Traditional surgical video recording requires professional personnel to manually adjust the camera capturing position, which leads to low recording efficiency and interference with the surgical process. To address these issues, this study proposes an automatic surgical video capture scheme based on an intelligent surgical video capture robot system. The method utilises the YOLO v5 algorithm and a 3D camera to identify and localise the surgical field area. Additionally, Convex Hull Edge Line Search (CHELS) algorithm is constructed to rapidly solve the optimal capturing position while avoiding obstacles. Finally, Position-Based Visual Servoing (PBVS) is applied to control the camera for precise capture of the surgical field. The detection model for the surgical field achieved an mAP@0.5 of 0.962, and the average solution time of the CHELS is 0.1469s. Automatic surgical video capture schemes can efficiently enable the intelligent and automatic recording of surgical videos.

An event-based model and hybrid genetic search algorithm for an inland multi-size container transportation problem

A dynamic clustering search algorithm for the rectangular strip packing problem

Mixed Copula for streamflow simulation based on intelligent knowledge set and parameter calibration using cooperation search algorithm

A multi-agent deep reinforcement learning driven adaptive construction and search algorithm for time-varying agile Earth observation satellite scheduling

Earth observation satellite scheduling problem for multitemporal revisit tasks: A variable neighborhood search algorithm

In electroencephalography (EEG) with dry electrodes, a tradeoff between signal stability and user comfort is a critical barrier to long-term, wearable applications. While various approaches exist, the mechanical impact of electrode tip geometry has not been adequately quantified. Moreover, while existing evaluations utilize subjective feedback, which is an indispensable metric for assessing user comfort, quantitative, mechanics-based analyses that complement these findings have not yet been commonly established. The current study aimed to evaluate the mechanical influence of different electrode tip geometries under both vertical and tilted contact conditions. Finite element analysis was conducted using strain energy density (SED), a mechanical index known to correlate with neural impulse activity, as a quantitative indicator of the mechanical influence of tip geometry on the skin. Six types of electrode tip geometries, ranging from flat to hemispherical, were defined based on the ratio of fillet radius to prong radius. These geometries were analyzed under inclination angles from 0 deg to 5 deg, and their peak SED values were compared. Building on these initial trends, an iterative search algorithm was employed to identify the geometry ratio that tends to minimize peak SED across extended inclination angles up to 15 deg, evaluated as a design boundary. The findings indicate that intermediate fillet geometries tend to reduce peak SED under inclined conditions. While hemispherical tips appear favorable at inclination angles beyond 15 deg, intermediate geometries may offer improved load distribution within the inclination range of 0 deg to 10 deg evaluated in this study.

Unbiased censored regression Euclidean direction search algorithm

The development of renewable energy in rural areas presents significant potential. Integrating renewable energy sources, such as wind power and photovoltaics, into microgrids as distributed generation systems offers a viable approach for local energy utilization. In recent years, the rapid advancement of agriculture, forestry, animal husbandry, and fisheries has led to an increasing demand for electricity in these regions. However, the existing power infrastructure remains underdeveloped, resulting in a pronounced imbalance between supply and demand. This paper investigates the optimization of rural microgrid configurations by incorporating demand response strategies and the synergistic interactions among wind turbines, photovoltaic systems, batteries, and loads. A multi-objective optimization model is developed to maximize annual profits and environmental externality (namely, the proposed microgrid achieves equivalent carbon dioxide emissions reductions by replacing thermal power generation through either selling green electricity to the main grid or meeting rural load demands), which is subsequently transformed into a single-objective formulation using the Shapley value method and solved via a global harmonic search algorithm. Simulation results validate the applicability of the proposed solution method and demonstrate the economic performance, development potential, and environmental benefits of the optimized microgrid configurations.

