Search Results (453)

The selection of locations for logistics distribution centers poses a significant challenge in logistics network planning. Traditional methods often demonstrate limited accuracy in solutions and a tendency to become trapped in local optima when addressing large-scale, multi-constraint location models. To address these shortcomings, this study introduces a firefly algorithm enhanced by genetic mutation strategies (GVFA) to optimize the location of distribution centers. Within the framework of the standard firefly algorithm, we incorporate an adaptive step-size decay mechanism and a mutation operator. The movement step size adjusts dynamically based on iteration counts, while a mutation probability of 5% is implemented to maintain population diversity, effectively reducing the risk of premature convergence. A specialized boundary-handling strategy ensures that the search process remains within the feasible solution space, guiding the population toward the global optimum. Experiments were conducted using latitude–longitude coordinates and logistics demand data from 159 Cainiao Post stations in Hengyang City, resulting in the construction of a location model aimed at minimizing total costs. The findings confirm the efficiency and stability of our method in optimizing distribution center locations, thereby providing a novel intelligent optimization approach for the siting of logistics distribution centers. Full article

The digital preservation of built cultural heritage requires precise documentation techniques capable of capturing complex architectural geometries often affected by occlusions and data voids. This study presents a robust multi-sensor fusion workflow integrating Terrestrial Laser Scanning (TLS) and Unmanned Aerial Vehicle (UAV) photogrammetry for the 3D reconstruction of the Hasaköy (Sasima) Church in Niğde, Türkiye. To address the limitations of traditional registration methods, specifically the susceptibility of the Iterative Closest Point (ICP) algorithm to local minima in datasets with partial overlaps, this study proposes a fine-tuning approach based on the Multi-population Based Differential Evolution (MDE) algorithm. The methodology employs a coarse-to-fine strategy, initiating with Fast Point Feature Histogram (FPFH) extraction and RANSAC (Random Sample Consensus) for global alignment, followed by TR-ICP, MDE, PSO, and Aquila Optimizer (AO) evaluation, computational-time analysis, FPFH-radius sensitivity testing, and 6-DoF transformation decomposition to characterize both accuracy and operational cost. In the 30-run fine-tuning evaluation, MDE reduced the mean bidirectional trimmed RMSE from 0.4152 m for TR-ICP to 0.3726 m. With a population parameter of 10, MDE retained a low median RMSE of 0.3718 m, while PSO exhibited a wider stochastic tail under the same bounded 6-DoF search budget. AO produced a higher mean bidirectional trimmed RMSE of 0.5233 m. The decimeter-scale bidirectional RMSE should be interpreted as a cross-source, partial-overlap distance metric rather than sensor precision; the overlapping facade objective was approximately 2.4–2.8 cm, and the UAV block was independently controlled with a 1.34 cm GCP RMSE. This study establishes a transparent and reproducible framework for heritage documentation, supporting the faithful digital preservation of endangered monuments with complex typologies. Full article

The NMR resonator is the sensor responsible for transmitting RF pulses and receiving detection signals, and its tuning and matching are crucial to acquiring high-sensitivity NMR signals. Automated tuning and matching (ATM) is therefore essential for rapid, accurate, and continuously efficient testing. Existing NMR ATM methods still primarily rely on iterative search strategies, whose dominant cost arises from repeated hardware measurements and waiting periods, often requiring multiple measurement cycles before convergence. The emergence of in situ NMR detection of high-concentration ionic samples has further increased the demand for real-time, rapid ATM with a large dynamic range, posing a major challenge to conventional approaches. This paper proposes a physics-informed few-shot learning method for automatic tuning and matching over wideband and multi-resonance-frequency NMR scenarios. The tuning-and-matching problem is formulated as a structure and frequency-conditioned function regression task, and a conditional neural process (CNP) is introduced to learn cross-task priors and directly predict the states of tunable components from only a small number of real-machine context measurements. A physics regularizer based on the local sensitivity of the input impedance is further designed to impose stronger penalties on errors under high-Q narrowband operating conditions without relying on proprietary analytical circuit models. Simulation studies and real NMR experiments are conducted on multiple circuit topologies and multiple target frequencies using only a small number of NMR samples. The results demonstrate consistent improvements in key metrics, including accuracy of tuning and matching and the number of collected real-machine samples required per task. In particular, with only 100 sampled tuning/matching capacitor points and 20 on-hardware collected samples, the proposed method already delivers satisfactory tuning-and-matching performance. The method achieves an attractive accuracy–cost tradeoff across both cross-topology and cross-frequency scenarios, and shows strong potential for few-shot, rapid, real-time detection. Full article

