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 :
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