Search Results - parallel numerical optimization algorithm
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A Parallel new high order iterative algorithm on shared memory multiprocessors parallel computer
Published 2004“…In this paper, the parallel implementation of the algorithm with optimal strategy on shared memory multiprocessors (SMP) was presented and discussed. …”
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Workflow optimization in distributed computing environment for stream-based data processing model / Saima Gulzar Ahmad
Published 2017“…Similarly, when data parallelism is introduced in the algorithm the performance of the algorithm improved further by 12% in latency and 17% in throughput when compared to PDWA algorithm. …”
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Review of the grey wolf optimization algorithm: variants and applications
Published 2023“…One of the most widely referenced Swarm Intelligence (SI) algorithms is the Grey Wolf Optimizer (GWO), which is based on the pack hunting and natural leadership organization of grey wolves. …”
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Computational simulation for analysis and synthesis of impact resilient structure
Published 2013“…The second objective is to utilize the computational algorithm for direct numerical simulation, and as a parallel scheme, commercial off-the shelf numerical code is utilized for parametric study, optimization and synthesis. …”
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Review of the grey wolf optimization algorithm: variants and applications
Published 2024“…One of the most widely referenced Swarm Intelligence (SI) algorithms is the Grey Wolf Optimizer (GWO), which is based on the pack hunting and natural leadership organization of grey wolves. …”
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Optimizing OLAP heterogeneous computing based on Rabin-Karp algorithm
Published 2013“…Yet, such a method will always need increasing processing resources. Numerous enhancements have been suggested in order to improve OLAP performance, part of them has gone to the processing capabilities whereby parallel processing has occupied a sizeable space. …”
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Proceeding Paper -
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Tool path generation of contour parallel based on ant colony optimisation
Published 2016“…This paper presents the minimisation of machining time of computer numerical control (CNC) by eliminating the uncut region of sharp corner based on contour parallel milling method. …”
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Computational simulation for static and dynamic load of rectangular plate in elastic region for analysis of impact resilient structure.
Published 2012“…The second objective is to utilize the computational algorithm for direct numerical simulation, and as a parallel scheme, commercial off-the shelf numerical code is utilized for parametric study, optimization and synthesis. …”
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Conference or Workshop Item -
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Parallelizing web scraping to improve performance and scalability
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Final Year Project / Dissertation / Thesis -
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Efficient Numerical Modelling of Extreme Wave
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Efficient Numerical Modelling of Extreme Waves
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Final Year Project -
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Load-Balancing Models for Scheduling Divisible Load on Large Scale Data Grids
Published 2009“…In the second stage, an Iterative DLT (IDLT) model is proposed. Recursive numerical equations are derived to find the optimal workload assigned to the grid node. …”
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Numerical methods for nonlinear optimal control problems using haar wavelet operational matrices / Waleeda Swaidan Ali
Published 2015“…To establish a direct method, an efficient new algorithm is proposed to solve nonlinear optimal control problems with a finite time horizon under inequality constraints. …”
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Critical insight for MAPReduce optimization in Hadoop
Published 2014“…The predominant philosophy behind Hadoop optimization is the optimization of MapReduce, which is a dominant programming platform effective in bringing a=bout many functional enhancements as per scheduling algorithms developed and implemented. …”
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Hybrid ANN and Artificial Cooperative Search Algorithm to Forecast Short-Term Electricity Price in De-Regulated Electricity Market
Published 2019“…Therefore, this research proposes a hybrid method for electricity price forecasting via artificial neural network (ANN) and artificial cooperative search algorithm (ACS). In parallel, a feature selection technique based on the combination of mutual information (MI) and neural network (NN) is developed in this study to select the input variables subsets, which have substantial impact on forecasting of electricity price. …”
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