The greater cane rat algorithm (GCRA) draws inspiration from the seasonal behavioral patterns of the greater cane rats: extensive roaming during the non-breeding period for global exploration, and aggregative foraging during the reproductive period for local exploitation. The GCRA leverages independent movement and population aggregation to iteratively update positions in pursuit of the optimal solution, which exhibits inherent structural deficiencies: precipitous population diversity collapse, lethargic convergence dynamics, suboptimal computational precision, high susceptibility to local optima, and severe dimensional scalability. This paper proposes a modified complex-valued encoding GCRA (CGCRA) that exploits the mathematical structure of complex numbers to construct a two-dimensional search domain on the complex plane and facilitate collaborative optimization. The CGCRA maps the decision variables onto the complex domain, the real part executes the native foraging mechanism for local fine-grained exploitation, and the imaginary part exploits phase rotation to generate global exploratory perturbations. The CGCRA leverages a dual-encoding redundancy mechanism with inherent error tolerance to attenuate result volatility, augment information capacity and population heterogeneity, elevate search adaptability and disturbance rejection, accelerate parallel computation and exploration efficiency, and facilitate spatial transformation and multi-dimensional data manipulation. Twenty-three benchmark functions and twelve real-world engineering designs are employed to assess the CGCRA’s stability and practical feasibility rigorously. The CGCRA delivers comprehensive spatial mapping and adaptive coordination to facilitate population collaboration and bolster resilience, expedite exhaustive research, and advance optimization efficiency. The experimental results demonstrate that the CGCRA emphasizes instructive superiority and practical utility to regulate exploration and exploitation, reduce result dispersion, mitigate search stagnation, accelerate convergence efficiency, elevate solution precision, and fortify stability and robustness. Full article

Accurately capturing the Kuroshio’s main axis and its multiscale frontal variations remains challenging due to the constraints of traditional fixed-section extraction methods. Here, we develop an adaptive iterative tracking algorithm utilizing high-resolution reanalysis data (2002–2024) that dynamically adjusts search directions and cross-sections via local velocity vectors, integrated with a dynamic step size and two-dimensional validation. Applying a multiscale variability decomposition framework across four key regions reveals distinct spatiotemporal dynamics. The North Equatorial Current bifurcation zone exhibits a significant strengthening trend driven by seasonal zonal and decadal meridional flows. Conversely, the Kuroshio east of Taiwan is dominated by high-frequency mesoscale processes (~70%) with a semi-annual cycle and no long-term trend. The East China Sea front maintains a highly stable seasonal meridional signal (25%). Crucially, the Luzon Strait intrusion shows a significant long-term weakening trend (~0.0029 m·s⁻¹·a⁻¹, p < 0.01), characterized by eastward strengthening and northward weakening, with ENSO significantly modulating its seasonal cycle. This approach substantially reduces systematic extraction errors compared to traditional fixed-section methods, as independently verified using satellite SST frontal gradients (median deviation < 0.2°), providing critical observational evidence for understanding western boundary current–marginal sea interactions and their dynamical responses under global warming. Full article

High-dimensional small-sample data are widely encountered in medical diagnosis, bioinformatics, and industrial inspection, where traditional feature selection methods often suffer from premature convergence and local optima. To address these issues, this paper proposes a Hybrid Breeding-based Co-evolutionary Ant Colony Optimization method (HBACO) for feature selection. Inspired by the principle of hybrid breeding, in which individuals with distinct traits produce superior offspring through cross recombination, inheritance of desirable genes and continuous evolution, the proposed algorithm establishes a three-population collaborative framework. It consists of an ACO-based search population, an HRO-based evolutionary population and a cooperative feedback population that evolve iteratively together. Furthermore, we devise a heuristic strategy integrating correlation and genetic characteristics to help mine high-value feature subsets. Meanwhile, a collaborative pheromone updating mechanism is adopted to realize efficient knowledge sharing among populations. Experiments conducted on 13 high-dimensional datasets, including Colon and Lung, demonstrate that HBACO achieves superior classification accuracy, feature reduction performance, and convergence behavior compared with 10 representative algorithms. Specifically, HBACO improves the average classification accuracy by 3.9% and achieves an average feature dimensionality reduction rate of 91.4%. Statistical tests further confirm the significance of the proposed method. The results indicate that HBACO provides an effective and robust solution for high-dimensional feature selection problems. Full article

