DocumentCode
2973973
Title
Cuckoo search for business optimization applications
Author
Xin-She Yang ; Deb, Sujay ; Karamanoglu, Mehmet ; Xingshi He
Author_Institution
Sch. of Sci. & Technol., Middlesex Univ., London, UK
fYear
2012
fDate
21-22 Nov. 2012
Firstpage
1
Lastpage
5
Abstract
Cuckoo search has become a popular and powerful metaheuristic algorithm for global optimization. In business optimization and applications, many studies have focused on support vector machine and neural networks. In this paper, we use cuckoo search to carry out optimization tasks and compare the performance of cuckoo search with support vector machine. By testing benchmarks such as project scheduling and bankruptcy predictions, we conclude that cuckoo search can perform better than support vector machine.
Keywords
business data processing; neural nets; optimisation; search problems; support vector machines; bankruptcy predictions; business optimization applications; cuckoo search; global optimization; metaheuristic algorithm; neural networks; project scheduling; support vector machine; Algorithm design and analysis; Business; Optimization; Particle swarm optimization; Prediction algorithms; Search problems; Support vector machines; algorithm; cuckoo search; metaheuristics; optimization; swarm intelligence;
fLanguage
English
Publisher
ieee
Conference_Titel
Computing and Communication Systems (NCCCS), 2012 National Conference on
Conference_Location
Durgapur
Print_ISBN
978-1-4673-1952-2
Type
conf
DOI
10.1109/NCCCS.2012.6412973
Filename
6412973
Link To Document