Search Results (236)

The joint optimization of storage location assignment and order picking efficiency for fresh products has become a vital challenge in intelligent warehousing because of the perishable nature of goods, strict temperature requirements, and the need to balance cost and efficiency. This study proposes a comprehensive mathematical model that integrates five critical cost components: picking path, storage layout deviation, First-In-First-Out (FIFO) penalty, energy consumption, and picker workload balance. To solve this NP-hard combinatorial optimization problem, we develop a Particle Swarm-guided hybrid Genetic-Simulated Annealing (PS-GSA) algorithm that synergistically combines global exploration by Particle Swarm Optimization (PSO), population evolution of Genetic Algorithm (GA), and the local refinement and probabilistic acceptance of Simulated Annealing (SA) enhanced with Variable Neighborhood Search (VNS). Computational experiments based on real enterprise data demonstrate the superiority of PS-GSA over benchmark algorithms (GA, SA, HPSO, and GSA) in terms of solution quality, convergence behavior, and stability, achieving 4.08–9.43% performance improvements in large-scale instances. The proposed method not only offers a robust theoretical contribution to combinatorial optimization but also provides a practical decision-support tool for fresh e-commerce warehousing, enabling managers to flexibly weigh efficiency, cost, and sustainability under different strategic priorities. Full article

The development of an optimally designed rain gauge network is crucial for achieving cost-efficient operation and maintenance and maintaining the overall accuracy of rainfall estimation. Traditional rainfall monitoring network optimization relies primarily on statistical methods without consideration of the underlying network configuration. This study presents a hybrid optimization approach integrating graph theory related to centrality (betweenness and clustering coefficient), minimum spanning tree (MST) and simulated annealing (SA) for monitoring station reduction. The proposed hybrid MST-SA algorithm with adaptive graph weighting applies to 317 monitoring stations in the northern Thailand using 11 years of wet-season rainfall data (2012–2022). Six main scenarios, involving the removal of 5 to 30 stations, are analyzed through the adjustment of the trade-off parameter between correlation and centrality. The results indicate that the proposed method outperforms the approach based solely on the correlation coefficient. This hybrid MST-SA approach achieves faster convergence and effectively preserves the continuity of spatial information throughout the domain. Furthermore, as the number of reduced stations increases, the influence of centrality becomes increasingly pronounced compared to that obtained solely from correlation analysis. Full article

The generation of high-quality test cases remains challenging due to combinatorial explosion and difficulty balancing exploration-exploitation in complex parameter spaces. This paper presents a novel Hybrid Artificial Bee Colony (ABC) algorithm that uniquely combines ABC optimization with Simulated Annealing temperature control and adaptive scout mechanisms for automated test case generation. The approach employs a four-tier categorical fitness function discriminating between boundary-valid, valid, boundary-invalid, and invalid values, with first-occurrence bonuses ensuring systematic exploration. Through comprehensive empirical validation involving 970 test suite generations across 97 parameter configurations, the hybrid algorithm demonstrates 68.3% improvement in fitness scores over pairwise testing (975.9 ± 10.6 vs. 580.0 ± 0.0, p < 0.001, d = 42.61). Statistical analysis identified three critical parameters with large effect sizes: MutationRate (d = 106.61), FinalPopulationSelectionRatio (d = 42.61), and TotalGenerations (d = 19.81). The value discrimination system proved essential, uniform weight configurations degraded performance by 7.25% (p < 0.001), while all discriminating configurations achieved statistically equivalent results, validating the architectural design over specific weight calibration. 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

This study proposes a method for designing spatial light modulator (SLM) projection phases in digital lasers using a simulated annealing (SA) approach combined with an initialized pre-designed phase to generate structured laser beams. SLM projection phases are optimized within the SA framework using a cost function based on the correlation between the corresponding laser field patterns and the target field. Numerical simulations demonstrate both the effectiveness of the proposed phase design method and its improvement in generating three geometric beams—quadrangular pyramid, triangular pyramid, and multi-ring fields—particularly with regard to enhanced edge sharpness. The resulting structured beams, especially those with simple geometric shapes, are suitable for microfabrication applications such as photolithography and photopolymerization. The proposed SA iteration framework is not limited to the L-shaped resonator used in this study and can be extended to digital laser cavities with higher numerical apertures, enabling the generation of more complex structured light fields. Full article

Unmanned surface vehicles (USVs) rely on multi-sensor perception, such as radar, LiDAR, GPS, and vision, to ensure safe and efficient navigation in complex maritime environments. Traditional ant colony optimization (ACO) for path planning, however, suffers from premature convergence, slow adaptation, and poor smoothness in cluttered waters, while the dynamic window approach (DWA) without global guidance can become trapped in local obstacle configurations. This paper presents a sensor-oriented hybrid method that couples an improved ACO for global route planning with an enhanced DWA for local, real-time obstacle avoidance. In the global stage, the ACO state–transition rule integrates path length, obstacle clearance, and trajectory smoothness heuristics, while a cosine-annealed schedule adaptively balances exploration and exploitation. Pheromone updating combines local and global mechanisms under bounded limits, with a stagnation detector to restore diversity. In the local stage, the DWA cost function is redesigned under USV kinematics to integrate velocity adaptability, trajectory smoothness, and goal-deviation, using obstacle data that would typically originate from onboard sensors. Simulation studies, where obstacle maps emulate sensor-detected environments, show that the proposed method achieves shorter paths, faster convergence, smoother trajectories, larger safety margins, and higher success rates against dynamic obstacles compared with standalone ACO or DWA. These results demonstrate the method’s potential for sensor-based, real-time USV navigation and collision avoidance in complex maritime scenarios. Full article