Melanoma is one of the most aggressive forms of skin cancer, with a high mortality rate when not detected early. This public health challenge underscores the need for accurate and efficient diagnostic tools. Convolutional Neural Networks have shown strong performance in medical image analysis. However, their effectiveness relies heavily on optimal architectural and hyperparameter configurations, which are often designed without alignment to the target domain or transferred from unrelated domains, limiting adaptability to specific medical datasets. Existing hybrid CNN-metaheuristic approaches typically optimize only fixed network parameters. They often fail to explore how metaheuristics can adaptively shape the CNN architectures themselves.In this study, a comprehensive hybrid optimization framework is proposed that integrates CNNs with six nature-inspired metaheuristic algorithms that mimic biological or physical phenomena to solve complex problems. These include Cuckoo Search, Firefly Algorithm, Whale Optimization Algorithm, Particle Swarm Optimization, Grey Wolf Optimizer, and Crow Search Algorithm. Rather than tuning a predefined architecture, each optimizer searches the architectural and training space to identify high-performing CNN configurations, enabling emergent and data-driven network design. This unified framework allows a systematic cross-algorithm comparison under identical conditions, providing new insights into convergence stability, exploration-exploitation dynamics, and generalization behavior. A robust preprocessing and data augmentation pipeline, including brightness normalization, hair artifact removal, and geometric transformations, is incorporated to improve model generalization and enhance the optimizer's search landscape. Experiments on the HAM10000 dataset demonstrate that the metaheuristic-optimized CNNs outperform the baseline, achieving accuracies up to 91.25%. These findings confirm that population-based optimization is an efficient and reliable mechanism for guiding CNN architecture design. This approach achieves superior performance compared to traditional manual or other optimization-based strategies.

A discrete crow search algorithm for solving the uncapacitated facility location problem

Abstract We report the discovery of three Milky Way satellite candidates: Carina IV, Phoenix III, and DELVE 7, in the third data release of the DECam Local Volume Exploration survey (DELVE). The candidate systems were identified by cross-matching results from two independent search algorithms. All three are extremely faint systems composed of old, metal-poor stellar populations ( τ ≳ 10 Gyr, [Fe/H] ≲−1.4). Carina IV ( M V = −2.8; r 1/2 = 40 pc) and Phoenix III ( M V = −1.2; r 1/2 = 19 pc) have half-light radii that are consistent with the known population of dwarf galaxies, while DELVE 7 ( M V = 1.2; r 1/2 = 2 pc) is very compact and seems more likely to be a star cluster, though its nature remains ambiguous without spectroscopic follow-up. The Gaia proper motions of stars in Carina IV ( M ⋆ = 225 0 − 830 + 1180 M ⊙ ) indicate that it is unlikely to be associated with the LMC, while DECam CaHK photometry confirms that its member stars are metal poor. Phoenix III ( M ⋆ = 52 0 − 290 + 660 M ⊙ ) is the faintest known satellite in the extreme outer stellar halo ( D GC > 100 kpc), while DELVE 7 ( M ⋆ = 6 0 − 40 + 120 M ⊙ ) is the faintest known satellite with D GC > 20 kpc.

Bi-level optimization research area has become increasingly popular, largely due to its effectiveness in modeling and solving real-world problems. This framework provides a hierarchical structure involving two decision-makers (i.e., upper and lower levels) that govern together to find an optimal solution to complex optimization problems. Most resolution methods proposed in the literature adhere to this hierarchical structure, which limit their applicability only to small-scale instances of the problem. Among these resolution strategies, we highlight an interesting evolutionary algorithm known as CODBA, which focuses on decomposing the lower-level search space into several parts that evolve in parallel to address the high complexity of the nested structure. In this paper, we enhance the searching capabilities of CODBA by proposing a novel evolutionary reinforcement learning approach that integrates the core CODBA scheme with a Q-learning strategy, presenting a promising method for training intelligent search algorithms for bi-level optimization problems. The computational statistical experiments are performed on bi-level multi-depot vehicle routing problem, demonstrated the effectiveness of our solution approach in terms of computation time and solution quality compared to existing algorithms.

