Refine your search
Collections
Co-Authors
Year
A B C D E F G H I J K L M N O P Q R S T U V W X Y Z All
Bourouba, Hadj
- A Cuckoo Optimisation Algorithm for Solving Financial Portfolio Problem
Abstract Views :380 |
PDF Views:0
Authors
Affiliations
1 Institute of Economic and Commercial Sciences, University (Center) of Relizane, DZ
2 University of Mostaganem, DZ
1 Institute of Economic and Commercial Sciences, University (Center) of Relizane, DZ
2 University of Mostaganem, DZ
Source
International Journal of Banking, Risk and Insurance, Vol 5, No 2 (2017), Pagination: 47-53Abstract
Over the years, different solution methods to financial portfolio optimisation problems have been developed and applied. In recent years, however, there has been an increasing use of heuristic methods as alternative to other methods. In this study, a newly developed heuristic method called Cuckoo Optimisation Algorithm (COA) is presented to solve financial portfolio optimisation problems. The results on a five stock application example show that the proposed cuckoo algorithm solves the portfolio optimisation problem more optimally than genetic algorithm and ant colony algorithm.Keywords
Financial Portfolio Optimisation, Approxi-Mate Methods, Cuckoo Optimisation Algorithm.
References
- Abido, M. A., & Elazouni, A. M. (2011). Multiobjective evolutionary finance-based scheduling: Entire projects’ portfolio. Journal of Computing in Civil Engeneering, 25(1).
- Abusrhan, A., & Daoud, A. (2013). A hybrid Algorithm Using a genetic algorithm and cuckoo search algorithm to solve the salesman problem and its application to multiple sequence alignment. International Journal of Advanced Science and Technology, 61, 29-38.
- Anagnostopoulos, K. P., & Mamanis, G. (2012). Multiobjective evolutionary algorithms for complex portfolio optimization problems. Computational Management Science, 8.
- Bhandari, A. K., Singh, V. K., Kumar, A., & Singh, G. K. (2014). Cuckoo search and wind driven optimation based study of satellite image segmentation for multilevel thresholding using Kapur’s entropy. Expert Systems with Applications, 41.
- Xing, B., & Gao, W. J. (2014). Innovative computational intelligence: A rough guide to 134 clever algorithms. Springer International Publishing, Switzerland.
- Brabazon, A., & O’Neil, M. (2008). Natural computing in computational finance. Springer-Verlarg Berlin Heidelberg.
- Burnwal. S., & Deb, S. (2013). Scheduling optimization of flexible manufacturing system using cuckoo search-based approach. International Journal of Advanced Manufacturing Technology, 64(5-8).
- Coello, C. G., Lamont, B., & Vanveldhuizen, D. A. (2007). Evolutionary algorithms for solving multiobjective problems (2nd Ed.). Springer Science and Business Media. LLC.
- Drezewski, R., & Siwik, L. (2010). Co-evolutionary multi-agent system for portfolio optimization. Studies in Computational Intelligence, 100.
- Elham, S., & Bijari, M. (2014). Evaluation of the efficiency of cuckoo optimization algorithm. International Journal on Computational Sciences & Applications (IJCSA), 4(2).
- Harlow, H. V. (1991). Asset allocation in a downside-risk framework. Financial Analysts Journal, 30-40.
- Hochreiter, R. (2013). Solving dynamic optimization problems with revolutionary algorithms. International Journal of Innovative Computing and Applications, 5(3).
- Kochenberger, G. A., Glover, F. Alidaee, B., & Rsgo, C. (2004). A unified modeling and solution framework for combinatorial optimization problems. Operational Research Specktum, 26(2).
- Lipinski, P., Winezura, K., & Wojcik, J. (2007). Building risk-optimal portfolio using evolutionary strategies. In Giacobini M (eds) Proceedings of EvoWorkshops, Lecture Notes in Computer Science 4448.
- Malik, K., & Tayal, A. (2014). Comparison of nature inspired meta-heuristic algorithms. International Journal of Electronic and Electrical Engineering, 7(8), 729-802.
- Puchinger, J., & Raidl, G. R. (2005). Combining Metaheuristics and Exact Algorithms in Combinatorial Optimization: A Survey and Classification. IWINAC’05 Proceedings of the First international work-conference on the Interplay Between Natural and Artificial Computation conference on Artificial Intelligence and Knowledge Engineering Applications: a bio inspired approach - Volume Part II.
- Rajabioun, R. (2011). Cuckoo optimization algorithm. Applied Soft Computing, 11, 5508-551.
- Sefiane, S., & Benbouziane, M. (2013). A meta-heuristic ant colony optimization method to portfolio optimization. International Journal of Financial Management, 3(4).
- Senel. K., Pamukcu, A. B., & Yanik, S. (2006). An evolutionary approach to asset allocation contribution pension schemes. Studies in Computational Intelligence, 100.
- Simon, D. (2008). Biography based Optimization. IEEE, Transactions on Evolutionary Computation, 12.
- Slingh, U., & Rattan, M. (2014). Design of linear and circular antenna arrays using cuckoo optimization algorithm. Progress in Electromagnetics Research, 46, 1-11.
- Taimoor, A., & Shoemaker, C. A. (2015). Multi objective optimization of computationally expensive multimodal functions with RBF surrogates and multi-rule selection. Journal of Global Optimization, 64(1).