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141
Transformations for Variants of the Travelling Salesman Problem and Applications
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doctoralThesis -
142
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Complexities of special matrix multiplication problems
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144
Parallel implementation of clique partitioning using artificial neural networks. (c2000)
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masterThesis -
145
Haplotype inference by pure-parsimony using revamped delayed haplotype selection. (c2011)
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masterThesis -
146
The multi-parameterized cluster editing problem
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147
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148
Incremental Genetic Algorithm
Published 2006“…If these problems are not small in size, it becomes costly to use a genetic algorithm to reoptimize them after each modification. …”
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Augmented arithmetic optimization algorithm using opposite-based learning and lévy flight distribution for global optimization and data clustering
Published 2022“…This paper proposes a new data clustering method using the advantages of metaheuristic (MH) optimization algorithms. …”
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151
Minimizing using BBO and DFO methods
Published 2022“…In [1], Nour and Zeidan proposed a numerical algorithm to solve optimal control problems involving sweeping processes. …”
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masterThesis -
152
Arabic Text Classification Using Modified Artificial Bee Colony Algorithm for Sentiment Analysis: The Case of Jordanian Dialect
Published 2022“…The second phase, modified the Artificial Bee Colony (ABC) Algorithm, with Upper Confidence Bound (UCB) Algorithm, to promote the exploitation ability for the minimum dimension, to get the minimum number of the optimal feature, then using forward feature selection strategy by four classifiers of machine learning algorithms: (K-Nearest Neighbors (KNN), Support vector machines (SVM), Naïve-Bayes (NB), and Polynomial Neural Networks (PNN). …”
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154
On the parameterized parallel complexity and the vertex cover problem
Published 2016“…We initiate the study of FPPT with the well-known k-vertex cover problem. In particular, we present a parallel algorithm that outperforms the best known parallel algorithm for this problem: using O(m) instead of O(n2) parallel processors, the running time improves from 4logn+O(kk) to O(k⋅log3n) , where m is the number of edges, n is the number of vertices of the input graph, and k is an upper bound of the size of the sought vertex cover. …”
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Stochastic optimal power flow framework with incorporation of wind turbines and solar PVs using improved liver cancer algorithm
Published 2024“…Moreover, ILCA is applied to resolve the stochastic renewable‐based (wind turbines + PVs) optimal power flow problem using a modified RER‐based IEEE 57‐bus. The objective of this work is to obtain the minimum predicted power losses and enhance the predicted voltage stability. …”
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Fuzzy genetic algorithm for floorplanning
Published 2020“…Genetic algorithms (GAs) have been found to be very effective in solving numerous optimization problems, especially those with many (possibly) conflicting and noisy objectives. …”
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160
BUC algorithm for iceberg cubes
Published 2003“…The Uniform distribution is used as a basis for comparison. Results show that when the cube is sparse there is a correlation between the data distribution and the running time of the algorithm. …”
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