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
키워드
Ant colony algorithmGenetic algorithmCombinatorial optimization
저자
Xu Yan [ Xi’ an Physical Education University, Shaanxi, China ]
보안공학연구지원센터(IJSIP) [Science & Engineering Research Support Center, Republic of Korea(IJSIP)]
설립연도
2006
분야
공학>컴퓨터학
소개
1. 보안공학에 대한 각종 조사 및 연구
2. 보안공학에 대한 응용기술 연구 및 발표
3. 보안공학에 관한 각종 학술 발표회 및 전시회 개최
4. 보안공학 기술의 상호 협조 및 정보교환
5. 보안공학에 관한 표준화 사업 및 규격의 제정
6. 보안공학에 관한 산학연 협동의 증진
7. 국제적 학술 교류 및 기술 협력
8. 보안공학에 관한 논문지 발간
9. 기타 본 회 목적 달성에 필요한 사업
간행물
간행물명
International Journal of Signal Processing, Image Processing and Pattern Recognition
간기
격월간
pISSN
2005-4254
수록기간
2008~2016
십진분류
KDC 505DDC 605
이 권호 내 다른 논문 / International Journal of Signal Processing, Image Processing and Pattern Recognition Vol.9 No.3