Search Results (87)

The coloring process in cherry fruit affects both fruit quality and the cherry fly (Rhagoletis cerasi). This is critically important for host preferences of major pests such as cherry fly. In this study, a randomized classification method was used to classify five distinct coloration stages of cherry fruit based on pomological characteristics and cherry fly density data. An explainable hybrid model combining Random Forest (RF) and Ant Colony Optimization (ACO) algorithms has been developed. In this study, fruit samples of the Ziraat 900 variety were collected from four different cherry orchards in Elazığ province during five different coloration stages. Pomological characteristics such as weight, width, length, height, stem length, firmness, seed weight, soluble solids content (SSC), NaOH, and acidity were determined, and adult cherry fly densities were also recorded. In the proposed method, candidate decision rules generated by the RF algorithm were optimized using the ACO algorithm, and the most distinctive rule sets were selected. The results showed that the proposed RF-ACO model achieved a 99.48% accuracy rate and exhibited higher performance than many common machine learning methods. Feature significance analysis revealed that SSC, cherry fly density, NaOH, and acidity were the most effective parameters in the classification process. The model not only provided high accuracy thanks to the explainable IF-THEN rules it generated, but also allowed for expert interpretation of the decision-making process. The findings offer significant contributions to the development of decision support systems for determining cherry ripening periods and controlling the cherry fruit fly. Full article

Enhancing oil and gas production performance is essential for maintaining the economic sustainability of petroleum enterprises and meeting the increasing global energy requirements. In this context, subsurface production optimization constitutes a fundamental component of strategic reservoir management, directly affecting critical decisions such as well location design and the regulation of operational parameters. Nevertheless, conventional reservoir optimization approaches are frequently constrained by high computational costs and limited optimization effectiveness. To overcome these limitations, evolutionary algorithms have gained considerable attention for addressing complex optimization tasks, owing to their gradient-free nature and strong capability for parallel exploration. This paper proposes a chaotic exploitation orthogonal learning fruit fly optimization algorithm (COFOA) tailored for global optimization and oil and gas production optimization. Specifically, we integrate a chaotic exploitation mechanism and an orthogonal learning strategy to improve the balance between exploration and exploitation. Following the population update in FOA, the chaotic exploitation mechanism is first applied to help the population escape local optima and enhance search efficiency. Subsequently, the orthogonal learning strategy is employed to strengthen the algorithm’s exploitation capability. To evaluate the performance of the improved FOA, extensive experiments were conducted on benchmark functions from IEEE CEC 2017 and IEEE CEC 2022, including ablation studies, scalability tests and comparisons with state-of-the-art algorithms. The results demonstrate that the proposed FOA significantly outperforms competing algorithms in optimizing reservoir production. COFOA demonstrates consistent performance superiority over all compared algorithms in terms of mean NPV. Specifically, it achieves improvements of approximately 2.35% to 16.23% compared with existing methods. Notably, COFOA outperforms strong competitors such as mSCA and BLPSO by 2.35% and 3.81%, respectively, while achieving more significant gains over algorithms such as SCADE (15.31%) and CCMSCSA (16.23%). Even when compared with relatively competitive methods like HGWO and CCMWOA, COFOA still maintains performance improvements of 4.79% and 6.12%, respectively. These results clearly demonstrate the superior optimization capability of COFOA in terms of maximizing NPV under complex reservoir conditions. Full article

