Search Results - global distribution ((function algorithm) OR (((encryption algorithm) OR (selection algorithm))))
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1
Network reconfiguration and control for loss reduction using genetic algorithm
Published 2010“…Tests have shown that the differences are demonstrated only in execution time to arrive the best fitness function of the Genetic Algorithm. Whereas the results obtained from both selection methods are similar to each other. …”
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2
Chaos search in fourire amplitude sensitivity test
Published 2012“…Work in Artificial Intelligence (AI) often involves search algorithms. In many complicated problems, however, local search algorithms may fail to converge into global optimization and global search procedures are needed. …”
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3
Chaos Search in Fourier Amplitude Sensitivity Test
Published 2012“…Work in Artificial Intelligence (AI) often involves search algorithms. In many complicated problems, however, local search algorithms may fail to converge into global optimization and global search procedures are needed. …”
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4
Efficient genetic partitioning-around-medoid algorithm for clustering
Published 2019“…In general, genetic based clustering algorithms showed the ability to reach near global optimal solution. …”
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5
Nomadic people optimizer (NPO) for large-scale optimization problems
Published 2019“…A Wilcoxon Signed-Rank Test was performed to measure the pair-wise statistical performances of the algorithms and from the results, NPO recorded a better statistical performance compared to the other benchmarking algorithms. …”
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6
Development of real-time monitoring BLE-LoRa positioning system based on RSSI for non-line-of-sight condition
Published 2023“…Finally, the algorithm is embedded into Raspberry Pi to view the location via desktop.…”
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7
A Harris hawks optimization based single- and multi objective optimal power flow considering environmental emission
Published 2020“…The system was used to demonstrate the effectiveness of selective objectives. The obtained results are compared with the other Artificial Intelligence (AI) techniques such as the Whale Optimization Algorithm (WOA), the Salp Swarm Algorithm (SSA), Moth Flame (MF) and Glow Warm Optimization (GWO). …”
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8
Static and self-scalable filter range selection algorithms for peer-to-peer networks
Published 2011“…Two multiple selection algorithm, which are known as “static filter range selection algorithm” and “self-scalable selection algorithm” are proposed. …”
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9
Garra Rufa‐inspired optimization technique
Published 2020“…Also, a multiobjective real issue in power system is tested using the proposed methods where the objectives were cumulative voltage deviation and power losses of three weight sets during the selection allocation of distribution generators. The results show that the proposed method provides good data and greater convergence to the optimal point compared with the classical methods for most of the functions tested.…”
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10
An empirical study of density and distribution functions for ant swarm optimized rough reducts
Published 2011“…The density and distribution functions are two common types of Gaussian distribution representation. …”
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Book Chapter -
11
Sizing and Placement of Battery-Sourced Solar Photovoltaic (B-SSPV) Plants in Distribution Networks
Published 2021“…To deal the stochastic behavior of solar irradiance, 15 years of weather data is modeled by using beta probability density function (Beta-PDF). The proposed algorithm is applied on IEEE 33 bus and IEEE 69 bus test distribution networks and optimum results are acquired for different time varying voltage dependent load models. …”
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12
Statistical fixed range multiple selection algorithm for peer-to-peer system
Published 2010“…In this research, a new multiple selection algorithm, which is known as "statistical fixed range multiple selection algorithm" is proposed. …”
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13
Static range multiple selection algorithm for peer-to-peer system
Published 2011“…In this research, a new multiple selection algorithm, which is known as "static range statistical multiple selection algorithm" is proposed. …”
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14
A new HMCR parameter of harmony search for better exploration
Published 2016“…This research focuses on the HMCR parameter adjustment strategy using step function with combined Gaussian distribution function to enhance the global optimality of HS. …”
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15
A new HMCR parameter of harmony search for better exploration
Published 2015“…This research focuses on the HMCR parameter adjustment strategy using step function with combined Gaussian distribution function to enhance the global optimality of HS. …”
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16
A firefly algorithm based hybrid method for structural topology optimization
Published 2020“…The lower and upper limit of design variables (0 and 1) were used to find initial material distribution to initialize the firefly algorithm based section of the hybrid algorithm. …”
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17
Meta-heuristic approaches for reservoir optimisation operation and investigation of climate change impact at Klang gate dam
Published 2023“…Consequently, seeking managing of reservoir optimisation operations had always been at the forefront and to improve managing, algorithms have had been presented over the past few decades, beginning with conventional algorithms, followed by heuristic algorithms, and finally, the meta-heuristic algorithms (MHAs). …”
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18
Optimal location and size estimation of distributed generators by employing grouping particle swarm optimization and grouping genetic algorithm
Published 2017“…Therefore, there are various algorithms that could be applied in order to integrate distribution generation units into the distribution network. …”
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19
Optimal multiple distributed generation output through rank evolutionary particle swarm optimization
Published 2015“…Moreover, the local best (P-best) and global best (G(best)) values are obtained in simplify manner in the REPSO algorithm. …”
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20
Enhanced Multi-Objective Grey Wolf Optimizer with Lévy Flight and Mutation Operators for Feature Selection
Published 2023“…The Lévy flight, a type of random walk with jump size determined by the Lévy distribution, enhances the global search capability of MOGWO, with the objective of maximizing classification accuracy while minimizing the number of selected features. …”
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