Search Results (328)

The large-scale adoption of electric vehicles has created an urgent need for the orderly management of charging loads in residential communities. While existing research on community-based orderly charging architectures and control strategies primarily focuses on connected charging piles (CPs) equipped with remote power control functions. However, in practical scenarios, most residential communities still rely on unconnected charging piles (UCPs) that lack remote communication capabilities, making it difficult to practically deploy many intelligent orderly architectures and control strategies that rely on communication with charging piles. Therefore, this paper proposes a non-intrusive orderly charging architecture tailored for UCPs. This architecture does not require modifying the hardware of UCPs; instead, it introduces pile-end management units (PMUs) to interact with users for orderly charging, thereby facilitating easier deployment and promotion. Based on this architecture, an optimized control strategy using the GD-SA (greedy-simulated annealing) algorithm for orderly charging is constructed, which considers the dual constraints of transformer capacity and charging demand. Case studies on a typical community in Tianjin, China, demonstrate that with the proposed order charging architecture and strategy, when users fully accept the orderly charging approach, the peak load can be reduced by over 17% compared to uncontrolled charging scenarios. Additionally, the effectiveness of the method has been validated through sensitivity analysis of user acceptance, stress scenario testing, and statistical analysis with a 95% confidence interval. Finally, this paper summarizes the practical value potential of supporting UCPs in achieving orderly charging, while also pointing out the limitations of the current research and identifying directions for further in-depth exploration. Full article

The reconfiguration of distribution networks plays a crucial role in minimizing active power losses and enhancing reliability, but the problem becomes increasingly complex with the integration of distributed generation (DG). Traditional optimization methods and even earlier hybrid metaheuristics often suffer from premature convergence or require problem reformulations that compromise feasibility. To overcome these limitations, this paper proposes a novel hybrid algorithm that couples Particle Swarm Optimization (PSO) with Simulated Annealing (SA) through an adaptive inertia weight mechanism derived from the Lundy–Mees cooling schedule. Unlike prior hybrid approaches, our method directly addresses the original non-convex, combinatorial nature of the Distribution Network Reconfiguration (DNR) problem without convexification or post-processing adjustments. The main contributions of this study are fourfold: (i) proposing a PSO-SA hybridization strategy that enhances global exploration and avoids stagnation; (ii) introducing an adaptive inertia weight rule tuned by SA, more effective than traditional schemes; (iii) applying a stagnation-based stopping criterion to speed up convergence and reduce computational cost; and (iv) validating the approach on 5-, 33-, and 69-bus systems, with and without DG, showing robustness, recurrence rates above 80%, and low variability compared to conventional PSO. Simulation results confirm that the proposed PSO-SA algorithm achieves superior performance in both loss minimization and solution stability, positioning it as a competitive and scalable alternative for modern active distribution systems. Full article

Dents are a common geometric deformation defect in pipelines where the dented section becomes susceptible to local buckling, significantly threatening the integrity and reliability of the pipeline. This paper developed a novel finite element (FE) machine learning (ML)-based approach to analyze and predict the critical buckling moment (CBM) of dented pipelines under combined internal pressure and bending moment (BM) loading. By quantifying the parametric effects on CBM and developing a dataset, an Extreme Learning Machine (ELM) framework through hybrid algorithm integration, combining Bald Eagle Search (BES), Lévy flight, and Simulated Annealing (SA), was proposed to achieve highly accurate CBM predictions. This study offers valuable insights into evaluating the buckling resistance of dented pipelines subjected to complex loading conditions. Full article

Multi-drone operations face significant efficiency challenges when launch pad locations are predetermined without optimization, leading to suboptimal route configurations and increased travel distances. This research addresses launch pad positioning as a continuous planar location-routing problem (PLRP), developing a genetic algorithm framework integrated with multiple Traveling Salesman Problem (mTSP) solvers to optimize launch pad coordinates within operational areas. The methodology was evaluated through extensive experimentation involving over 17 million test executions across varying problem complexities and compared against brute-force optimization, Particle Swarm Optimization (PSO), and simulated annealing (SA) approaches. The results demonstrate that the genetic algorithm achieves 97–100% solution accuracy relative to exhaustive search methods while reducing computational requirements by four orders of magnitude, requiring an average of 527 iterations compared to 30,000 for PSO and 1000 for SA. Smart initialization strategies and adaptive termination criteria provide additional performance enhancements, reducing computational effort by 94% while maintaining 98.8% solution quality. Statistical validation confirms systematic improvements across all tested scenarios. This research establishes a validated methodological framework for continuous launch pad optimization in UAV operations, providing practical insights for real-world applications where both solution quality and computational efficiency are critical operational factors while acknowledging the simplified energy model limitations that warrant future research into more complex operational dynamics. Full article

