Search Results (18)

In agricultural automation, trajectory planning for fruit-picking robot arms must satisfy dynamic obstacle avoidance and real-time control constraints in complex orchards, forming a high-dimensional, constrained optimization problem. Due to strong nonlinearity and steep gradients, traditional planners often yield high-cost trajectories with unstable quality. This paper introduces a Reinforced Arctic Puffin Optimization (RAPO) algorithm for trajectory planning in high-dimensional, complex, constrained scenarios. RAPO improves Arctic Puffin Optimization (APO), which uses a two-stage foraging strategy but may suffer premature convergence, insufficient population diversity, and weak boundary handling. Dynamic fitness–distance balance (DFDB) adaptively coordinates exploration and exploitation. An elite-pool dynamic search strategy (DEPSS) combines t-distribution perturbation and Lévy flight to maintain diversity and enhance exploitation. A convex-lens opposition-learning boundary control method (CLOBC) improves out-of-bounds handling and reduces invalid search. Stochastic centroid opposition learning (SOBL) further suppresses premature convergence and expands coverage. On the CEC2017 benchmark (30/50/100 dimensions), RAPO outperforms nine algorithms in convergence speed and solution quality, verified by Wilcoxon and Friedman tests. In dense, narrow, and dynamic obstacle scenarios, RAPO achieves the lowest path cost, converges within 30 iterations, reduces variance, and generates smoother trajectories. This case study demonstrates RAPO’s robust mathematical performance, providing a robust and efficient framework for agricultural picking robots. Full article

The Arctic Puffin Optimization (APO) Algorithm is a recently proposed metaheuristic algorithm that has been widely applied to solve optimization problems in continuous spaces. However, it cannot be directly used to solve combinatorial optimization problems in discrete spaces. To address these limitations, a Binary Arctic Puffin Optimization (BAPO) Algorithm is proposed, focusing on developing transfer functions to convert the algorithm’s continuous solutions into discrete binary solutions. Two primary transfer function types, S-shaped and V-shaped, are commonly employed. Experimental analysis identifies optimal functions for different algorithmic stages. These are then integrated with a conversion factor to propose a hybrid transfer function for the binarization of the Puffin Optimization Algorithm. To address the issue of slow particle convergence in the later stages of the exploration phase and the tendency to overlook high-quality solutions during the exploitation phase in the binary algorithm, logarithmic inertia weight and the golden sine strategy are incorporated, respectively, for improvement. Simulation experiments were conducted to solve both single-dimensional and multidimensional 0–1 knapsack problems. Experimental data and convergence curves, including mean values and standard deviations, were analyzed. The results demonstrate that the binary Arctic puffin optimization algorithm exhibits excellent convergence, stability, and fast search speed. Full article

Accurate daily runoff forecasting is essential for flood control and water resource management, yet existing models struggle with the seasonal non-stationarity and inter-basin variability of runoff sequences. This paper proposes a Season-Aware Ensemble Forecasting (SAEF) method that integrates SVM, LSSVM, LSTM, and BiLSTM models to leverage their complementary strengths in capturing nonlinear and non-stationary hydrological dynamics. SAEF employs a seasonal segmentation mechanism to divide annual runoff data into four seasons (spring, summer, autumn, winter), enhancing model responsiveness to seasonal hydrological drivers. An Improved Arctic Puffin Optimization (IAPO) algorithm optimizes the model weights, improving prediction accuracy. Beyond numerical gains, the framework also reflects seasonal runoff generation processes—such as rapid rainfall–runoff in wet seasons and baseflow contributions in dry periods—providing a physically interpretable perspective on runoff dynamics. The effectiveness of SAEF was validated through case studies in the Dongjiang Hydrological Station (China), the Elbe River (Germany), and the Quinebaug River basin (USA), using four performance metrics (MAE, RMSE, NSEC, KGE). Results indicate that SAEF achieves average Nash–Sutcliffe Efficiency Coefficient (NSEC) and Kling–Gupta efficiency (KGE) coefficients of over 0.92, and 0.90, respectively, significantly outperforming individual models (SVM, LSSVM, LSTM, BiLSTM) with RMSE reductions of up to 58.54%, 55.62%, 51.99%, and 48.14%. Overall, SAEF not only strengthens predictive accuracy across diverse climates but also advances hydrological understanding by linking data-driven ensembles with seasonal process mechanisms, thereby contributing a robust and interpretable tool for runoff forecasting. Full article