The European cherry fruit fly (Rhagoletis cerasi L.) poses a significant pest threat to cherry production due to its rapid reproduction and host specificity, causing substantial economic damage. This study presents a novel, explainable, and biologically inspired data-driven classification model based on fruit characteristics to support targeted and sustainable pest control strategies. In research conducted at four different locations in Elazığ province, three population classes were determined based on the number of adult individuals caught in traps, and 10 different fruit characteristics were measured in fruit samples belonging to each class. The data used in this study are original data obtained by the authors. To examine the relationship between pomological characteristics of cherry fruit and cherry fruit fly density, the Chaotic Rule-based–Strength Pareto Evolutionary Algorithm2 (CRb-SPEA2) method, developed as a multi-objective and chaos-integrated evolutionary rule mining framework, was adapted. The developed algorithm aimed for high performance, interpretability, and transparency. Accuracy, Precision, and Recall metrics, which are conflicting objectives, were optimized with Pareto-optimal solutions, yielding selectable results for domain experts. To increase population diversity and reduce the risk of early convergence and getting stuck in a local optimum, the Tent chaotic mapping mechanism was also integrated into the system. Furthermore, the model was trained without the need for predefined automatic discretization of the continuous value ranges of the attributes. The proposed model achieved superior results across all classes, with the highest accuracy rate of 82.6% recorded in the High class, demonstrating excellent sensitivity and recall values. Full article

The high penetration of photovoltaic and wind power introduces strong non-stationarity and multi-scale fluctuations into power system load profiles, challenging the accuracy of short-term load forecasting (STLF). To address this, we propose a hybrid forecasting framework, IMVMD-XLSTM, which synergistically integrates an optimized multivariate decomposition with an advanced neural network. First, to address the critical issue that MVMD performance is highly sensitive to its parameter settings, which impacts decomposition quality, a multi-strategy Improved Fruit Fly Optimization Algorithm (IFOA) is developed to task-oriented adaptively tune the key parameters of MVMD, forming an Improved MVMD (IMVMD). This optimization aims to ensure decomposition stability and maximize the relevance for the subsequent forecasting task. Second, to fully leverage the characteristics of the frequency-aligned, multi-channel sub-sequences generated by IMVMD, an Extended LSTM (XLSTM) network is designed. Its serially arranged BisLSTM and mLSTM units are specifically tailored to capture the bidirectional long-term dependencies within each stable sub-sequence and the complex high-dimensional interactions across the aligned sub-sequences, respectively. Evaluated on 15 min resolution data from the Austrian grid, the proposed IMVMD-XLSTM framework achieves a day-ahead forecasting Mean Absolute Percentage Error (MAPE) of 2.45% (±1.41%). This study provides a verifiable and effective solution that couples data-adaptive signal processing with a purpose-built neural architecture to enhance forecasting reliability in renewable-rich power systems. Full article

Faced with the contradiction between the increasingly growing demand and labor-intensive manufacturing modes, in the current era of rapid development of informatization and artificial intelligence, improving manufacturing efficiency by means of automated manufacturing equipment has become a recognized development direction for most shipyards. This trend is particularly evident in the manufacturing of sub-assemblies, which are the smallest composite units of the hull. Taking an automated sub-assembly welding line in a shipyard as the research object, this paper constructs a mathematical model aimed at optimizing production efficiency based on the analysis of its operational processes and characteristics and proposes an improved two-layer fruit fly optimization algorithm (ITLFOA) for solving the automated sub-assembly welding line scheduling problem (ASWLSP). The proposed ITLFOA features a two-layer nested algorithm structure, with several key improvements proposed for both optimization layers, such as heuristic rules for spatial layout, improved neighborhood operators, an added disturbance mechanism, and an added population diversity restoration mechanism. Finally, the performance of ITLFOA is validated through a comparative analysis against the initial two-layer fruit fly optimization algorithm (initial TLFOA), the well-established Variable Neighborhood Search (VNS) algorithm and the actual manual operation results on a specific case of a shipyard. Full article