Coarse-grained (CG) force field models are extensively utilized in material simulations because of their scalability. Ordinarily, these models are parametrized using hybrid strategies that sequentially integrate top-down and bottom-up approaches. However, this combination restricts the capacity to jointly optimize all parameters. Although Bayesian optimization (BO) has been explored as an alternative search strategy to identify well-optimized CG parameters, its application has conventionally been limited to low-dimensional scenarios. This has contributed to the assumption that BO is unsuitable for more complex CG models, which often involve a large number of parameters. In this study, we challenge this assumption by successfully extending BO, using the tree-structured Parzen estimator (TPE) model, to optimize a high-dimensional CG model. Specifically, we show that a 41-parameter CG model of Pebax-1657, a copolymer composed of alternating polyamide and polyether segments, can be effectively parametrized using BO, resulting in a model that accurately reproduces the key physical properties of its parent atomistic representation. Our optimization framework simultaneously targets structural and thermodynamic properties, namely, density, radius of gyration, and glass transition temperature. Compared to traditional search algorithms, BO-TPE not only converges faster but also delivers consistent improvements over more standard parametrization approaches.

To address the strongly coupled and highly nonlinear optimization problems arising from the increasing system complexity, optimization objectives, and variable dimensions in practical engineering applications, this paper proposes a multi-strategy enhanced NSGA-III algorithm (MSNSGA-III) by introducing K-means clustering, an adaptive hybrid operator, and an assistant evolutionary population strategy on the basis of the NSGA-III algorithm. This algorithm overcomes the performance limitations of the original algorithm in large-scale search with multiple variables. By employing the DTLZ test functions with different variable dimensions and conducting comparisons with six other representative algorithms, the proposed algorithm is proven to have strong competitiveness in terms of diversity and convergence speed. To reflect the superiority of the algorithm in practical applications, this paper establishes a variable-thickness optimization model for the morphing leading edge. By adopting the spline curve-based optimization variable control strategy and the MSNSGA-III algorithm, the optimal thickness distribution of the leading edge skin is obtained. The results show that, compared with the leading edge with a fixed skin thickness of 1.5 mm, the optimized variable thickness skin leading edge achieves 43.6% improvement in shape maintaining accuracy, 40.9% improvement in deformation accuracy, and 17.5% reduction in driving force.

To address the significant operational disruptions caused by inclement weather in maritime logistics, this study investigates the integrated rescheduling optimization of vessels and tugboats within one-way channel ports. The research aims to minimize total operational costs, including dispatching and delay penalties, by synchronizing vessel movements with tugboat service capabilities under uncertain conditions. Methodologically, a rolling horizon decision-making mechanism is proposed to accommodate dynamic operational scenarios driven by fluctuating weather. On this basis, an integrated rescheduling model is developed to address the compounded challenges of navigation rule changes, channel closures, vessel delays, and additional shifting tasks. The model explicitly incorporates critical constraints such as channel navigation protocols, tugboat availability, power capacity limits, and tidal windows for deep-draft vessels. To achieve efficient solution generation, an improved Variable Neighborhood Search (VNS) algorithm is designed to effectively handle the problem’s complexity. Experimental results validate the effectiveness of the proposed approach and the robustness of the algorithm in diverse disruption scenarios. Furthermore, sensitivity analyses reveal how channel closure duration, vessel delay intensities, and the volume of shifting tasks quantitatively influence rescheduling outcomes. This study contributes a novel synergistic optimization framework that enhances the operational resilience and decision-making capabilities of port authorities.

