원문정보
보안공학연구지원센터(IJSIP)
International Journal of Signal Processing, Image Processing and Pattern Recognition
Vol.9 No.3
2016.03
pp.155-166
피인용수 : 0건 (자료제공 : 네이버학술정보)
초록
영어
Since the ant colony algorithm is proposed, it has achieved the remarkable achievements in many fields. With the development of the times, the traditional ant colony algorithm exposes its limitations for solving the questions. In this paper, we improve the ant colony algorithm. And we combine the ant colony algorithm with the genetic algorithm. Then, we propose the GAPSPAC algorithm. The algorithm combines the advantages of the genetic algorithm and the ant colony algorithm. And it overcomes the disadvantages to improve the efficiency of solving the questions. In the last experiment, we can see the algorithm has the better problem solving ability and the stability.
목차
Abstract
1. Introduction
2. Basic Theory
2.1. Mathematical Description of the Ant Colony Algorithm
2.2. The Basic Steps of the Genetic Algorithm
2.3. PSO Algorithm
3. GAPSOAC Algorithm
3.1. GAPSO Algorithm
3.2. The Path Selection Formula
3.3. The Updated Method of the Pheromone
4. Experiment
5. Conclusion
References
1. Introduction
2. Basic Theory
2.1. Mathematical Description of the Ant Colony Algorithm
2.2. The Basic Steps of the Genetic Algorithm
2.3. PSO Algorithm
3. GAPSOAC Algorithm
3.1. GAPSO Algorithm
3.2. The Path Selection Formula
3.3. The Updated Method of the Pheromone
4. Experiment
5. Conclusion
References
저자정보
참고문헌
자료제공 : 네이버학술정보