The olive fruit fly Bactrocera oleae (Rossi) (Diptera: Tephritidae) is the primary pest of olive crop globally, causing serious economic losses each year. Early and accurate detection of this pest is essential for implementing integrated management strategies and minimizing the use of chemical inputs. In this context, the application of advanced technologies such as computer vision and machine learning through modelling emerges as a promising solution for monitoring and managing this pest. However, the absence of a robust and efficient dataset has hindered the development of reliable models for its recognition. This study details the creation procedure of a dataset comprising 2440 images collected from field and laboratory environments, along with data augmentation and training of three different models using machine learning algorithms. The models were implemented with YOLOv5 and optimized with different versions (s, m, and epoch). All three models achieved accuracy exceeding 90%. The optimisation process, which combined different YOLOv5 versions (s and m) and epochs (300 and 150), determined that the model trained with the s version and 300 epoch provided the best trade-off between accuracy, robustness, and computational efficiency. This makes it the most suitable option for implementation on low-cost, resource-limited platforms such as the Raspberry Pi. This study represents a step toward the integration of artificial intelligence into olive cultivation, bringing significant benefits to both producers and the environment. This study differs from previous YOLOv5-based pest detection research by providing a heterogeneous dataset that combines field and laboratory conditions, and by validating its deployment on a low-cost embedded platform (Raspberry Pi), thus enabling practical automation in Integrated Pest Management (IPM) systems. Full article

With the large-scale integration of wind power into the grid, the accuracy of wind farm cluster power prediction has become a key factor for the sustainability of modern power systems. Reliable ultra-short-term forecasts support the secure dispatch of high-penetration renewable energy, reduce wind curtailment, and improve the low-carbon and economical operation of power systems. Aiming at the problem of significant differences in wind turbine characteristics, this paper proposes a prediction method based on an improved density-based spatial clustering of applications with noise (DBSCAN) and a hybrid deep learning model. First, the wind speed signal is decomposed at multiple scales using successive variational modal decomposition (SVMD) to reduce non-stationarity. Subsequently, the DBSCAN parameters are optimized by the fruit fly optimization algorithm (FOA), and dimensionality reduction is performed by principal component analysis (PCA) to achieve efficient clustering of wind turbines. Next, the representative turbines with the highest correlation are selected in each cluster to reduce computational complexity. Finally, the SVMD-TCN-BiGRU-MSA-GJO hybrid model is constructed, and long-term dependence is extracted using a temporal convolutional network (TCN); the temporal features are captured by bidirectional gated recurrent units (BiGRUs); the feature weights are optimized by a multi-head self-attention mechanism (MSA), and the hyper-parameters are, in turn, optimized by golden jackal optimization (GJO). The experimental results show that this method reduces the MAE, RMSE, and MAPE by 14.02%, 12.9%, and 13.84%, respectively, and improves R² by 3.9% on average compared with the traditional model, which significantly improves prediction accuracy and stability. These improvements enable more accurate scheduling of wind power, lower reserve requirements, and enhanced stability and sustainability of power system operation under high renewable penetration. Full article

This study proposes an improved Fruit Fly Optimization Algorithm integrated with Simulated Annealing (SA-FOA) for hybrid flow shop scheduling problems with dual objectives of minimizing makespan and total tardiness. The algorithm adopts a three-stage integration strategy to generate high-quality initial populations, surpassing random initialization. During olfactory search, insertion-based neighborhood operations expand search scope, while visual search incorporates simulated annealing acceptance criteria to escape local optima. Validation employs three scalable instances, comparing SA-FOA against basic FOA and classical scheduling rules. Experimental results demonstrate significant superiority in Inverted Generational Distance (IGD), Non-dominant rate (NR), and Convergence Matrix (C-matrix metrics), highlighting enhanced convergence, distribution, and diversity. Notably, performance advantages amplify with problem scale growth. Full article

This paper introduces the Knowledge-Guided Genetic Algorithm (KGGA), a hybrid metaheuristic that reimagines crossover as a form of genetic engineering rather than random recombination. By embedding knowledge-guided exploitation principles directly into the crossover operator, KGGA selectively amplifies high-quality genetic material, intensifying the search around promising regions of the solution space. Experimental results on a large scale DRC-FJSSP benchmark show that KGGA outperforms state-of-the-art alternatives—including the Classic Genetic Algorithm (GA), Knowledge-Guided Fruit Fly Optimization Algorithm (KGFOA), and Hybrid Artificial Bee Colony Algorithm (HABCA)—consistently achieving superior solution quality. Full article

