DE\BBO is the combination of differential evolution and biogeography based optimization algorithm to get a better optimization algorithm in terms of convergence speed. In DE\BBO, constant crossover rate has been used which sometimes affect the performance of the hybrid algorithm leading to increase in convergence speed. To cope up with this problem, variable crossover rate has been introduced in the hybrid algorithm helping in removing the problem of constant crossover rate. Modified algorithm has been named as DE\BBO\L in which local search mutation and variable crossover rate are used. Testing of BBO, DE\BBO\rand\1 and DE\BBO\L has been performed on different test functions. The results reveal that DE\BBO\L with variable crossover rate is better than DE\BBO with constant crossover rate.
목차
Abstract 1. Introduction 2. Related Work 3. Modified Hybridization of DE/BBO 4. Experimental Results 5. Conclusion and Future Scope References
키워드
OptimizationMutationDifferential evolutionBiogeography Based Optimization
저자
Ekta [ Department of Computer Science, GNDU, RC, Jalandhar ]
Mandeep Kaur [ Department of Computer Science, GNDU, RC, Jalandhar ]
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.9 No.1