Heuristic-Based Ant Colony Optimization Algorithm For Protein Functional Module Detection In Protein Interaction Network

Ant colony optimization (ACO) is a metaheuristic algorithm that has been successfully applied to several types of optimization problems such as scheduling, routing, and more recently for solving protein functional module detection (PFMD) problem in protein-protein interaction (PPI) networks. For a s...

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Main Author: SALLIM, JAMALUDIN
Format: Thesis
Language:English
Published: 2017
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Online Access:http://eprints.usm.my/42907/1/JAMALUDIN__SALLIM.pdf
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spelling my-usm-ep.429072019-04-12T05:24:59Z Heuristic-Based Ant Colony Optimization Algorithm For Protein Functional Module Detection In Protein Interaction Network 2017-07 SALLIM, JAMALUDIN QA75.5-76.95 Electronic computers. Computer science Ant colony optimization (ACO) is a metaheuristic algorithm that has been successfully applied to several types of optimization problems such as scheduling, routing, and more recently for solving protein functional module detection (PFMD) problem in protein-protein interaction (PPI) networks. For a small PPI data size, ACO has been successfully applied to but it is not suitable for large and noisy PPI data, which has caused to premature convergence and stagnation in the searching process. To cope with the aforementioned limitations, we propose two new enhancements of ACO to solve PFMD problem. 2017-07 Thesis http://eprints.usm.my/42907/ http://eprints.usm.my/42907/1/JAMALUDIN__SALLIM.pdf application/pdf en public phd doctoral Universiti Sains Malaysia Pusat Pengajian Sains Komputer
institution Universiti Sains Malaysia
collection USM Institutional Repository
language English
topic QA75.5-76.95 Electronic computers
Computer science
spellingShingle QA75.5-76.95 Electronic computers
Computer science
SALLIM, JAMALUDIN
Heuristic-Based Ant Colony Optimization Algorithm For Protein Functional Module Detection In Protein Interaction Network
description Ant colony optimization (ACO) is a metaheuristic algorithm that has been successfully applied to several types of optimization problems such as scheduling, routing, and more recently for solving protein functional module detection (PFMD) problem in protein-protein interaction (PPI) networks. For a small PPI data size, ACO has been successfully applied to but it is not suitable for large and noisy PPI data, which has caused to premature convergence and stagnation in the searching process. To cope with the aforementioned limitations, we propose two new enhancements of ACO to solve PFMD problem.
format Thesis
qualification_name Doctor of Philosophy (PhD.)
qualification_level Doctorate
author SALLIM, JAMALUDIN
author_facet SALLIM, JAMALUDIN
author_sort SALLIM, JAMALUDIN
title Heuristic-Based Ant Colony Optimization Algorithm For Protein Functional Module Detection In Protein Interaction Network
title_short Heuristic-Based Ant Colony Optimization Algorithm For Protein Functional Module Detection In Protein Interaction Network
title_full Heuristic-Based Ant Colony Optimization Algorithm For Protein Functional Module Detection In Protein Interaction Network
title_fullStr Heuristic-Based Ant Colony Optimization Algorithm For Protein Functional Module Detection In Protein Interaction Network
title_full_unstemmed Heuristic-Based Ant Colony Optimization Algorithm For Protein Functional Module Detection In Protein Interaction Network
title_sort heuristic-based ant colony optimization algorithm for protein functional module detection in protein interaction network
granting_institution Universiti Sains Malaysia
granting_department Pusat Pengajian Sains Komputer
publishDate 2017
url http://eprints.usm.my/42907/1/JAMALUDIN__SALLIM.pdf
_version_ 1747821124963532800