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581
A Hybrid Algorithm with a Data Augmentation Method to Enhance the Performance of the Zero-Inflated Bernoulli Model
Published 2025-05-01“…This zero-inflated structure significantly contributes to data imbalance. To improve the ZIBer model’s ability to accurately identify minority classes, we explore the use of momentum and Nesterov’s gradient descent methods, particle swarm optimization, and a novel hybrid algorithm combining particle swarm optimization with Nesterov’s accelerated gradient techniques. …”
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582
OPTIMIZING PROCESSOR WORKLOADS AND SYSTEM EFFICIENCY THROUGH GAME-THEORETIC MODELS IN DISTRIBUTED SYSTEMS
Published 2024-09-01“…Key results from this study highlight that while Nash Equilibrium fosters stability within the system, the adoption of optimal cooperative strategies significantly improves operational efficiency and minimizes transaction costs. …”
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583
Adaptive predator prey algorithm for many objective optimization
Published 2025-04-01“…This paper presents the Many-Objective Marine Predator Algorithm (MaOMPA), an adaptation of the Marine Predators Algorithm (MPA) specifically enhanced for many-objective optimization tasks. …”
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584
Heuristic Global Optimization for Thermal Model Reduction and Correlation in Aerospace Applications
Published 2025-06-01“…This research employs a series of numerical simulations using methods such as Genetic Algorithms, Cultural Algorithms, and Artificial Immune Systems, with an emphasis on parameter tuning to optimize the reduced thermal model correlation. …”
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585
USING REINFORCEMENT LEARNING ALGORITHMS FOR UAV FLIGHT OPTIMIZATION
Published 2024-12-01“…The study of the results of the functionality of the proposed algorithm was carried out in the environment of three-dimensional modeling. …”
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586
Prediction of Lithium-Ion Battery State of Health Using a Deep Hybrid Kernel Extreme Learning Machine Optimized by the Improved Black-Winged Kite Algorithm
Published 2024-11-01“…Addressing the non-linear and non-stationary characteristics of battery capacity sequences, a novel method for predicting lithium battery SOH is proposed using a deep hybrid kernel extreme learning machine (DHKELM) optimized by the improved black-winged kite algorithm (IBKA). …”
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587
Research on Vehicle Route Optimization for Half-Open Multi-Energy Urban Distribution Considering Order Priority
Published 2025-01-01“…Aiming at the current problems of increasingly serious tailpipe pollution of urban distribution vehicles and irrational distribution route planning, we construct a fuel-electric hybrid multi-trip multi-center half-open joint distribution vehicle routing optimization model (F-EHOMTMDVRPOPTW) considering order priority and fuzzy time window, and introduce Tent chaotic mapping combined with an improved discrete sparrow search algorithm (DSSA) with stochastic key encoding strategy for solving. …”
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588
Two-Layer Optimal Scheduling and Economic Analysis of Composite Energy Storage with Thermal Power Deep Regulation Considering Uncertainty of Source and Load
Published 2024-09-01“…The upper layer takes pumped storage as the optimization goal to improve net load fluctuation and the optimal peak load benefit; the lower layer takes the system’s total peak load cost as the optimization goal and obtains a day-before scheduling plan for the energy storage system, using an improved gray wolf algorithm to process it. …”
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589
Optimization model and heuristic solution method for multi-channel cooperative sensing in cognitive radio networks
Published 2011-11-01“…An optimization model under the scenario where multi-channels are cooperatively sensed and used by multi-secondary users (SU) was proposed.The model aims to maximize the system throughput and optimizes the parameters including the sensing time and the weight coefficient of the sampling result of each SU for each channel,meanwhile the false access probability for each channel must not violate the given constraints.To solve this non-linear optimization model,a sequential parameters optimization method(SPO)was proposed.The method begins with deriving the lower bound of the objective function of the optimization model.Then it maximizes this lower bound by optimizing the weight coefficients through solving a series of sub-optimal problems using Lagrange method,and finally finding an optimized sensing time parameter by the golden search algorithm.Extensive experiments by simulations demonstrate the effectiveness of the proposed method and the advantage of the proposed model on improving the system throughput.…”
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590
Landslide Displacement Prediction Model Based on Optimal Decomposition and Deep Attention Mechanism
Published 2025-01-01“…To address this, this study proposes an advanced forecasting framework integrating the Chebyshev Levy Flight-Sparrow Search Algorithm (CLF-SSA) with Variational Mode Decomposition (VMD) to enhance decomposition accuracy and optimize parameter selection. …”
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591
An Optimal Longitudinal Control Strategy of Platoons Using Improved Particle Swarm Optimization
Published 2020-01-01“…An improved particle swarm optimization algorithm was used to optimize the weighting coefficients for the controller state and control variables. …”
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592
A new type of sustainable operation method for urban rail transit: Joint optimization of train route planning and timetabling
Published 2025-12-01“…A linearization method for the model is proposed. To solve large-scale problems, an improved adaptive large neighborhood search algorithm (ALNS) is designed accordingly. …”
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593
Aging Prediction of IGBT Based on Improved Support Vector Regression
Published 2022-07-01“…In order to accurately predict the aging state of insulated gate bipolar transistor (IGBT), a novel IGBT aging prediction method is proposed based on improved whale optimization algorithm (IWOA) and optimized support vector regression (SVR). …”
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594
IPO: An Improved Parrot Optimizer for Global Optimization and Multilayer Perceptron Classification Problems
Published 2025-06-01“…The Parrot Optimizer (PO) is a new optimization algorithm based on the behaviors of trained Pyrrhura Molinae parrots. …”
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595
Based on the improved SCGM(1,1)c and WIV rainfall landslide susceptible area prediction model
Published 2024-12-01“…On the basis of the single factor system cloud grey model (SCGM (1,1)c), an improved SCGM (1,1)c model is proposed based on Markov prediction theory and CS algorithm optimization to predict rainfall. …”
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596
Intelligent guarantee power supply decision method based on reinforcement learning algorithm
Published 2025-06-01“…Therefore, a decision model for guaranteeing power supply is constructed based on an improved proximal policy optimization algorithm, to study the intelligent guarantee power supply decision method. …”
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597
Network heterogeneous information integrated management system based on improved RNN multi-source fusion algorithm
Published 2023-12-01“…The article adopted the wild horse optimizer (WHO) algorithm to improve the recurrent neural network (RNN) and designed a multi-source heterogeneous data fusion model. …”
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598
Optimization Strategy of Multi-group Inspection Path of Distribution Equipment with Equipment Information Included
Published 2022-07-01“…A distribution equipment information model is established, and a multi group distribution equipment routing optimization model is proposed to minimize the total routing cost, which is solved by genetic algorithm. …”
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599
The Utilization of a Naïve Bayes Model for Predicting the Energy Consumption of Buildings
Published 2023-12-01“…It introduces a fusion of the African Vultures Optimization Algorithm (AVOA) and the Sand Cat Swarm Optimization (SCSO) with the Naïve Bayes (NB) model, aiming to elevate heating load prediction accuracy and streamline HVAC system optimization. …”
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600
An Inverse Modeling Multi-Objective Optimization Technique Based on Incremental Learning and Fuzzy Clustering
Published 2025-01-01“…This paper aims to develop an inverse modeling MOEA based on decomposition that employs an incremental learning-based support vector regression (SVR) model, as an alternative to the Gaussian process model, in order to improve the quality of obtained solutions and speed up convergence of the algorithm. …”
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