Retraction Note: ACO–MKFCM: An Optimized Object Detection and Tracking Using DNN and Gravitational Search Algorithm

ABSTRACT A mobile ad hoc network (MANET) is a collection of mobile nodes that connect to one another over wireless networks. A number of researches have been suggested for enhancing the reliability among routing nodes, trust management, cryptographic systems application, and centralized routing decisions. Nevertheless, most of the routing approaches are challenging to implement in practical scenarios because it is problematic to find out the malevolent activities of the routing nodes. Therefore, a blockchain (BC) and dual‐discriminator conditional generative adversarial network optimized with momentum search algorithm (TDRS‐BCDCGAN‐MSA‐MANET) is proposed for delivering the reliable distributed routing‐based information using BC token transactions. The aim is to demonstrate an efficient data transmission method that creates tokens for packet stream admission using a secret key that is provided to each routing mobile node. The proof of continuous work (PoCW) consensus–based BC technology is used to provide the reliable routing information. Then dual‐discriminator conditional generative adversarial network (DCGAN) is employed for identifying optimal routes in MANET. Then momentum search algorithm (MSA) is proposed for enhancing the weight parameters of DCGAN for improving the efficiency of best route identification. The simulation of the proposed system is performed in network simulator‐2 and the metrics like delay, average latency, average energy consumption, and throughput of BC token transactions are evaluated. The TDRS‐BCDCGAN‐MSA‐MANET approach has attained 24.6%, 21.7%, and 16.8% lower delay and 41.087%, 39.87%, and 36.85% low energy consumption than the existing models.

Cloud Computing and the Internet of Things play pivotal roles in advancing the healthcare system through enhanced observation mechanisms. These mechanisms can be implemented using various algorithms, including the Sparrow Search Algorithm (SSA), Goal Programming Algorithm (GPA) and Reptile Search Algorithm (RSA) etc. Task scheduling is one of the major challenges in cloud computing, as solving this problem requires reducing costs while meeting deadlines. Efficient task scheduling is essential to optimize resource utilization and ensure timely completion of tasks. To overcome this challenge, this study presents an innovative algorithm named Adaptive Parameter Control Reptile Search Algorithm (APC-RSA) which is designed to optimize healthcare tasks scheduling by achieving a balance between time and cost as well as ensuring tasks completion within deadlines. The performance of APC-RSA is driven by dynamic parameter adjustment that balances exploration and exploitation during optimization. This study evaluates the effectiveness of APC-RSA demonstrating the significant improvements over existing algorithms like SSA, GPA and RSA. Experimental results indicate that APC-RSA achieves a minimized cost of 17,061.68 with a 99.83% task success rate. In comparison, RSA achieves a cost of 45,821.17 with a 99.70% success rate, SSA achieves a cost of 50,411.52 with a 99.67% success rate, and GPA incurs a cost of 138,448.73 with a 95.37% task success rate. The findings suggest that APC-RSA has the potential to significantly enhance cloud task scheduling in the healthcare sector, offering a cost-effective and reliable solution to improve global healthcare systems.

Abstract In normative models a decision-maker is usually assumed to be Bayesian rational, and so to maximize subjective expected utility, within a complete and correctly specified decision model. Following the discussion in Hammond (HEI 179–195, 2007) of Schumpeter’s (Theorie der wirtschaftlichen Entwicklung; Eine Untersuchung über Unternehmergewinn, Kapital, Kredit, Zins und den Konjunkturzyklus, Leipzig, 1911; The Theory of Economic Development: An Inquiry into Profits, Capital, Credit, Interest, and the Business Cycle, 1934) concept of entrepreneurship, as well as Shackle’s (Economica NS, 20:112-117, 1953) concept of potential surprise, we consider enlivened decision trees whose growth over time cannot be accurately modelled in full detail. An enlivened decision tree involves more severe limitations than a mis-specified model, unforeseen contingencies, or unawareness, all of which are typically modelled with reference to a universal state space large enough to encompass any decision model that an agent may consider. We consider a motivating example based on Homer’s classic tale of Odysseus and the Sirens. Though our novel framework transcends standard notions of risk or uncertainty, for finite decision trees that may be truncated because of bounded rationality, an extended and refined form of Bayesian rationality is still possible, with real-valued subjective evaluations instead of consequences attached to terminal nodes where truncations occur. Moreover, these subjective evaluations underlie, for example, the kind of Monte Carlo tree search algorithm used by recent chess-playing software packages. They may also help rationalize the contentious precautionary principle.