This study addresses the Capacitated Vehicle Routing Problem (CVRP) known to be NP-hard. In this problem, a set of customers with varying demands is considered. To solve the problem, routes were generated for several vehicles with identical capacity, which were responsible for delivering products to a set of geographically dispersed customers. The purpose of the problem is to minimize the total cost of all routes. This problem was solved by applying the metaheuristic Simulated Annealing (SA) and incorporating four different neighborhoods to improve the initial solution generated randomly. In the SA, a set of cooling factors is used. The best solution obtained by SA is refined by the use of Hill Climbing using a double neighborhood. The algorithm was tested with instances from the literature in order to measure its effectiveness in solution quality and execution time. We tested the approach with 106 instances from the literature and obtained the optimum in 93 instances. The average time in most instances was less than five minutes. Delivery companies can benefit from this approach. They only need to identify the depot, the clients, and the distance between locations, and this approach can be used with relative ease. Full article

Effectively managing agricultural practices is crucial for maximizing yield, reducing investment costs, preserving soil health, ensuring sustainability, and mitigating environmental impact. This study proposes an adaptation of the Grey Wolf Optimizer (GWO) metaheuristic to operate under specific constraints, with the goal of identifying optimal agricultural practices that boost maize crop yields and enhance economic profitability for each farm. To achieve this objective, we employ a probabilistic algorithm that constructs a model based on Clusterwise Linear Regression (CLR) as the primary method for predicting crop yield. This model considers several factors, including climate, soil conditions, and agricultural practices, which can vary depending on the specific location of the crop. We compare the performance of the Grey Wolf Optimizer (GWO) algorithm with other optimization techniques, including Hill Climbing (HC) and Simulated Annealing (SA). This analysis utilizes a dataset of maize crops from the Department of Córdoba in Colombia, where agricultural practices were optimized. The results indicate that the probabilistic algorithm defines a two-group CLR model as the best approach for predicting maize yield, achieving a 5% higher fit compared to other machine learning algorithms. Furthermore, the Grey Wolf Optimizer (GWO) metaheuristic achieved the best optimization performance, recommending agricultural practices that increased farm yield and profitability by 50% relative to the original practices. Overall, these findings demonstrate that the proposed algorithm can recommend optimal practices that are both technically feasible and economically viable for implementation and replication. Full article

With the widespread adoption of space–air–ground integrated networks (SAGINs) in next-generation wireless communications, intelligent reflecting surfaces (IRSs) have emerged as a key technology for enhancing system performance through passive link reinforcement. This paper addresses the prevalent issue of channel state information (CSI) uncertainty in practical systems by constructing an IRS-assisted multi-hop SAGIN communication model. To capture the performance degradation caused by channel estimation errors, a norm-bounded uncertainty model is introduced. A simulated annealing (SA)-based phase optimization algorithm is proposed to enhance system robustness and improve worst-case communication quality. Simulation results demonstrate that the proposed method significantly outperforms traditional multiple access strategies (SDMA and NOMA) under various user densities and perturbation levels, highlighting its stability and scalability in complex environments. Full article

Virtual power plants (VPPs) integrate distributed energy sources and demand-side resources, but their efficient intelligent resource decision-making faces challenges such as high-dimensional constraints, output volatility of renewable energy, and insufficient adaptability of traditional optimization algorithms. To address these issues, an innovative intelligent decision-making framework based on the Ant Colony Algorithm–Simulated Annealing (ACO-SA) is first proposed in this paper, aiming to realize intelligent collaborative decision-making for the economy and operational stability of VPP in complex scenarios. This framework combines the global path-searching capability of the Ant Colony Algorithm (ACO) with the probabilistic jumping characteristic of the Simulated Annealing Algorithm (SA) and designs a dynamic parameter collaborative adjustment mechanism, which effectively overcomes the defects of traditional algorithms such as slow convergence and easy trapping in local optimal solutions. Secondly, a resource intelligent decision-making cost model under the VPP framework is constructed. To verify algorithm performance, comparative experiments covering multiple scenarios (agricultural parks, industrial parks, and industrial parks with energy storage equipment) are designed and conducted. Finally, the simulation results show that compared with ACO, SA, Genetic Algorithm (GA), and Particle Swarm Optimization (PSO), ACO-SA exhibits significant advantages in terms of scheduling cost and convergence speed; the average scheduling cost of ACO-SA is 2.31%, 0.23%, 3.57%, and 1.97% lower than that of GA, PSO, ACO, and SA, respectively, and it can maintain excellent stability even in high-dimensional constraint scenarios with energy storage systems. Full article

