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501
Integrated decision support system for optimizing time and cost trade offs in linear repetitive construction projects
Published 2025-06-01“…Unlike previous studies that focus solely on single-algorithm applications, this research evaluates two metaheuristic optimization strategies—Genetic Algorithm (GA) and Particle Swarm Optimization (PSO)—within a consistent problem setting. …”
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502
Adaptive Multi-Objective Firefly Optimization for Energy-Efficient and QoS-Aware Scheduling in Distributed Green Data Centers
Published 2025-06-01“…AFBO is evaluated using real-world trace-driven simulations and compared against benchmark multi-objective algorithms, including multi-objective particle swarm optimization (MOPSO), simulated annealing-based bi-objective differential evolution (SBDE), and the baseline Multi-Objective Firefly Algorithm (MOFA). …”
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503
Exploration and Exploitation Mechanism in Pairwise Test Case Generation: A Systematic Literature Review
Published 2025-01-01“…Covering research from 2014 to 2024, the review evaluates hybrid and metaheuristic strategies, including Pairwise Migrating Birds Optimization-Based Strategies (PMBOS), Pairwise Gravitational Search Algorithm Strategy (PGSAS), Pairwise hybrid Artificial Bee Colony (PhABC), Genetic and Particle Swarm Optimization (GAPSO) algorithm, Hybrid Optimization Algorithm (HOA), and Parameter Free Choice Function based Hyper-Heuristic (PCFHH), among others. …”
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504
A novel global MPPT method based on sooty tern optimization for photovoltaic systems under complex partial shading
Published 2025-07-01“…A comprehensive simulation framework was implemented in MATLAB/Simulink, employing a 3 × 3 PV array (3 kW capacity) and a boost converter to test the proposed method across four shading scenarios, including highly irregular and dynamic patterns. Performance evaluation against benchmark algorithms—Perturb & Observe (P&O), Particle Swarm Optimization (PSO), and Grey Wolf Optimization (GWO)—revealed that STOA consistently outperformed its counterparts. …”
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505
A Novel Deep Learner for Human Behavior Prediction Over Public Video Surveillance
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506
Response mitigations of adjacent structure with MPTMD under real and stochastic excitations
Published 2025-05-01“…The performance of the MPTMD system is optimized using the Particle Swarm Optimization (PSO) algorithm. The optimization process calculates the optimal placement and parameters of the MPTMD system under two objective functions: the first function aims to minimize the Park-Ang damage index of all structures, while the second focuses on reducing the maximum impact force between the adjacent structures. …”
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507
Transactive energy management for efficient scheduling and storage utilization in a grid-connected renewable energy-based microgrid
Published 2025-03-01“…Comparative analysis reveals that SMA consistently outperforms conventional methods such as Particle Swarm Optimization (PSO) and Genetic Algorithm (GA) in terms of convergence speed and computational efficiency, making it particularly suitable for real-time energy management. …”
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508
Development of A Novel Discharge Routing Method Based On the Large Discharge Dataset, Muskingum Model, Optimization Methods, and Multi-Criteria Decision Making
Published 2024-10-01“…Different MOAs, including a Genetic Algorithm (GA), Particle Swarm Optimization (PSO), Firefly Algorithm (FFA), Cuckoo Search (CS), Bat Algorithm (BA), Shark Smell Optimization (SSO), Whale Optimization Algorithm (WOA), Harris Hawk''s Optimization (HHO), and hybrid of WOA and CS (WOA_CS), were developed for Muskingum calibration. …”
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509
Enhancing power system stability by coordinating a wind turbine voltage regulator and lead-lag power system stabilizer using GOOSE optimization
Published 2025-04-01“…The GOA performance compared with the Osprey Optimization Algorithm (OOA) and Particle Swarm Optimizer (PSO). …”
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510
Multi-Criteria Optimization of a Standalone Photovoltaic System in Cyprus (Techno-Economic Analysis)
Published 2025-06-01“…This study proposes a two-stage optimization approach for standalone photovoltaic systems, employing three distinct optimization algorithms—NSGA-II, DEMO, and Particle Swarm Optimization—to minimize both the Loss of Load Probability (LLP) and the life cycle cost (LCC). …”
