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A Comparative Study on DG Placement Using Marine Predator and Osprey Algorithms to Enhance Loss Reduction Index in the Distribution System
Published 2025-06-01“…The Marine Predator Algorithm (MPA) and Osprey Optimization Algorithm (OOA) are nature-inspired metaheuristic techniques used for optimizing the location and sizing of distributed generation (DG) in power distribution systems. …”
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The berth allocation problem in bulk terminals under uncertainty
Published 2025-06-01“…To solve the model, we use an exact decomposition algorithm (DA) in which the probability distribution information is iteratively included in the master problem through optimal dual cuts. …”
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2565
An improved deep learning approach for automated detection of multiclass eye diseases
Published 2025-09-01“…The overall average accuracy of the model reached 0.9621. Conclusion: The integration of biorthogonal wavelet transforms into our CNN model has proven effective, surpassing the performance of several similar algorithms reported in the literature. …”
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A Comparison of Inversion Methods for Surrogate‐Based Groundwater Contamination Source Identification With Varying Degrees of Model Complexity
Published 2024-04-01“…To evaluate the applicability of these methods, we chose one representative inversion algorithm from each category, namely the Improved Butterfly Optimization Algorithm (IBOA) for simulation optimization, the Ensemble Smoother with Multiple Data Assimilation (ES‐MDA) for data assimilation, and the DiffeRential Evolution Adaptive Metropolis with a Snooker Update and Sampling from a Past Archive (DREAM(ZS)) for Bayesian inference. …”
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2568
IoT-based prediction model for aquaponic fish pond water quality using multiscale feature fusion with convolutional autoencoder and GRU networks
Published 2025-01-01“…Thus, the water quality predicted data is obtained. The proposed model integrates GRU networks with a convolutional autoencoder to improve water quality prediction by capturing trends and managing temporal dependencies. …”
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2569
An optimized spatial target trajectory prediction model for multi-sensor data fusion in air traffic management
Published 2025-03-01“…This paper proposes an innovative network model based on the improved snow ablation optimizer algorithm. …”
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2570
Optimized multi-unit coordinated scheduling based on improved IGDT: Low-carbon scheduling research for the electric-heat-oxygen integrated energy system
Published 2025-06-01“…This model combines the entropy weight method (EWM) and non-dominated sorting genetic algorithm II (NSGA-II), improving the objectivity and rationality of uncertainty weight settings in risk-averse strategy (RAS) and risk-seeking strategy (RSS). …”
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Inversion of Water Quality Parameters from UAV Hyperspectral Data Based on Intelligent Algorithm Optimized Backpropagation Neural Networks of a Small Rural River
Published 2025-01-01“…Second, a method combining the Pearson’s correlation coefficient and the variance inflation factor (PCC–VIF) was utilized to decrease the dimensionality of features and improve the quality of the input data. Again, based on the screened features, a back-propagation neural network (BPNN) model optimized using a mixture of the genetic algorithm (GA) and the particle swarm optimization (PSO) algorithm was established as a means of estimating water quality parameter concentrations. …”
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Research on multi-objective energy optimization design for multi-story residential buildings in Suzhou region based on artificial neural networks
Published 2025-09-01“…To address the issues of high energy consumption, low thermal comfort, and excessive greenhouse gas emissions in residential buildings, this study optimizes multi-story residential buildings in the Suzhou region using a multi-objective Non-dominated Sorting Genetic Algorithm III (NSGA-III) coupled with an Artificial Neural Network (ANN). …”
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Optimal Allocation Strategy for Power Quality Control Devices Based on Harmonic and Three-Phase Unbalance Comprehensive Evaluation Indices for Distribution Network
Published 2020-11-01“…Secondly, taking the global configuration effects, the total number and capacity of control devices as the optimization goals, and regarding the harmonic distortions and unbalance degrees of the nodes satisfying the standard as the constraint condition, the optimal configuration node and capacity of each device is determined by multi-objective particle swarm algorithm. …”
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Multi-Robot exploration for coal mine rescue based on the extension of undirected graph
Published 2025-05-01“…Finally, three different models of robots are used for real experiments and compared with commonly used multi-robot autonomous exploration algorithms based on boundary point methods. …”
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The Robust Steiner Team Orienteering Problem with Decreasing Priorities under budgeted uncertainty
Published 2025-12-01“…The cut-and-branch algorithm based on the stronger model also stands out, solving 20 more instances than the other. …”
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2577
Application of genetic algorithm for the set-covering problem solution
Published 2016-09-01Get full text
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Opposition-based learning techniques in metaheuristics: classification, comparison, and convergence analysis
Published 2025-07-01“…In recent years, opposition-based learning (OBL) has emerged as a powerful enhancement strategy in metaheuristic algorithms (MAs), gaining significant attention for its potential to accelerate convergence and improve solution quality. …”
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Minimizing Delay at Closely Spaced Signalized Intersections Through Green Time Ratio Optimization: A Hybrid Approach With K-Means Clustering and Genetic Algorithms
Published 2025-01-01“…Closely spaced intersections play a key role in traffic flow management. This study aims to model different traffic related parameters to minimize the delay of a closely spaced intersection by optimizing the green time ratio with the help of the genetic algorithm. …”
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Optimization of emergency material distribution routes in flood disaster with truck‐speedboat‐drone coordination
Published 2025-03-01“…To solve this optimization problem, we introduce an improved adaptive large neighborhood search (IALNS) algorithm, which builds on the traditional ALNS framework through refined tuning of deletion and insertion operators. …”
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