Aiming at the problems of high-dimensional nonlinear constraints, multi-objective conflicts, and low solution efficiency in microgrid optimal scheduling, this paper proposes a multi-objective Harris Hawk–Grey Wolf hybrid intelligent algorithm (IMOHHOGWO). The problem of balancing the global exploration and local exploitation of the algorithm is solved by introducing an adaptive energy factor and a nonlinear convergence factor; in terms of the algorithm’s exploration scope, the stochastic raid strategy of Harris Hawk optimization (HHO) is used to generate diversified solutions to expand the search scope, and constraints such as the energy storage SOC and DG outflow are finely tuned through the α/β/δ wolf bootstrapping of the Grey Wolf Optimizer (GWO). It is combined with a simulated annealing perturbation strategy to enhance the adaptability of complex constraints and local search accuracy, at the same time considering various constraints such as power generation, energy storage, power sales, and power purchase. We establish the microgrid multi-objective operation cost and carbon emission cost objective function, and through the simulation examples, we verify and determine that the IMOHHOGWO hybrid intelligent algorithm is better than the other three algorithms in terms of both convergence speed and convergence accuracy. According to the results of the multi-objective test function analysis, its performance is superior to the other four algorithms. The IMOHHOGWO hybrid intelligent algorithm reduces the grid operation cost and carbon emissions in the microgrid optimal scheduling model and is more suitable for the microgrid multi-objective model, which provides a feasible reference for future integrated microgrid optimal scheduling. 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

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

Wireless sensor networks (WSNs) are a key enabling technology for modern e-Health applications. However, their deployment in clinical environments faces critical challenges due to dynamic network topologies, signal interference, and stringent energy constraints. Static resource allocation schemes often prove inadequate in these mission-critical settings, leading to communication failures that can compromise data integrity and patient safety. This paper proposes a novel hybrid framework for intelligent, dynamic resource allocation that addresses these challenges. The framework combines classical graph-coloring heuristics—Greedy and Recursive Largest First (RLF) for efficient initial channel assignment with the adaptive power of metaheuristics, specifically Simulated Annealing and Genetic Algorithms, for localized refinement. Unlike conventional approaches that require costly, network-wide reconfigurations, our method performs targeted adaptations only in interference-affected regions, thereby optimizing the trade-off between network reliability and energy efficiency. Comprehensive simulations modeled on dynamic, hospital-scale WSNs demonstrate the effectiveness of various hybrid strategies. Notably, our results demonstrate that a hybrid strategy using a Genetic Algorithm can most effectively minimize interference and ensure high data reliability, validating the framework as a scalable and resilient solution. These results validate the proposed framework as a scalable, energy-aware solution for resilient, real-time healthcare telecommunication infrastructures. 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

Optimizing tourist routes is a critical challenge in smart tourism, which aims to enhance the visitor experience while optimizing practical parameters. However, traditional routing algorithms often fail to provide personalized and efficient itineraries in complex real-world environments. This study aims to develop a hybrid framework that integrates Simulated Annealing for global route optimization with the A algorithm* for accurate local pathfinding, leveraging geographic data from OpenStreetMap. The proposed method computes the shortest paths between all Points of Interest using A*, constructing a comprehensive distance matrix, and applying Simulated Annealing to determine the most efficient visiting sequence. The framework was evaluated in the Old Medina of Fez, Morocco, demonstrating its effectiveness in generating realistic and efficient itineraries. Compared to alternative strategies such as Genetic Algorithms, the hybrid approach achieves superior computational efficiency and produces better routes in terms of travel distance. These findings highlight the practical applicability of the framework as a modular service for smart tourism applications, offering tourists and tourism platform developers a scalable solution for personalized and sustainable itinerary planning. Full article

Achieving ubiquitous coverage in 6G networks presents significant challenges due to the limitations of high-frequency signals and the need for extensive infrastructure, and providing seamless connectivity in remote and rural areas remains a challenge. We propose an integrated optimization framework for UAV-LEO-RIS-assisted wireless networks, aiming to maximize system sum rate through the strategic placement and configuration of Unmanned Aerial Vehicles (UAVs), Low Earth Orbit (LEO) satellites, and Reconfigurable Intelligent Surfaces (RIS). The framework employs a dual wireless backhaul and utilizes a grid search method for UAV placement optimization, ensuring a comprehensive evaluation of potential positions to enhance coverage and data throughput. Simulated Annealing (SA) is utilized for RIS placement optimization, effectively navigating the solution space to identify configurations that improve signal reflection and network performance. For sum rate maximization, we incorporate several metaheuristic algorithms, including Particle Swarm Optimization (PSO), Genetic Algorithm (GA), Grey Wolf Optimization (GWO), Salp Swarm Algorithm (SSA), Marine Predators Algorithm (MPA), and a hybrid PSO-GWO approach. Simulation results demonstrate that the hybrid PSO-GWO algorithm outperforms individual metaheuristics in terms of convergence speed and achieving a higher sum rate. The coverage improves from 62% to 100%, and the results show an increase in spectrum efficiency of 23.7%. Full article