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511
Machine Learning and Metaheuristics Approach for Individual Credit Risk Assessment: A Systematic Literature Review
Published 2025-05-01“…It categorizes the use of machine learning algorithms, feature selection methods, and metaheuristic optimization techniques, including genetic algorithms, particle swarm optimization, and biogeography-based optimization. …”
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512
An automatic classification of breast cancer using fuzzy scoring based ResNet CNN model
Published 2025-07-01“…The extracted curvelet coefficients and the turn-time difficulty are too excessive to be categorized. Utilizing swarm intelligence, the Adaptive Grey Wolf Optimization Algorithm (AGWOA) was presented to reduce the time difficulty and choose the key characteristics. …”
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513
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514
A survey on autonomous navigation for mobile robots: From traditional techniques to deep learning and large language models
Published 2025-08-01“…The review extends to modern metaheuristic algorithms, including genetic algorithms (GA), particle swarm optimization (PSO), and ant colony optimization (ACO). …”
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515
Chiller power consumption forecasting for commercial building based on hybrid convolution neural networks-long short-term memory model with barnacles mating optimizer
Published 2025-07-01“…The study compares the proposed CNN-LSTM-BMO against other metaheuristic optimization algorithms, including Genetic Algorithm (GA), Particle Swarm Optimization (PSO), Ant Colony Optimization (ACO), and Differential Evolution (DE). …”
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516
A novel hybrid methodology for wind speed and solar irradiance forecasting based on improved whale optimized regularized extreme learning machine
Published 2024-12-01“…Further, permutation entropy is employed to extract the complexity of IMFs for filtering and reconstruction of decomposed components to alleviate the difficulty of direct modeling. Then, a unique swarm intelligence technique, the non-linear dimension learning Hunting Whale Optimization Algorithm (NDLHWOA), is devised to optimize regularized extreme learning machine model parameters to capture the implicit information of each reconstructed sub-series. …”
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517
Risk Prediction and Response Strategies in Corporate Financial Management Based on Optimized BP Neural Network
Published 2021-01-01“…To improve the accuracy of financial risk prediction, principal component analysis and particle swarm algorithm are applied to optimize the BP neural network model, the input data of the prediction model is improved, and the optimal initial weights and thresholds are given to the BP neural network by using particle swarm algorithm search, whereby the financial risk prediction model of particle swarm optimization BP neural network is constructed. …”
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518
Optimal Assembly Position and Multi-objective Trajectory Optimization for Dual Robotic Arms Collaboration
Published 2024-01-01“…Firstly, the motion constraint relationship in the coordinated assembly process of double robotic arms is analyzed, the operability degree is used as the flexibility evaluation index, the operability evaluation method of dual robotic arms collaborative assembly system is proposed, the optimization objective function is constructed, and the particle swarm algorithm is used to find the assembly position with the optimal global operability degree of the system in the collaborative space of double robotic arms. …”
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519
Green Energy Strategic Management for Service of Quality Composition in the Internet of Things Environment
Published 2020-01-01“…Considering that the service composition in the Internet of things environment is NP hard, moth algorithm (MFO) is successfully applied to the QoS evaluation model and energy evaluation model. …”
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520
Classification Prediction of Rockburst in Railway Tunnel Based on Hybrid PSO-BP Neural Network
Published 2022-01-01“…In order to accurately predict the rockburst intensity level of the railway tunnel, the rock stress coefficient σθ/σc, rock brittleness coefficient σc/σt, and elastic energy index Wet are used as evaluation indexes of rockburst intensity, and a BP neural network rockburst prediction model based on hybrid particle swarm optimization algorithm is proposed. …”
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