Search Results - (( property optimization method algorithm ) OR ( parallel distribution modified algorithm ))
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Parallel algorithms for numerical simulations of EHD ion-drag micropump on distributed parallel computing systems
Published 2014“…To implement the parallel algorithms a distributed parallel computing laboratory using easily available low cost computers is setup. …”
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A modified artificial neural network (ANN) algorithm to control shunt active power filter (SAPF) for current harmonics reduction
Published 2013“…The novelty control design is an artificial neural network (ANN) adopting a modified mathematical algorithm (a modified delta rule weight-updating W-H) and a suitable alpha value (learning rate value) which determines the filters optimal operation. …”
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Parallel Diagonally Implicit Runge-Kutta Methods For Solving Ordinary Differential Equations
Published 2009“…All algorithms are written in C language and the parallel code is implemented on Sun Fire V1280 distributed memory system. …”
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A modified conjugate gradient coefficient with inexact line search for unconstrained optimization
Published 2016“…Conjugate gradient (CG) method is a line search algorithm mostly known for its wide application in solving unconstrained optimization problems. …”
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The convergence properties of a new hybrid conjugate gradient parameter for unconstrained optimization models
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A Descent Three-Term Conjugate Gradient Direction For Problems Of Unconstrained Optimization With Application
Published 2026journal::journal article -
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Performance analysis of a modified conjugate gradient algorithm for optimization models
Published 2021“…The Conjugate gradient (CG) algorithms is very important and widely used in solving optimization models. …”
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Managing Heterogeneous Database Replication Using Persistence Layer Synchronous Replication (PLSR)
Published 2013“…It achieves faster time execution and cost minimization than that other replication processes. This algorithm also introduces a multi thread based persistence layer, which supports early binding and parallel connection to the servers. …”
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Improved stochastic gradient descent algorithm with mean-gradient adaptive stepsize for solving large-scale optimization problems
Published 2023“…SGD uses random or batch data sets to compute gradient in solving optimization problems. It is an iterative algorithm with descent properties that reduces computational cost by using derivatives of random data points. …”
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New Quasi-Newton Equation And Method Via Higher Order Tensor Models
Published 2010“…The global and local convergence properties of the new method on uniformly convex problems are also analyzed. …”
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Automatic generic process migration system in linux
Published 2012“…A migration algorithm is designed which attempts to exploit the unique features of the basic migration algorithms to form a generic algorithm. …”
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Towards optimal search: a modified secant method for efficient search in a big database
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Application of sampling-based motion planning algorithms in autonomous vehicle navigation
Published 2016“…In this chapter, a novel sampling-based navigation architecture is introduced, which employs the optimal properties of RRT* planner and the low running time property of low-dispersion sampling-based algorithms. …”
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15
Hybrid DFP-CG method for solving unconstrained optimization problems
Published 2017“…The conjugate gradient (CG) method and quasi-Newton method are both well known method for solving unconstrained optimization method. …”
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A 'snowflake' geometrical representation for optimised degree six 3-modified chordal ring networks
Published 2016“…The performance parameters and properties of chordal rings have been researched extensively as models for parallel and distributed interconnection topology models since their founding in 1981. …”
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Hybrid Artificial Bees Colony algorithms for optimizing carbon nanotubes characteristics
Published 2018“…Optimization is a crucial process to select the best parameters in single and multi-objective problems for manufacturing process.However,it is difficult to find an optimization algorithm that obtain the global optimum for every optimization problem.Artificial Bees Colony (ABC) is a well-known swarm intelligence algorithm in solving optimization problems.It has noticeably shown better performance compared to the state-of-art algorithms.This study proposes a novel hybrid ABC algorithm with β-Hill Climbing (βHC) technique (ABC-βHC) in order to enhance the exploitation and exploration process of the ABC in optimizing carbon nanotubes (CNTs) characteristics.CNTs are widely used in electronic and mechanical products due to its fascinating material with extraordinary mechanical,thermal,physical and electrical properties. …”
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20
Gooseneck barnacle optimization algorithm: A novel nature inspired optimization theory and application
Published 2024“…In contrast to the previously published Barnacle Mating Optimizer (BMO) algorithm, GBO more accurately captures the unique static and dynamic mating behaviours specific to gooseneck barnacles. …”
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