evolution (MO-BDE) for array thinning and multi-objective particle swarm optimization (MO-PSO) for optimizing amplitude excitations. This integrated methodology reduces the number of active elements while enhancing overall array performance. The optimization process targets minimizing peak side lobe levels and maximizing directivity over a broad frequency range. Two designs are explored: one optimized at a primary frequency, and another providing consistent wideband behavior. The proposed method achieves a 37.5% reduction in active elements. Design A shows an SLL reduction of −12 dB at the target frequency, while Design B maintains up to −3 dB SLL improvement across the bandwidth. The results confirm the efficacy of the proposed synthesis method for developing scalable, energy-efficient antenna arrays for next-generation systems. Full article

Under the dual carbon objectives, wind power penetration has accelerated markedly. However, the inherent volatility and insufficient peak regulation capability in energy storage allocation hamper efficient grid integration. To address these challenges, this paper presents a hybrid storage capacity configuration method that combines Symplectic Geometry Mode Decomposition (SGMD) with Particle Swarm Optimization (PSO). SGMD provides fine-grained, multi-scale decomposition of load–power curves to reduce modal aliasing, while PSO determines globally optimal ESS capacities under peak-shaving constraints. Case-study simulations showed a 25.86% reduction in the storage investment cost compared to EMD-based baselines, maintenance of the state of charge (SOC) within 0.3–0.6, and significantly enhanced overall energy management efficiency. The proposed framework thus offers a cost-effective and robust solution for energy storage at renewable energy plants. Full article

Path planning for stratospheric airships in dynamic wind fields is challenging due to complex wind variations and strict nighttime energy constraints. This paper proposes a hybrid Level-Set Particle Swarm Optimization (LS-PSO) framework. Firstly, it employs PSO to search iteratively for a propulsion velocity sequence in the velocity domain, with a multi-objective fitness function that integrates reachability, energy consumption and time cost to evaluate each velocity sequence. Then, the reachability of each candidate sequence is numerically solved by the Level Set forward evolution. To improve optimization efficiency, we proposed a multi-resolution grid adaptive strategy for forward evolutions. Finally, with the optimal velocity sequence, the optimal path is generated once by the Level Set backtrack processing. To validate the resulting methodology, we used a benchmark case of a dynamic complex four-gyre flow, described by mathematical formulas, with the optimal day–night path identified by GPOPS-II. The results show the LS-PSO solution has comparable accuracy, with a trajectory deviation less than 3%. Then, we tested the methodology in the stratospheric wind flows using ERA5 reanalysis data. The results demonstrate that our path planning methodology provides a computationally efficient and optimal energy–time solution for autonomous stratospheric airships, while conforming to reachability and strict nighttime energy constraints. Full article

The growing use of unmanned aerial vehicles (UAVs) in diverse fields such as disaster recovery, rural regions, and smart cities necessitates effective dynamic drone network establishment techniques. Conventional optimization techniques like genetic algorithms (GAs) and particle swarm optimization (PSO) are weak when it comes to real-time adjustment to the environment and multi-objective constraints. This paper proposes a hybrid optimization framework combining genetic algorithms and reinforcement learning (RL) to improve the deployment of drone networks. We integrate Q-learning into the GA mutation process to allow drones to adaptively adjust locations in real time under coverage, connectivity, and energy constraints. In the scenario of large-scale simulations for wildfire tracking, disaster response, and urban monitoring tasks, the hybrid approach performs better than GA and PSO. The greatest enhancements are 6.7% greater coverage, 7.5% less average link distance, and faster convergence to optimal deployment. The proposed framework allows drones to establish strong and stable networks that are dynamic in nature and adapt to dynamic mission demands with efficient real-time coordination. This research has important applications in autonomous UAV systems for mission-critical applications where adaptability and robustness are essential. Full article

The Quantum Snowflake Algorithm (QSA) is a novel metaheuristic for both continuous and discrete optimization problems, combining collision-based diversity, quantum-inspired tunneling, superposition-based partial solution sharing, and local refinement steps. The QSA embeds candidate solutions in a continuous auxiliary space, where collision operators ensure that agents—snowflakes—reject each other and remain diverse. This approach is inspired by snowflakes which prevent collisions while retaining unique crystalline patterns. Large leaps to escape deep local minima are simultaneously provided by quantum tunneling, which is particularly useful in highly multimodal environments. Tests on challenging functions like Lévy and HyperSphere showed that the QSA can more reliably obtain very low objective values in continuous domains than conventional swarm or evolutionary approaches. A 200-city Traveling Salesman Problem (TSP) confirmed the excellent tour quality of the QSA for discrete optimization. It drastically reduces the route length compared to Artificial Bee Colony (ABC), Genetic Algorithm (GA), Particle Swarm Optimization (PSO), Simulated Annealing (SA), Quantum Particle Swarm Optimization (QPSO), and Cuckoo Search (CS). These results show that quantum tunneling accelerates escape from local traps, superposition and local search increase exploitation, and collision-based repulsion maintains population diversity. Together, these elements provide a well-rounded search method that is easy to adapt to different problem areas. In order to establish the QSA as a versatile solution framework for a range of large-scale optimization challenges, future research could investigate multi-objective extensions, adaptive parameter control, and more domain-specific hybridisations. Full article

To realize the autonomous operation of unmanned excavators, this study takes the four-axis manipulator arm of an unmanned excavator as the research object, uses the five-order B-spline curve for operation trajectory planning, and proposes an improved particle swarm optimization algorithm for the continuous trajectory optimization problem of excavator single operation. The specific contents are as follows: based on the standard PSO algorithm, dynamic parameter update is used to enhance the global search ability in the early stage and improve the local search accuracy in the later stage; the diversity monitoring mechanism is enhanced to avoid premature maturity convergence; multi-particle SA perturbation is introduced, and the new solution is accepted according to the Metropolis criterion to enhance global search ability. The adaptive cooling rate flexibly responds to different search situations and improves the search efficiency and quality of the solution. To verify the effectiveness of the improved PSO–SA algorithm, this study compares it with the standard PSO algorithm, the standard PSO–SA algorithm, and the MPSO algorithm. The simulation results show that the improved PSO–SA algorithm can converge to the global optimal solution more quickly, has the shortest time in trajectory planning, and the generated trajectory has higher tracking accuracy, which ensures that the vibration and impact of the manipulator during motion are effectively suppressed. Full article

The safe and efficient design of dynamic submarine cables is critical for the reliability of floating offshore wind turbines, yet traditional time-domain simulation-based optimization approaches are computationally intensive and time consuming. To address this challenge, this study proposes a closed-loop optimization framework that couples machine learning with intelligent optimization algorithms for a dynamic cable configuration design. A high-fidelity surrogate model based on a backpropagation (BP) neural network was trained to accurately predict cable dynamic responses. Three optimization algorithms—Particle Swarm Optimization (PSO), Ivy Optimization (IVY), and Tornado Optimization (TOC)—were evaluated for their effectiveness in optimizing the arrangement of buoyancy and weight blocks. The TOC algorithm exhibited superior accuracy and convergence stability. Optimization results show an 18.3% reduction in maximum curvature while maintaining allowable effective tension limits. This approach significantly enhances optimization efficiency and provides a viable strategy for the intelligent design of dynamic cable systems. Future work will incorporate platform motions induced by wind turbine operation and explore multi-objective optimization schemes to further improve cable performance. Full article