To address the imbalance between global exploration and local exploitation in the secretary bird optimization algorithm (SBOA), this paper presents a multi-strategy improved version termed MSISBOA. The proposed approach incorporates optimal Latin hypercube sampling during initialization to achieve a more uniform distribution of initial solutions. In the hunting phase, an adaptive Cauchy mutation factor and a boundary strategy are integrated to refine local search precision. To reduce the risk of stagnation in local optima during later iterations, a triangular walk strategy is utilized for mutation perturbation. Furthermore, the escape phase employs a combined Tent chaotic-Gaussian mutation factor and an elite retention strategy to maintain high-quality solutions while diversifying the population. The performance of MSISBOA was evaluated using the benchmark suites released for the IEEE Congress on Evolutionary Computation (CEC), including CEC-2017 and CEC-2022, against nine other swarm intelligence algorithms, with statistical results showing that MSISBOA achieved the highest average rank. Additionally, the algorithm was applied to 18 engineering optimization problems to assess its capability in solving practical constrained tasks. Experimental results indicate that MSISBOA provides competitive convergence characteristics and solution quality across the tested scenarios. Full article

To address the limitations of the original Secretary Bird Optimization Algorithm (SBOA), such as insufficient population diversity, weak local exploitation ability, and a tendency to become trapped in local optima when solving complex optimization problems, this paper proposes a Multi-Strategy Improved Secretary Bird Optimization Algorithm (MISBOA). First, a chaotic elite initialization strategy is introduced to improve the quality and diversity of the initial population. Second, an adaptive spiral Lévy flight strategy is designed to enhance the balance between global exploration and local exploitation during the iterative process. Third, a dynamic neighborhood-guided mutation strategy is incorporated to maintain population diversity and improve convergence accuracy in the later search stage. To validate the effectiveness of the proposed algorithm, MISBOA is comprehensively evaluated on the IEEE CEC2014, CEC2017, and CEC2020 benchmark suites. Experimental results demonstrate that MISBOA achieves superior convergence speed, optimization accuracy, and robustness compared with several representative metaheuristic algorithms. Furthermore, MISBOA is applied to Otsu-based multilevel threshold image segmentation. The segmentation performance is assessed using PSNR, FSIM, SSIM, and visual quality comparisons. The results indicate that MISBOA can generate more accurate and stable segmentation outcomes, demonstrating its strong potential for solving complex numerical optimization and artistic image segmentation problems. Full article

Low-frequency traffic noise below 500 Hz is difficult to mitigate because its long wavelengths require impractically large conventional resonators. Here, we report an active-learning-guided inverse-design approach for scalable phononic-crystal-based acoustic metamaterial resonators that simultaneously suppress low-frequency noise transmission and harvest acoustic energy. The approach combines Gaussian process regression surrogate modeling with genetic algorithm optimization to efficiently explore high-dimensional cavity geometries. By iteratively retraining the surrogate with FEM-validated designs, the active-learning process guides the search toward high-performance structures while reducing costly FEM evaluations compared with conventional GA optimization. After geometric scaling, the 2.5D prototype derived from the nine-point optimized cavity achieved a pressure amplification factor of approximately 20 near 490 Hz, while the revolved 3D cavity exhibited amplification exceeding 30 and a transmission loss of approximately 14 dB near the target frequency. Integrated with a mass-loaded five-PZT stack, the device generated 5.5 V_pp and 0.25 mW under 100 dB SPL, corresponding to a normalized power density of 0.58 μW Pa⁻² cm⁻³. These results demonstrate a route toward multifunctional piezoelectric acoustic devices for noise mitigation, localized energy harvesting, and self-powered sensing. Full article

