A hybrid genetic scheduling strategy (H-GA) is described in this article, H-GA combines with grouping and load balancing strategy based on traditional genetic algorithm (GA). First, tasks are divided into several different subgroups by task granularity. Then, task subgroup which is selected by granularity from big to small is used to schedule by the genetic algorithm, and during scheduling, the load balancing strategy is used to adjust task distribution in the individual. Grouping can cut down the length of individual, which speeds up convergence of genetic algorithm. Load balancing strategy can make the individual better, which also speeds up convergence of genetic algorithm. The implementation shows that converging speed of H-GA is faster than GA, and result of H-GA is optimal than GA if the iteration times are equal.
목차
Abstract 1. Introduction 2. Related works 2.1. Standard PSO 2.2. Harmony search 3. The realization of IPSO based of HS 4. Simulation results and comparisons 4.1. Experimental parameters setting 4.2. Test functions 4.3. Experimental results 5. Conclusions Refrenece:
저자
Benting Wan [ software institute, Jiangxi University of finance and economics, Nanchang, Jiangxi,China ]
보안공학연구지원센터(IJHIT) [Science & Engineering Research Support Center, Republic of Korea(IJHIT)]
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 Hybrid Information Technology
간기
격월간
pISSN
1738-9968
수록기간
2008~2016
십진분류
KDC 505DDC 605
이 권호 내 다른 논문 / International Journal of Hybrid Information Technology Vol.1 No.1