Rapid access to defibrillators, blood products, and time-critical medicines can improve survival, yet urban congestion and fragmented infrastructure delay deliveries. We present and evaluate an end-to-end framework for beyond-visual-line-of-sight (BVLOS) UAV logistics in Essex (UK), integrating (I) strategic depot placement, (II) a hybrid obstacle-aware route planner, and (III) a time-window-aware (TWA) Mixed-Integer Linear Programming (MILP) scheduler coupled to a battery/temperature feasibility model. Four global planners—Ant Colony Optimisation (ACO), Genetic Algorithm (GA), Particle Swarm Optimisation (PSO), and Rapidly Exploring Random Tree* (RRT*)—are paired with lightweight local refiners, Simulated Annealing (SA) and Adaptive Large-Neighbourhood Search (ALNS). Benchmarks over 12 destinations used real Civil Aviation Authority no-fly zones and energy constraints. RRT*-based hybrids delivered the shortest mean paths: RRT* + SA and RRT* + ALNS tied for the best average length, while RRT* + SA also achieved the co-lowest runtime at $v=60\mathrm{km}{\mathrm{h}}^{-1}$. The TWA-MILP reached proven optimality in 0.11 s, showing that a minimum of seven UAVs are required to satisfy all 20–30 min delivery windows in a single wave; a rolling demand of one request every 15 min can be sustained with three UAVs if each sortie (including service/recharge) completes within 45 min. To validate against a state-of-the-art operations-research baseline, we also implemented a Vehicle Routing Problem with Time Windows (VRPTW) in Google OR-Tools, confirming that our hybrid planners generate competitive or shorter NFZ-aware routes in complex corridors. Digital-twin validation in AirborneSIM confirmed CAP 722-compliant, flyable trajectories under wind and sensor noise. By hybridising a fast, probabilistically complete sampler (RRT*) with a sub-second refiner (SA/ALNS) and embedding energy-aware scheduling, the framework offers an actionable blueprint for emergency medical UAV networks. Full article

The number of IoT devices has been increasing at a rapid rate, and the advent of information-intensive Internet of Multimedia Things (IoMT) applications has placed serious challenges on computing infrastructure, especially for latency, energy efficiency, and responsiveness to tasks. The legacy cloud-centric approach cannot meet such requirements because it suffers from local latency and central resource allocation. To overcome such limitations, fog computing proposes a decentralized model by reducing latency and bringing computation closer to data sources. However, effective scheduling of tasks within heterogeneous and resource-limited fog environments is still an NP-hard problem, especially in multi-criteria optimization and priority-sensitive situations. This research work proposes a new simulated annealing (SA)-based task scheduling framework to perform multi-objective optimization for fog computing environments. The proposed model minimizes makespan, energy consumption, and execution cost, and integrates a priority-aware penalty function to provide high responsiveness to high-priority tasks. The SA algorithm searches the scheduling solution space by accepting potentially sub-optimal configurations during the initial iterations and further improving towards optimality as the temperature decreases. Experimental analyses on benchmark datasets obtained from Google Cloud Job Workloads demonstrate that the proposed approach outperforms ACO, PSO, I-FASC and M2MPA approaches in terms of makespan, energy consumption, execution cost, and reliability at all task volume scales. These results confirm the proposed SA-based scheduler as a scalable and effective solution for smart task scheduling within fog-enabled IoT infrastructures. Full article

A newly developed hybrid maximum power point tracker (MPPT) utilizes a modified simulated annealing (SA) algorithm in conjunction with an adaptive hill climbing (HC) technique to optimize the extraction of the maximum power point (MPP) from photovoltaic (PV) systems. This innovative MPPT improves the ability to harvest maximum power from the PV system, particularly under rapidly fluctuating weather conditions and in situations of partial shading. The controller combines the rapid local search abilities of HC with the global optimization advantages of SA, which has been modified to retain and retrieve the maximum power achieved, thus ensuring the extraction of the global maximum. Furthermore, an adaptive HC algorithm is implemented with a variable step size adjustment, which accelerates convergence and reduces steady-state oscillations. Additionally, an offline SA algorithm is utilized to fine-tune the essential parameters of the proposed controller, including the maximum and minimum step sizes for duty cycle adjustments, initial temperature, and cooling rate. Simulations performed in Matlab/Simulink, along with experimental validation using Imperix-Opal-RT, confirm the effectiveness and robustness of the proposed controller. In the scenarios that were tested, the suggested HC–SA reached the global maximum power point (GMPP) of approximately 600 W in about 0.05 s, whereas the traditional HC stabilized at a local maximum close to 450 W, and the fuzzy-logic MPPT attained the GMPP at a slower rate, taking about 0.2 s, with a pronounced transient dip before settling with a small steady-state ripple. These findings emphasize that, under the operating conditions examined, the proposed method reliably demonstrates quicker convergence, enhanced tracking accuracy, and greater robustness compared with the other MPPT techniques. Full article

