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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| 主要作者: | |
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| 格式: | Thesis |
| 語言: | English |
| 出版: |
2017
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| 主題: | |
| 在線閱讀: | http://eprints.usm.my/42907/1/JAMALUDIN__SALLIM.pdf |
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| 總結: | 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. |
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