Rice husk ash (RHA) offers an eco-friendly way to improve concrete. Owing to the complex mix design of RHA concrete, accurately predicting its strength remains a challenge. This study addresses this need by compiling a dataset of 291 compressive strength records for RHA concrete. Using seven key input variables (e.g., cement, water, and RHA content), three novel hybrid models were developed by integrating the XGBoost algorithm with advanced metaheuristic optimizers: Northern Goshawk Optimization (NGO), Arctic Puffin Optimization (APO), and Catch Fish Optimization Algorithm (CFOA). These hybrid models were compared against classic Random Forest (RF), and Support Vector Regression (SVR), and unoptimized XGBoost models. The results demonstrated that all hybrid models significantly outperformed the unoptimized classic models. The APO–XGBoost model achieved the highest prediction accuracy on the testing set (RMSE = 3.5462, R² = 0.9579 on testing set), followed by CFOA–XGBoost and NGO–XGBoost. Cement content was revealed to be the most influential parameter on compressive strength, as determined by a sensitivity analysis, ahead of both water and coarse aggregate content. This research confirms the superiority of metaheuristic-optimized hybrid models for predicting the strength of RHA concrete, providing a reliable data-driven tool to support its mix design and promote its application in sustainable construction. Full article

The Arctic Puffin Optimization (APO) algorithm, proposed in 2024, is a swarm intelligence optimization. Similar to other swarm intelligence optimization algorithms, it suffers from issues such as slow convergence in the early stage, being easy to fall into local optima, and insufficient balance between exploration and exploitation. To address these limitations, an improved APO (IAPO) algorithm incorporating multiple strategies is proposed. Firstly, a mirror opposition-based learning mechanism is introduced to expand the search scope, improving the efficiency of searching for the optimal solution, which enhances the algorithm’s convergence accuracy and optimization speed. Secondly, a dynamic differential evolution strategy with adaptive parameters is integrated to improve the algorithm’s ability to escape local optima and achieve precise optimization. Comparative experimental results between IAPO and eight other optimization algorithms on 20 benchmark functions, as well as CEC2019 and CEC2022 test functions, show that IAPO achieves higher accuracy, faster convergence, and superior robustness, securing first-place average rankings of 1.35, 1.30, 1.25, and 1.08 on the 20 benchmark functions, CEC 2019, 10- and 20-dimensional CEC 2022 test sets, respectively. Finally, simulation experiments were conducted on three engineering optimization design problems. IAPO achieved optimal values of 5.2559 × 10⁻¹, 1.09 × 10³, and 1.49 × 10⁴ for these engineering problems, ranking first in all cases. This further validates the effectiveness and practicality of the IAPO algorithm. Full article

The precise reconstruction of target scattering centers (TSCs) using sensors plays a crucial role in feature extraction and identification of non-cooperative targets. Radar sensor networks (RSNs) are well suited for this task, as they are capable of illuminating targets from multiple aspect angles and rapidly capturing reflected signals. However, the complex geometry and diverse material composition of real-world targets result in significant variations in the radar cross-section (RCS) observed at different angles. Although these RCS responses are interrelated, they exhibit considerable angular diversity. Furthermore, achieving precise spatiotemporal registration and fully coherent processing is infeasible for RSNs composed of small mobile sensor platforms, such as drone swarms. Therefore, an intelligent algorithm is required to extract and accumulate correlated and meaningful information from the target echoes received by the RSN. In this work, a novel collaborative TSC reconstruction framework for RSNs is proposed. The framework performs similarity evaluation on wide-angle high-resolution range profiles (HRRPs) to achieve adaptive angular segmentation of TSC models. It combines the expectation–maximization (EM) algorithm with an enhanced Arctic puffin optimization (EAPO) algorithm to effectively integrate echo information from the RSN in a non-coherent manner, thereby enabling accurate TSC estimation. The proposed method outperforms existing mainstream approaches in terms of spatiotemporal registration requirements, estimation accuracy, and stability. Comparative experiments on measured datasets demonstrate the robustness of the framework and its adaptability to complex target scattering characteristics, confirming its practical value. Full article

