Official thesis title: Ant Colony Optimization and Applications
1. Full name: Do Duc Dong
2. Sex: Male
3. Date of birth: 28/9/1981
4. Place of birth: Hanoi
5. Admission decision number: No. 3613/SĐH dated 22/10/2009 of President of Vietnam National University, Hanoi.
6. Changes in academic process: No
(List the forms of change and corresponding times)
7. Official thesis title: Ant Colony Optimization and Applications
8. Major: Computer science
9. Code: 62 48 01 01
10. Supervisors: Assoc. Prof. Dr Hoang Xuan Huan
(Full name, academic title and degree)
11. Summary of the new findings of the thesis:
- Basing some analysis of pheromone update rules in ACO algorithms applied to the Traveling Salesman Problem, proposed three new improvements which are Multi-Level Ant System, Smoothed Max-min Ant System and 3-Levels Ant System. Their pheromone update rules are simple, and their elite effect has been shown by the experimental results based on the standard test data in compare to the Max-Min Ant System.
- Using Ant Colony Optimization (ACO) metaheuristic, named ACOHAP, to infer haplotypes from unphased Single Polymorphism Nucleotide (SNP) marker data. Experiments showed that ACOHAP outperformed the state-of-the-art methods for haplotype inference in both simulated and biological data.
- Using Ant Colony Optimization (ACO) metaheuristic, named AcoSeeD, to find Optimal Spaced Seeds in Biological Sequence Search. Experiments showed that AcoSeeD outperformed the state-of-the-art method.
- Proposed meta-heuristic approaches to select the best parameters for regulatory prediction from transcription factor binding profiles. Experimental results show that our approach outperforms existing methods and the potentials for further analysis beyond the prediction.