Search Results - (( parallel optimization method algorithm ) OR ( using set method algorithm ))
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1
Topology-aware hypergraph based approach to optimize scheduling of parallel applications onto distributed parallel architectures
Published 2020“…Any optimization algorithm is suitable for only a specific domain of optimization problems. …”
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2
Sequential and parallel multiple tabu search algorithm for multiobjective urban transit scheduling problems
Published 2018“…The major contribution of this research is to propose MTS method for solving UTSP in both sequential and parallel approach and it is shown that the algorithms are able to produce comparable results for most of the cases from literature and the computational times can be reduced significantly for more than 80% by the parallel execution. …”
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3
Probabilistic ensemble fuzzy ARTMAP optimization using hierarchical parallel genetic algorithms
Published 2015“…This was achieved by mitigating convergence in the genetic algorithms by employing a hierarchical parallel architecture. …”
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4
Improved black-winged kite algorithm and finite element analysis for robot parallel gripper design
Published 2024“…The central composite design (CCD) method was used to design the FEA experiment and establish the BKA-BPNN regression prediction model. …”
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5
Investigation on the dynamic of computation of semi autonomous evolutionary computation for syntactic optimization of a set of programming codes
Published 2007“…Genetic Algorithm as one of the Evolutionary Computation method improve the execution of parallel programming codes by optimizing the number of processors and the distribution of data. …”
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6
PID Parameters Improvement for AGC in Three Parallel-Connected Power Systems
Published 2016“…The optimal parameters of the PID scheme optimized by the proposed MS algorithm are compared with that one’s obtained by GA algorithm, and the proposed method has proven that its performance is more efficient and improved as well. …”
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7
Distributed parallel deep learning with a hybrid backpropagation-particle swarm optimization for community detection in large complex networks
Published 2022“…Next, the method is integrated with two optimization algorithms: (1) backpropagation (BP), which optimizes deep learning locally within each local chunk of the CN; (2) particle swarm optimization (PSO), which is used to improve the BP optimization involving all CN chunks. …”
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Improved hybrid teaching learning based optimization-jaya and support vector machine for intrusion detection systems
Published 2022“…Most of the currently existing intrusion detection systems (IDS) use machine learning algorithms to detect network intrusion. …”
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9
Model of Improved a Kernel Fast Learning Network Based on Intrusion Detection System
Published 2019“…In this paper, the particle swan optimization algorithm (PSO) was used to obtain an optimal set of initial parameters for Reduce Kernel FLN (RK-FLN), thus, creating an optimal RKFLN classifier named PSO-RKELM. …”
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10
Restoration planning strategy of transmission system based on optimal energizing time of sectionalizing islands / Dian Najihah Abu Talib
Published 2019“…Modification and simplification using parallel processing are done for both methods to grossly search the optimal solution and to minimize the program running time, respectively. …”
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11
Dynamic partitioning and data allocation method on heterogeneous architecture / Muhammad Helmi Rosli
Published 2015“…Each experiment highlight the advantages and disadvantages of the experimental architecture.The disadvantages from each experiment prompts the design of dynamic parallel partitioning and allocating framework. The case study use for this experiment is Sobel edge detection algorithm. …”
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12
A parallel ensemble learning model for fault detection and diagnosis of industrial machinery
Published 2023“…Deep learning has recently emerged as effective methods for machine FDD applications. However, the gradient descent optimization method that is commonly used in deep learning suffers from several limitations, such as high computational cost and local sub-optimal solutions. …”
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13
Web based personalized university timetable for UiTM students using genetic algorithm / Mohd Radhi Fauzan Jamli and Ahmad Firdaus Ahmad Fadzil
Published 2024“…Future work entails enhancing system robustness through comprehensive contingency planning, real-time data integration, and algorithmic optimization, with a focus on refining the genetic algorithm and exploring parallel processing techniques to further enhance efficiency and scalability.…”
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14
Speed Efficient Hardware Implementation Of Advanced Encryption Standard (Aes)
Published 2017“…The methods of optimization include on the fly computations and looping architecture, where all these of methods reduce the speed. …”
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15
Scheduling dynamic cellular manufacturing systems in the presence of cost uncertainty using heuristic method
Published 2016“…Then, design of experiments is used to examine the sensitivity of the parameters of each solving algorithm using Taguchi method. …”
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16
Development of a multi-objective optimization model for transport and environment in a closed-loop automotive supply chain
Published 2014“…After that a Meta-heuristic method is used for finding the global optimum solution. …”
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17
An Improved Fuzzy Parallel Distributed –Like Controller for Multi-Input Multi-Output Twin Rotor System
Published 2009“…Twin Rotor Multi Input Multi Output (MIMO) System (TRMS) is a laboratory set-up design for which it has been used for control experiments, control theories developments, and applications of the autonomous helicopter. …”
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18
A basic study on hybrid systems for small race car to improve dynamic performance using lap time simulation
“…Therefore, the dynamic performance of the hybrid system was investigated by calculating the lap times on a given circuit using a quasi-steady-state method with low computational load and high prediction accuracy. …”
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19
Voting algorithms for large scale fault-tolerant systems
Published 2011“…To solve this problem and gain benefits of this algorithm, we employed parallel algorithm technique and by using optimal number of processors, we could propose optimal algorithms known as Parallel Average Voting and Parallel Weighted Average Voting which both have optimal time complexity and less calculation cost. …”
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