The weighted averaging algorithm is a widely adopted high-efficiency data fusion approach for micro-electro-mechanical system (MEMS) inertial measurement unit (IMU) array, where the configuration of weighting coefficients plays a critical role in improving measurement accuracy. In this study, an optimal weighted averaging algorithm based on the fruit fly optimization algorithm (FOA) is proposed by analyzing the data fusion mechanism of the MEMS IMU array. Firstly, a measurement model for the MEMS IMU array is constructed, and the principles of data fusion are systematically investigated. Secondly, the optimal weighting coefficients under ideal conditions are derived, and their limitations in practical applications are discussed. Building on this framework, the FOA is employed to search for optimal weights, enabling the realization of high-precision weighted averaging fusion. Simulation and experimental results demonstrate that the proposed method outperforms conventional approaches in terms of accuracy and robustness. Full article

In response to the current research status and existing problems of material distribution during major emergency events, we construct an intelligent emergency logistics route model based on cellular space AGNES clustering (AGglomerative NESting clustering) and a symmetrical fruit fly optimization algorithm. We establish the cellular algorithm based on urban road nodes and node local spaces, and construct the topology algorithm to implement the cellular space in a way that includes distribution centers and delivery points. In the cellular space, we develop an improved AGNES clustering algorithm based on the cellular space model in accordance with the neighboring relationship between distribution centers and delivery points, which quantifies the spatial clustering relationship between the distribution centers and the delivery points. Based on the clustering model, we construct an emergency logistics route model by using a symmetrical fruit fly optimization algorithm. In line with the symmetrical feature of a logistics route from one destination to another, the traveling distances within one route section are the same in both directions. Thus, we construct the logistics sub-intervals and logistics intervals by using distribution centers and delivery points, and the optimal fruit fly individuals and corresponding fitness functions are searched within the two-level intervals to obtain the emergency logistics routes with the lowest costs. Experimental results show that the proposed algorithm can output the optimal logistics routes for each logistics sub-interval and the entire logistics interval. Compared with the traditional route planning methods Dijkstra’s algorithm and the A* algorithm, it can reduce the cost of route planning and achieve optimization rates of 9.89% and 13.12%, respectively. The t-test proves that the constructed algorithm is superior to the traditional route planning algorithms in saving route costs. Full article

Water ecological security is one of the key directions of current environmental protection. With the acceleration of urbanization and industrialization, the Shanghai region of the Yangtze River Basin faces various aquatic ecological issues, such as eutrophication and declining benthic biodiversity. Dissolved oxygen (DO), as a critical indicator for measuring water self-purification capacity and ecological health status, has been widely applied in water quality monitoring and early warning systems. Therefore, accurate prediction of dissolved oxygen concentration is of significant importance for the ecological and environmental protection of river basins. This study introduces a hybrid prediction model combining Variational Mode Decomposition (VMD), Improved Fruit Fly Optimization Algorithm (IFOA), and Attention-based Gated Recurrent Unit (Attention-GRU). The model first decomposes preprocessed dissolved oxygen data through VMD to extract multiple intrinsic mode functions, reducing non-stationarity and high-frequency noise interference. It then utilizes the Improved Fruit Fly Optimization Algorithm to adaptively optimize key parameters of the Attention-GRU network, enhancing the model’s fitting capability. Experiments demonstrate that the VMD-IFOA-Attention-GRU model achieves 0.286, 0.302, and 0.915 for Mean Absolute Error (MAE), Root Mean Square Error (RMSE), and coefficient of determination (R²), respectively, significantly outperforming other comparative models. The results indicate that this method can provide a reference for intelligent water quality prediction in typical regions such as the Yangtze River Basin. Full article

