Search Results - (( java application optimized algorithm ) OR ( using selection problem algorithm ))
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Attribute reduction based scheduling algorithm with enhanced hybrid genetic algorithm and particle swarm optimization for optimal device selection
Published 2022“…Enhance hybrid genetic algorithm and particle Swarm optimization are developed to select the optimal device in either fog or cloud. …”
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2
Enhancement of Ant Colony Optimization for Grid Job Scheduling and Load Balancing
Published 2011“…The resource allocation problem is modeled as a graph that can be used by the ant to deliver its pheromone. …”
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3
Ant colony optimization algorithm for load balancing in grid computing
Published 2012“…The resource allocation problem is modelled as a graph that can be used by the ant to deliver its pheromone.This graph consists of four types of vertices which are job, requirement, resource and capacity that are used in constructing the grid resource management element. …”
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4
Computer Lab Timetabling Using Genetic Algorithm Case Study - Unit ICT
Published 2006“…Genetic Algorithm is one of the most popular optimization solutions used in various applications such as scheduling. …”
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5
Improving Class Timetabling using Genetic Algorithm
Published 2006“…We have targeted the research on class timetabling problem. Hence, Genetic Algorithm (GA) is used as one of the most popular optimization solutions. …”
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6
Examination timetabling using genetic algorithm case study: KUiTTHO
Published 2005“…Genetic Algorithm (GA) is one of the most popular optimization solutions. …”
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7
Features selection for intrusion detection system using hybridize PSO-SVM
Published 2016“…Features selection process can be considered a problem of global combinatorial optimization in machine learning. …”
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8
Examination Timetabling Using Genetic Algorithm Case Study : KUiTTHO
Published 2005“…Genetic Algorithm (GA) is one of the most popular optimization solutions. …”
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9
Resource management in grid computing using ant colony optimization
Published 2011“…Resources with high pheromone value are selected to process the submitted jobs.Global pheromone update is performed after completion processing the jobs in order to reduce the pheromone value of resources.A simulation environment was developed using Java programming to test the performance of the proposed EACO algorithm against other ant based algorithm, in terms of resource utilization.Experimental results show that EACO produced better grid resource management solution.…”
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Monograph -
10
Input-output based relation combinatorial testing using whale optimization algorithm for generating near optimum number of test suite
Published 2025“…Combinatorial testing offers an alternative to overcome the problem. This study proposes a combinatorial testing method utilizing the Whale Optimization Algorithm (WOA). …”
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Web-based expert system for material selection of natural fiber- reinforced polymer composites
Published 2015“…In the exponentially growing material database, selection of optimal material for engineering design is Multi Criteria Decision Making (MCDM) problem as many properties of each material influence the selection process. …”
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12
Optimization of blood vessel detection in retina images using multithreading and native code for portable devices
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Performance evaluation of real-time multiprocessor scheduling algorithms
Published 2016“…These results suggests that optimal algorithms may turn to be non-optimal when practically implemented, unlike USG which reveals far less scheduling overhead and hence could be practically implemented in real-world applications. …”
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14
Route Optimization System
Published 2005“…After much research into the many algorithms available, and considering some, including Genetic Algorithm (GA), the author selected Dijkstra's Algorithm (DA). …”
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Formulating new enhanced pattern classification algorithms based on ACO-SVM
Published 2013“…ACO originally deals with discrete optimization problem.In applying ACO for solving SVM model selection problem which are continuous variables, there is a need to discretize the continuously value into discrete values.This discretization process would result in loss of some information and hence affects the classification accuracy and seeking time.In this algorithm we propose to solve SVM model selection problem using IACOR without the need to discretize continuous value for SVM.The second algorithm aims to simultaneously solve SVM model selection problem and selects a small number of features.SVM model selection and selection of suitable and small number of feature subsets must occur simultaneously because error produced from the feature subset selection phase will affect the values of SVM model selection and result in low classification accuracy.In this second algorithm we propose the use of IACOMV to simultaneously solve SVM model selection problem and features subset selection.Ten benchmark datasets were used to evaluate the proposed algorithms.Results showed that the proposed algorithms can enhance the classification accuracy with small size of features subset.…”
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Improved Salp Swarm Algorithm based on opposition based learning and novel local search algorithm for feature selection
Published 2020“…An improved version of Salp Swarm Algorithm (ISSA) is proposed in this study to solve feature selection problems and select the optimal subset of features in wrapper-mode. …”
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Dengue classification system using clonal selection algorithm / Karimah Mohd
Published 2012“…Some popular intelligent techniques like Genetic Algorithm, Fuzzy Logic and Artificial Neural Network are often used by reasearcher to perform classifcation problems. …”
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19
Feature selection and model selection algorithm using incremental mixed variable ant colony optimization for support vector machine classifier
Published 2013“…In order to enhance SVM performance, these problems must be solved simultaneously because error produced from the feature subset selection phase will affect the values of the SVM parameters and resulted in low classification accuracy.Most approaches related with solving SVM model selection problem will discretize the continuous value of SVM parameters which will influence its performance.Incremental Mixed Variable Ant Colony Optimization (IACOMV) has the ability to solve SVM model selection problem without discretising the continuous values and simultaneously solve the two problems.This paper presents an algorithm that integrates IACOMV and SVM.Ten datasets from UCI were used to evaluate the performance of the proposed algorithm.Results showed that the proposed algorithm can enhance the classification accuracy with small number of features.…”
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A Comparative Study on three Component Selection Mechanisms for Hyper-Heuristics in Expensive Optimization
Published 2018“…Numerous studies in optimization problems often lead to tailoring a specific algorithm to adapt to the problem instances, especially in expensive optimization problems. …”
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