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iterative optimization » objective optimization (Expand Search), migration optimization (Expand Search), storage optimization (Expand Search)
1 based » 41 based (Expand Search), _ based (Expand Search), 3 based (Expand Search)
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An enhanced binary Rat Swarm Optimizer based on local-best concepts of PSO and collaborative crossover operators for feature selection
Published 2022“…In this paper, a recent swarm intelligence metaheuristic method called RSO which is inspired by the social and hunting behavior of a group of rats is enhanced and explored for FS problems. The binary enhanced RSO is built based on three successive modifications: i) an S-shape transfer function is used to develop binary RSO algorithms; ii) the local search paradigm of particle swarm optimization is used with the iterative loop of RSO to boost its local exploitation; iii) three crossover mechanisms are used and controlled by a switch probability to improve the diversity. …”
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On Higher-Order Iterative Learning Control Algorithm in Presence of Measurement Noise
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Multiclass feature selection with metaheuristic optimization algorithms: a review
Published 2022“…Metaheuristic algorithms have also been presented in four primary behavior-based categories, i.e., evolutionary-based, swarm-intelligence-based, physics-based, and human-based, even though some literature works presented more categorization. …”
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A new genetic algorithm approach for unit commitment
Published 1997Get full text
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Optimal supercharge scheduling of electric vehicles
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Eye-Clustering: An Enhanced Centroids Prediction for K-means Algorithm
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A new minimum curvator multi-step method for unconstrained optimization
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Multidimensional Gains for Stochastic Approximation
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A hybrid model to predict the pressure gradient for the liquid-liquid flow in both horizontal and inclined pipes for unknown flow patterns
Published 2023“…The important feature subset is identified using the modified Binary Grey Wolf Optimization Particle Swarm Optimization (BGWOPSO) algorithm. …”
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A method for data path synthesis using neural networks
Published 2017“…The proposed algorithm has a running time complexity of O(1) for a neural network with n vertices and c cliques. …”
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Scatter search for homology modeling
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Shuffled Linear Regression with Erroneous Observations
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