The Parrot Optimizer (PO) is a new optimization algorithm based on the behaviors of trained Pyrrhura Molinae parrots. In this paper, an improved PO (IPO) is proposed for solving global optimization problems and training the multilayer perceptron. The basic PO is enhanced by using three improvements, which are aerial search strategy, modified staying behavior, and improved communicating behavior. The aerial search strategy is derived from Arctic Puffin Optimization and is employed to enhance the exploration ability of PO. The staying behavior and communicating behavior of PO are modified using random movement and roulette fitness–distance balance selection methods to achieve a better balance between exploration and exploitation. To evaluate the optimization performance of the proposed IPO, twelve CEC2022 test functions and five standard classification datasets are selected for the experimental tests. The results between IPO and the other six well-known optimization algorithms show that IPO has superior performance for solving complex global optimization problems. The results between IPO and the other six well-known optimization algorithms show that IPO has superior performance for solving complex global optimization problems. In addition, IPO has been applied to optimize a multilayer perceptron model for classifying the oral English teaching quality evaluation dataset. An MLP model with a 10-21-3 structure is constructed for the classification of evaluation outcomes. The results show that IPO-MLP outperforms other algorithms with the highest classification accuracy of 88.33%, which proves the effectiveness of the developed method. Full article

Papillomaviruses are small, circular DNA viruses that infect epithelial and mucosal cells, which have co-evolved with their hosts over time. While certain mammalian papillomaviruses—especially those linked to disease—are well studied, there is limited knowledge about papillomaviruses associated with avian species. In this study, we identified two avian papillomaviruses from eye/choana swabs of the sacred kingfisher (Todiramphus sanctus) and the little corella (Cacatua sanguinea), collected in Queensland, Australia. The genomes of these viruses, designated as todiramphus sanctus papillomavirus 1 (TsPV1) and cacatua sanguinea papillomavirus 1 (CsPV1), were found to be 7883 and 7825 base pairs in length, respectively. The TsPV1 and CsPV1 genomes exhibited the highest nucleotide sequence identity (>56%) with papillomavirus genomes previously sequenced from mallards or wild ducks in the United States, followed by those from black-legged kittiwakes and Atlantic puffins (>54%) in Newfoundland, Canada. Both TsPV1 and CsPV1 share approximately a 65% nucleotide sequence identity in the L1 gene with anas platyrhynchos papillomavirus 3 (AplaPV3), indicating that they represent novel avian papillomaviruses. Notably, the two genomes in this study were nearly identical (99.69%), and their L1 proteins shared 100% sequence identity. Phylogenetic analysis positioned TsPV1 and CsPV1 within a clade of avian papillomaviruses associated with closely related avian hosts, including the mallard, African grey parrot, common chaffinch, and Atlantic canary. These findings underscore the importance of further research on studying additional Australian bird species longitudinally, which will help to establish potential disease associations and ecological impacts of previously unrecognised and novel papillomaviruses in Australian wild birds. Full article

The Atlantic puffin (Fratercula arctica) is a seabird species characterized by great diving capabilities and transoceanic migratory behavior. These movements contribute to the dispersion of the species during migration, and episodes of mortality associated with migration may be a normal event in the dynamic of the Atlantic puffin populations. This study aimed to describe the anatomopathological findings of an unusual mortality event of Atlantic puffins observed during the non-breeding period along the coast of the Canary Islands. The most consistent gross finding during necropsy was generalized muscle atrophy and fat depletion. The main histological findings were centered in the urinary tract, with dilation and inflammation of the primary ureter branch and medullary cones, and intraluminal trematodes identified as Renicola sloanei based on morphology and molecular analysis. Influenza virus infection was ruled out. The postmortem investigations performed in this mortality event of Atlantic puffins indicate that the animals were severely emaciated and suffered from nephropathy. The etiopathological investigation performed in relation to this mortality event of Atlantic puffins indicates starvation associated with bad weather conditions during migratory movement as the most likely cause of the unusual mortality event. Full article

Power network codes necessitate that any renewable source aligns with LVRT rules and assists in voltage restoration during voltage dips. This paper focuses on increasing the low-voltage ride through capability of a doubly fed induction generator-based wind turbine. Three different controllers are discussed in this article. The first is based on robust super-twisting sliding mode control, which is a recent robust control technique. The second uses a new metaheuristic optimizer called the Arctic Puffin optimizer (APO), and the third relies on the traditional PI controller. The grid-side converter sustains the potential of the DC converter link and the regulation of both the active and reactive power supplied to the power grid via three controllers. The rotor-side converter regulates the generator’s electromagnetic torque via two controllers. Doubly fed induction generator control is a challenging task as the two converters have five controllers, and it is vital to specify the ideal parameters for each controller. In the case of super-twisting sliding mode control, the APO is utilized to obtain the sliding surfaces needed for the five controllers. Moreover, the APO is exploited to obtain the optimal constants of the suggested PI regulators. The simulation results prove the excellent performance of both super-twisting- and APO-based controllers, with better performance demonstrated with super-twisting sliding mode control, which demonstrates excellent transient performance with the least overshoot among the three controllers. The super-twisting-based controller has a distinct feature, as it has good performance with parameter variations. Full article

