Yarasa Algoritması ve Klonal Seçim Algoritmasının Optimizasyon Problemleri ile Performans Analizi

Evrimsel algoritmalar, özellikle optimizasyon alanında çalışan bir çok farklı araştırmacı tarafından tercih edilmektedir. Evrimsel algoritmaların verilen problemleri optimize etmenin yanı sıra, bu problemleri az sayıda iterasyon kullanarak çözmeleri bu algoritmalar için önemli bir ayırt edici özelliktir. Bu çalışmada, optimizasyon alanında verimliliği kanıtlanmış iki evrimsel algoritma; yarasa algoritması ve klonal seçim algoritması test fonksiyonları kullanılarak kıyaslanmıştır. Kıyaslama yapılan test fonksiyonlarından elde edilen sonuçlara göre, yarasa algoritması klonal seçim algoritmasına göre daha iyi bir performans göstermiştir. Ayrıca, yarasa algoritması optimizasyonun ilk safhalarında dahi yüksek çözüm kalitesine ulaşmıştır. Bu analiz, gelecek çalışmalar için evrimsel algoritmaların performans kıyaslamaları açısından rehber olarak kullanılabilir niteliktedir.

PERFORMANCE STUDY OF BAT ALGORITHM AND CLONAL SELECTION ALGORITHM FOR OPTIMIZATION TASKS

Evolutionary algorithms are preferred by many researchers in different areas for optimization tasks. It is quite important to find optimum points of problems with less number of iterations. In this paper, performance analysis of two powerful optimization algorithms; bat algorithm and clonal selection algorithm are studied using well-known benchmark functions. The experimental results show that bat algorithm outperforms clonal selection algorithm on most of the selected problems. It is also seen that bat algorithm can produce high quality results even at the first stages of iterations. This paper can be used as guidance of performance comparisons for future studies.

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1. Adarsh, B. R., Raghunathan, T., Jayabarathi, T., and Yang, X. S. (2016) Economic dispatch using chaotic bat algorithm, Energy, 96, 666-675. doi: 10.1016/j.energy.2015.12.096.
2. Bin Basir, M.A. and Binti Ahmad, F. (2014) Comparison of Swarm Algorithms for Feature Selections/Reductions, International Journal of Scientific and Engineering Research, 5, 479-486. doi: 10.1109/ISPACS.2007.4445974.
3. Dandy, G.C., Simpson, A.R., and Murphy L.J. (1996) An improved genetic algorithm for pipe network optimization, Water Resources Research, 32, 449-458. doi: 10.1029/95WR02917.
4. De Castro and Von Zuben, F. J. (2000) An evolutionary immune network for data clustering, In Neural Network, Proceedings Sixth Brazilian Symposium on, 84-89. doi: 10.1109/SBRN.2000.889718.
5. Gong M, Jiao L, Zhang L and Ma W. (2007) Improved real-valued clonal selection algorithm based on a novel mutation method, International Symposium on Intelligent Signal Processing and Communication Systems, ISPACS 2007, 662-665. doi: 10.1109/ISPACS.2007.4445974.
6. Goyal, S., and Patterh, M. S. (2016) Modified Bat Algorithm for Localization of Wireless Sensor Network, Wireless Personal Communications, 86(2), 657-670. doi: 10.1007/s11277- 015-2950-9.
7. Generalized penalized function. (2015,June) .Retrieved from http://alroomi.org/benchmarks/unconstrained/n-dimensions/172-generalized-penalized-function-no- 1
8. Test functions and datasets. (2015, January). Retrieved from http://www.sfu.ca/~ssurjano/optimization.html.
9. Sindhuja, L. S., and Padmavathi, G. (2016) Replica Node Detection Using Enhanced Single Hop Detection with Clonal Selection Algorithm in Mobile Wireless Sensor Networks, Journal of Computer Networks and Communications. doi: 10.1155/2016/1620343.
10. Ulutas, B.H. and Kulturel-Konak, S. (2011) A review of clonal selection algorithm and its applications, Artificial Intelligence Review, 36(2), 117-138.doi:10.1007/s10462-011-9206-1.
11. Vatansever, F. and Şen, D. (2013) Design of PID Controller Simulator based on Genetic Algorithm, Uludağ University Journal of The Faculty of Engineering, 18(2), 7-18. doi: 10.17482/uujfe.33406.
12. Wolpert, D.H. and Macready, WG. (1997) No free lunch theorems for optimization, IEEE Transactions on Evolutionary Computation,1, 67-82. doi: 10.1109/4235.585893.
13. Yang X.S. (2010) A new metaheuristic bat-inspired algorithm. In Nature inspired cooperative strategies for optimization, Springer Berlin Heidelberg, NICSO 2010, 65-74. doi: 10.1007/978-3-642-12538-6-6.