With the growing complexity of engineering optimization and high-dimensional data analysis tasks, balancing global exploration and local exploitation remains a core challenge in computational intelligence. The Tiki-taka Algorithm (TTA), a football-inspired metaheuristic, is simple to implement and provides competitive baseline performance. However, it may suffer from premature convergence, rapid loss of population diversity, and rigid search transitions when solving complex multimodal or high-dimensional problems. This paper proposes the Modernized Tiki-taka Algorithm (MTTA), which incorporates a role-specialization mechanism inspired by contemporary football tactics into the optimization framework. MTTA adopts a Logistic–Tent hybrid chaotic mapping for uniform initial population distribution, and establishes a fitness-based three-role mechanism (forwards, midfielders, defenders) with tailored update rules, achieving a smooth, adaptive balance between exploration and exploitation throughout the iteration to fundamentally overcome TTA’s inherent flaws. Comprehensive experiments on classical benchmarks, the IEEE CEC 2017 test suite, constrained engineering problems, and feature selection tasks demonstrate that MTTA achieves statistically significant superiority over TTA, classic metaheuristics, and state-of-the-art optimizers in convergence speed, solution accuracy, and robustness, providing an efficient, scalable solution for complex real-world optimization scenarios. Full article

Collaborative 3D path planning for multiple unmanned aerial vehicles (UAVs) in dense urban airspace is difficult, which does not come from one factor alone. Buildings, flight restrictions, moving obstacles, and inter-UAV coupling all act together, and the search space grows quickly as the scene becomes more crowded. In such cases, a standard swarm optimizer may still find a path, but it often struggles with early feasibility, later-stage refinement, and local replanning after the environment changes. To deal with these issues, this paper develops a spatio-temporal collaborative improved multi-strategy dung beetle optimization algorithm, called STC-IMSDBO, for urban multi-UAV path planning. The framework combines five linked components: feasible-airspace population initialization, spatio-temporal variable-step search, multi-factor adaptive weighting, local game-based conflict handling, and rolling-horizon replanning. A normalized multi-objective cost is used to balance flight efficiency, smoothness, obstacle avoidance, airspace compliance, and cooperative safety. The method is tested in four simulated urban scenarios and compared with six representative methods. In the tested cases, the STC-IMSDBO generates shorter feasible routes, uses less energy, converges in fewer iterations, and maintains better cooperative safety than the comparison methods. These results suggest that the method is a useful planning option for dense urban missions such as logistics, inspection, and emergency response. That said, larger-swarm runtime tests and field validation are still needed. Full article

Aiming at the inherent limitations of the basic White Shark Optimizer (WSO), such as insufficient population diversity, unbalanced global and local search mechanisms, and weak convergence in the later stage, this paper proposes an Improved White Shark Optimizer (IWSO). The algorithm is improved from the following three aspects: Firstly, the Tent chaotic map is introduced to replace the traditional random initialization in the population initialization stage. Secondly, an adaptive nonlinear convergence factor and a dynamic inertia weight adjustment strategy are designed to focus on the fine search in the neighborhood of the optimal solution. Thirdly, the Levy flight perturbation mechanism and the elite opposition-based learning strategy are integrated to expand the search range and further accelerate the convergence speed. To verify the effectiveness and superiority of the IWSO algorithm, the CEC2017 test suite is selected for simulation experiments, and the IWSO is systematically compared with seven other representative swarm intelligence algorithms. The experimental results show that the IWSO is significantly superior to all comparison algorithms in multiple evaluation indicators, including minimum makespan, average convergence value, standard deviation, and successful convergence rate, on scheduling instances of different scales and difficulties. The convergence curve remains leading throughout the iteration process and shows a smoother convergence trend. The multi-strategy enhanced white shark optimizer proposed in this paper effectively overcomes the inherent defects of the basic algorithm, significantly improves the solution accuracy and convergence efficiency of the job shop scheduling problem, and has high theoretical research value and practical engineering application prospects. In the future, the multi-strategy improved White Shark Optimizer will be extended to multi-objective job shop scheduling, dynamic disturbance job shop scheduling, and large-scale production scheduling scenarios with numerous workpieces and machines. Full article