In recent years, China’s vegetable market has faced frequent and drastic price fluctuations due to factors such as supply–demand relationships and climate change, which significantly affect government bodies, farmers, consumers, and other participants in the vegetable industry and supply chain. Traditional forecasting methods demonstrate evident limitations in capturing the nonlinear characteristics and complex volatility patterns of price series, underscoring the necessity of developing high-precision prediction models. This study proposes a hybrid forecasting model integrating variational mode decomposition (VMD), the Fruit Fly Optimization Algorithm (FOA), and a gated recurrent unit (GRU). The model employs VMD for multi-scale decomposition of original price series and utilizes the FOA for adaptive optimization of the GRU’s critical parameters, effectively addressing the challenges of high volatility and nonlinearity in agricultural price forecasting. Empirical analysis conducted on daily price data of six major vegetables, specifically, Chinese cabbage, cucumber, beans, tomato, chili, and radish, from 2014 to 2024 reveals that the proposed model significantly outperforms traditional methods, single deep learning models, and other hybrid models in predictive performance. Experimental results indicate substantial improvements in key metrics including the Mean Absolute Error (MAE), Root Mean Square Error (RMSE), and Coefficient of Determination (R²), with R² values consistently exceeding 99.4% and achieving over 5% enhancement compared to the baseline GRU model. This research establishes a novel methodological framework for analyzing agricultural price forecasting while providing reliable technical support for market monitoring and policy regulation. Full article

Subjective selection of simulation interface shapes may introduce errors in the strength and fatigue analysis of thermal barrier coatings (TBCs). However, the applicability of different interface shapes for the TBCs simulation has rarely been investigated. Based on the TBCs thermal fatigue experiment, a finite element model is established and combined with the Fruit Fly Optimization Algorithm (FOA), a TBCs life prediction model is established. Then, five typical interface shapes, sawtooth, sinusoidal, semicircular, elliptical, and trapezoidal, are identified based on fine-scale photographs of the real interface morphology of the TBCs. Finally, the interface shape with the highest simulation applicability is identified through interface stress state analysis and life prediction error analysis, and verified through experiment. The results show that the stress maximum location of the sawtooth and trapezoidal interface shapes is inconsistent with the experimental onset of damage in TBCs, which proves that the applicability of the two shapes in the simulation of TBCs is not high. When applying equivalent strain for life prediction, the life prediction errors for the semicircular interface shape, elliptical interface shape, and sinusoidal interface shape are 72.84%, 61.74%, and 58.72%, respectively. The lowest life prediction error is obtained by using data from the sinusoidal interface shape. Therefore, the sinusoidal interface shape is the most applicable simplified shape for TBC simulation. Applying sinusoidal interface shape for additional TBCs life prediction with only 13.52% error, which verifies the accuracy of the methodology and conclusions of this study. These conclusions can inform accurate strength and fatigue simulation analysis of TBCs. Full article

Due to the complex changes in the physical and chemical properties of rolling bearings from degradation to failure, most model-driven and data-driven methods generally suffer from insufficient accuracy and robustness in predicting the remaining useful life of rolling bearings. To address this challenge, this paper proposes a data-driven artificial neural network method, namely the CNN-LSTM bearing remaining life prediction model based on the fruit fly optimization algorithm (FOA). This method utilizes the deep feature mining capabilities of convolutional neural networks (CNN) and long short-term memory networks (LSTM) to effectively extract spatial features and temporal information sequences from the dataset. In addition, introducing FOA enables the model to dynamically adjust the hidden layers and thresholds while optimizing the optimal path, thereby finding the best solution. This article conducts ablation experiments on the model using the acceleration life dataset of IEEE PHM 2012 rolling bearings. The experimental results show that the FOA-CNN-LSTM model proposed in this paper significantly outperforms other comparative methods in RUL prediction accuracy and stability, verifying its effectiveness and innovation in dealing with complex degradation processes. This method helps to take preventive measures before faults occur, thereby reducing economic losses and having important practical significance for predicting the remaining life of rolling bearings. Full article