Examining contaminant concentrations in birds in Arctic environments is important for managing species for assessing long-term trends. Recent reports on mercury (Hg) concentrations in Arctic species of seabirds has identified a need for data from missing regions or species. We measured arsenic (As), cadmium (Cd), chromium (Cr), lead (Pb), manganese (Mn), Hg and selenium (Se) in the body feathers of Atlantic Puffin (Fratercula arctica) from four colonies in Iceland in 2011 and one in 2009. Puffins forage on small fish at an intermediate trophic concentration. We found that (1) concentrations examined in the colony in 2009 were lower than in 2011 for all metals except As and Hg, and (2) concentrations of Cd and Se varied significantly among colonies for feathers collected in 2011. Pb concentrations in Puffin feathers in one colony were 14-fold higher in 2009 than in 2011 (mean of 805 ng.g⁻¹ vs. 58 ng.g⁻¹). The highest mean Hg concentration in 2011 was 362 ng.g⁻¹ and was 4880 ng.g⁻¹ for Se. The concentrations of Hg in the Atlantic Puffins reported in this study were similar to, or lower than those reported for the same species elsewhere and for Tufted Puffin from the Pacific. Full article

This study presents an innovative hybrid evolutionary algorithm that combines the Arctic Puffin Optimization (APO) algorithm with the JADE dynamic differential evolution framework. The APO algorithm, inspired by the foraging patterns of Arctic puffins, demonstrates certain challenges, including a tendency to converge prematurely at local minima, a slow rate of convergence, and an insufficient equilibrium between the exploration and exploitation processes. To mitigate these drawbacks, the proposed hybrid approach incorporates the dynamic features of JADE, which enhances the exploration–exploitation trade-off through adaptive parameter control and the use of an external archive. By synergizing the effective search mechanisms modeled after the foraging behavior of Arctic puffins with JADE’s advanced dynamic strategies, this integration significantly improves global search efficiency and accelerates the convergence process. The effectiveness of APO-JADE is demonstrated through benchmark tests against well-known IEEE CEC 2022 unimodal and multimodal functions, showing superior performance over 32 compared optimization algorithms. Additionally, APO-JADE is applied to complex engineering design problems, including the optimization of engineering structures and mechanisms, revealing its practical utility in navigating challenging, multi-dimensional search spaces typically encountered in real-world engineering problems. The results confirm that APO-JADE outperformed all of the compared optimizers, effectively addressing the challenges of unknown and complex search areas in engineering design optimization. Full article

Moulting is a crucial yet challenging life-history trait to study in seabirds, particularly in the wild. Public aquariums offer valuable opportunities to collect detailed data, which, although not directly transferable to wild populations, provide important insights. At Oceanário de Lisboa, six Fratercula arctica individuals were monitored over seven years to document moulting patterns. The start and end of each moult were consistently recorded around the spring and autumn equinoxes. Pre-alternate moults lasted between 17 and 73 days, while pre-basic moults ranged from 11 to 48 days, with primary moults occurring between the two. This study is the first to document an asynchrony between the primary and the pre-alternate moults in F. arctica, highlighting a previously unreported aspect of the species’ moulting process. This seven-year time series and its findings prompt a call for action for further studies in controlled conditions, to investigate this pattern under different conditions and across puffins’ life stages. Such data could be crucial for developing more effective conservation strategies for this vulnerable species. These findings emphasize the importance of continued monitoring and research on ex situ puffin populations to expand our understanding of their moulting behaviour and its implications for wild populations. Full article

The Atlantic puffin is a medium-sized seabird with black and white plumage and orange feet. It is distributed mainly along the northern Atlantic Ocean, and due, among other reasons, to human activities, it is in a threatened situation and classified as a vulnerable species according to the International Union of Conservation of Nature (IUCN). In this study, we used a total of 20 carcasses of juvenile Atlantic puffins to perform MRI, as well as anatomical cross-sections. Thus, an adequate description of the head was made, providing valuable information that could be helpful as a diagnostic tool for veterinary clinicians, who increasingly treat these birds in zoos, rehabilitation centers, and even in the wild. Full article

In birds, unlike mammals, there is no complete separation between the thoracic and abdominal cavities. Instead, they have the coelomic cavity where most main organs are found. Therefore, an adequate knowledge of the anatomy of the coelomic cavity is of great importance for veterinarians, biologists and the scientific community. This study aimed to evaluate the coelomic cavity anatomy in the Atlantic puffin (Fratercula arctica) using anatomical sections and computed tomography images. Full article