To address the shortcomings of the PSO algorithm, i.e., premature convergence and a tendency to fall into local optima, a collaborative particle regeneration strategy is introduced to help particles escape local optima. The principle of this strategy is as follows: if a particle in the population is detected to have not been updated for several iterations, information from a “leader” and a “follower” in the population is used to guide the particle out of the local optimum. Furthermore, to balance the global and local search capabilities of particles, the velocity update mechanism of the Bat Algorithm (BA) is incorporated, enabling particles to fully explore the solution space in the early stage and then quickly approach the optimal solution in the later stage. Simulation comparison experiments on the CEC 2017 benchmark suite demonstrate that the proposed improved PSO algorithm, combining these two enhancements, outperforms several other algorithms. In a task allocation simulation example, the proposed algorithm achieves an optimal fitness value of 170.89, verifying its efficiency and robustness under complex constraints. Full article

Real-world information diffusion frequently spans multiple heterogeneous platforms and relational layers, making multilayer influence maximization (MLIM) a critical and challenging problem. Existing methods for multilayer networks often rely on local structural signals for surrogate evaluation, failing to accurately characterize multi-hop diffusion and inter-layer coupling effects. In discrete combinatorial search, meta-heuristic random exploration often disrupts the structural inheritance and reuse of effective node configurations, compromising search stability and quality. To address these challenges, this paper proposes a Probabilistic-Based Multilayer Slime Mold Algorithm (PB-MSMA). It employs the slime mold algorithm as its search framework to perform discrete combinatorial optimization within a controlled candidate space. It utilizes the Preference-based Expected Diffusion Value (P-EDV) as a surrogate fitness metric during the search phase. This design reduces the need for repeated Monte Carlo simulations for iterative candidate evaluation while improving the characterization of inter-layer and higher-order diffusion effects. Furthermore, a probabilistic pipeline mechanism is introduced to encode recurring effective node configurations from historical searches as statistical priors, guiding the search process to enhance structural inheritance and stability. After the seed sets are obtained, the final influence spread of all compared methods is evaluated using 10,000 Monte Carlo simulations under the MLIC model. Experiments on six real-world multilayer network datasets and nine seed budgets show that PB-MSMA achieves a dataset-level improvement range of 3.68–14.50% over representative baselines, including CELF, DPSOMIM, Degree, DIRCI, and PRGC, with an average improvement of 10.32%. These results indicate that PB-MSMA provides an efficient seed-selection strategy for multilayer diffusion scenarios where repeated simulation-based evaluation is costly. Full article

This study proposes and validates a framework that integrates Grey Wolf Optimization (GWO) with Extreme Gradient Boosting (XGBoost) for estimating the Poisson’s ratio of auxetic structures. First, for 320 models derived from Computer-Aided Design-based (CAD-based) unit-cell designs, a systematic sweep of diameter and cellular dimensions was conducted to obtain porosity coverage in the 45–85% range. Subsequently, elastic modulus and Poisson’s ratio were computed via finite element analysis (FEA) at three mesh resolutions (0.20/0.25/0.30 mm), and relationships between design variables and outputs were examined using correlation heatmaps and Locally Weighted Scatterplot Smoothing (LOWESS) curves. GWO optimized the XGBoost hyperparameters through a multi-band narrowed search strategy; performance was evaluated using Mean Absolute Error (MAE), Root Mean Squared Error (RMSE), Mean Squared Error (MSE), and Coefficient of Determination ($R^2$) metrics, as well as residual diagnostics and Ground Truth–Prediction alignments for Poisson’s ratio. Across all configurations, $R^2\ge 0.994$ and absolute errors are on the order of ∼${10}^{-3}$; the 0.25 mm mesh stands out in terms of overall balance with the lowest squared-error profile and the highest $R^2$, the 0.30 mm mesh is practically equivalent in terms of MAE, and the 0.20 mm mesh is comparatively weaker. Residual diagnostics—comprising a pattern-free cloud around zero, slight right-skewness, and limited heteroskedasticity—indicate low bias and no substantive model-specification issues. The findings align with physical insight, confirming that Poisson’s ratio shifts toward more negative values as porosity increases and toward less negative values as diameter increases. The proposed GWO–XGBoost framework provides a reliable pre-screening tool for rapid design exploration and Poisson’s-ratio-targeted optimization, with the potential to reduce the need for additional FEA simulations and experimental iterations during early-stage design. Full article