This study proposes a hybrid optimization algorithm (IFWA-SA) integrating an improved fireworks algorithm with simulated annealing for sparse array synthesis in multiple-input multiple-output (MIMO) radar systems. The innovation lies in synergistically combining the multidimensional directional explosion mechanism of the fireworks algorithm for global exploration with simulated annealing’s probabilistic jumping strategy for local optimization. Initial populations generated via Sobol sequences eliminate local clustering from random initialization. During global exploration, the proposed discrete variant of the fireworks algorithm, tailored for sparse array optimization, significantly enhances the search efficiency, while temperature-controlled probabilistic optimization refines array aperture and element spacing to escape local optima during local refinement. Comparative experiments with particle swarm optimization (PSO), simulated annealing (SA), genetic algorithm (GA) and gray wolf optimization (GWO) demonstrated that the proposed method effectively suppresses sidelobes. On average, the IFWA-SA reduced the peak sidelobe level (PSL) by about 1.3–3.8 dB compared with the benchmark algorithms, confirming its superior convergence capability and effectiveness in synthesizing high-performance sparse arrays. Full article

Addressing key challenges in unstructured environments, including local optimum traps, limited real-time interaction, and convergence difficulties, this research pioneers a hybrid reinforcement learning approach that combines simulated annealing (SA) with proximal policy optimization (PPO) for robotic arm trajectory planning. The framework enables the accurate, collision-free grasping of randomly appearing objects in dynamic obstacles through three key innovations: a probabilistically enhanced simulation environment with a 20% obstacle generation rate; an optimized state-action space featuring 12-dimensional environment coding and 6-DoF joint control; and an SA-PPO algorithm that dynamically adjusts the learning rate to balance exploration and convergence. Experimental results show a 6.52% increase in success rate (98% vs. 92%) and a 7.14% reduction in steps per set compared to the baseline PPO. A real deployment on the AUBO-i5 robotic arm enables real machine grasping, validating a robust transfer from simulation to reality. This work establishes a new paradigm for adaptive robot manipulation in industrial scenarios requiring a real-time response to environmental uncertainty. Full article

Background: Urban rail transit ensures efficient mobility in densely populated metropolitan areas. This study focuses on the Cairo Metro Network and addresses the Rolling Stock Rotation Planning Problem (RSRPP), aiming to improve operational efficiency and service quality. Methods: A Mixed-Integer Linear Programming (MILP) model is developed to integrate rolling stock rotation, deadhead routing, and maintenance scheduling. Two single-objective formulations are introduced to separately minimize denied passengers and the number of Electric Multiple Units (EMUs) used. To address scalability for larger instances, a Simulated Annealing (SA) metaheuristic is designed using a list-based solution representation and customized neighborhood operators that preserve feasibility. Results: Computational experiments based on real-world data validate the practical relevance of the model. The MILP achieves optimal solutions for small and medium-sized instances but becomes computationally infeasible for larger ones. In contrast, the SA algorithm consistently produces high-quality solutions with significantly reduced solve times. Conclusions: To the best of the authors’ knowledge, this is the first study to apply SA to the urban rail RSRPP while jointly integrating deadhead routing and maintenance scheduling. The proposed approach proves to be robust and scalable for large metro systems such as Cairo’s. Full article

In high-voltage switchgear, partial discharge (PD) detection using six-degree-of-freedom (6-DOF) manipulators presents challenges. However, these involve inverse kinematics (IK) solution redundancy and the lack of synergistic optimization between end-effector positioning accuracy and energy consumption. To address these issues, a dual-layer adaptive optimization model integrating multiple algorithms is proposed. In the first layer, a spatio-temporal correlation particle memory-based particle swarm optimization BP neural network (STPSO-BP) is employed. It replaces traditional IK, while long short-term memory (LSTM) predicts particle movement trends, and trajectory similarity penalties constrain search trajectories. Thereby, positioning accuracy and adaptability are enhanced. In the second layer, a chaotic mapping-based simulated annealing (CM-SA) algorithm is utilized. Chaotic joint angle constraints, dynamic weight adjustment, and dynamic temperature regulation are incorporated. This approach achieves collaborative optimization of energy consumption and positioning error, utilizing cubic spline interpolation to smooth the joint trajectory. Specifically, the positioning error decreases by 68.9% compared with the traditional BP neural network algorithm. Energy consumption is reduced by 60.18% in contrast to the pre-optimization state. Overall, the model achieves significant optimization. An innovative solution for synergistic accuracy–energy control in 6-DOF manipulators for PD detection is offered. Full article