원문정보
보안공학연구지원센터(IJUNESST)
International Journal of u- and e- Service, Science and Technology
Vol.7 No.4
2014.08
pp.159-170
피인용수 : 0건 (자료제공 : 네이버학술정보)
초록
영어
In order to effectively address the lack of basic ant colony algorithm in terms of parameters, we use four-step method instead of the popular three-step, based on a large number of experiments of the parameters setting, this paper summed up an effective selection method for m, α, β, ρ and Q parameters to select the best combination of parameters. Applying the improved ant colony algorithms including optimal retention policy ant system, max-min ant system, ant-based sorting systems and best-worst ant system, performance comparison analysis was conducted with the same TSP problems, and experiments proved that the proposed method of parameter combinations greatly improves the speed of convergence.
목차
Abstract
1. Introduction
2. Ant Colony Algorithm Overview
2.1. Basic Principles of Ant Colony Algorithm
2.2. Shortness of Ant Colony Algorithm
3. Study of Ant colony Algorithm Parameter Setting
3.1. Hardware/software Platform
3.2. Effects of Ants Number on Basic ant Colony Algorithm
3.3. Information Heuristic Factor and Expectations Heuristic factor
4. Experimental Discussion for Ant colony Algorithm
4.1. Improved Ant Colony Optimization Algorithm
4.2. Comparative Analysis of Simulation and Algorithm Performance
5. Conclusion
Acknowledgements
References
1. Introduction
2. Ant Colony Algorithm Overview
2.1. Basic Principles of Ant Colony Algorithm
2.2. Shortness of Ant Colony Algorithm
3. Study of Ant colony Algorithm Parameter Setting
3.1. Hardware/software Platform
3.2. Effects of Ants Number on Basic ant Colony Algorithm
3.3. Information Heuristic Factor and Expectations Heuristic factor
4. Experimental Discussion for Ant colony Algorithm
4.1. Improved Ant Colony Optimization Algorithm
4.2. Comparative Analysis of Simulation and Algorithm Performance
5. Conclusion
Acknowledgements
References
저자정보
참고문헌
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