Rulet Tekerleği Yöntemi Kullanılarak Simbiyotik Organizmalar Arama Algoritmasının Geliştirilmesi

Öz

Simbiyotik Organizmalar Arama (Symbiotic Organisms Search-SOS)    Algoritması, doğadaki canlıların simbiyotik ilişkilerini taklit ederek geliştirilmiş güçlü bir meta-sezgisel optimizasyon algoritmasıdır. Bu çalışmada SOS algoritmasına rulet tekerleği yöntemi kullanılarak geliştirilmesi amaçlamıştır. Geliştirilen R-SOS algoritması ile çözümün olması beklenen optimum noktaya daha da yaklaşması sağlanmıştır. Geliştirilen algoritma 30 benchmark üzerinde test edilmiş ve sonuçların klasik SOS algoritmasına göre daha güçlü olduğu görülmüştür.

Anahtar Kelimeler

• Simbiyotik Organizmalar,Arama Algoritması,Optimizasyon,Rulet Tekerleği

Improving Symbiotic Organisms Search Algorithm Using Roulette Wheel Method

Öz

Symbiotic Organisms Search (SOS) Algorithm is a powerful meta-heuristic optimization algorithm developed by simulating the symbiotic relationships of living creatures in nature. In this study, it was aimed to develop SOS algorithm by using roulette wheel method. With the R-SOS algorithm developed, the solution is approached to the expected optimum point. The developed algorithm was tested on 30 benchmarks and the results were found to be stronger than the classical SOS algorithm.

Anahtar Kelimeler

• Symbiotic Organisms,Search Algorithm,Optimization,Roulette Wheel

Kaynakça

1. [1] Cheng, M.Y., Prayogo, D. (2014). Symbiotic organisms search: a new metaheuristic optimization algorithm. Comput. Struct. 139, 98-112.
2. [2] Goldberg, D. E., & Holland, J. H.. (1988). Genetic algorithms and machine learning. Machine learning, 3(2), 95-99,.
3. [3] Kennedy, J.; Eberhart, R. C., (1995). Particle Swarm Optimization, Proc. of the IEEE Int. Conference on Neural Networks, 4, 1942-1948,.
4. [4]. Storn R., (1997). Diferential Evolution, A Simple and Efficient Heuristic Strategy for Global Optimization over Continuous Spaces", Journal of Global Optimization, 11: 341-359.
5. [5] Karaboga, D., & Basturk, B. (2008). On the performance of artificial bee colony (ABC) algorithm. Applied soft computing, 8(1), 687-697.
6. [6] Jones, D. F., Mirrazavi, S. K., & Tamiz, M. (2002). Multi-objective meta-heuristics: An overview of the current state-of-the-art. European journal of operational research, 137(1), 1-9.
7. [7] Yang, X. S., & Deb, S. (2009, December). Cuckoo search via Lévy flights. In 2009 World Congress on Nature & Biologically Inspired Computing (NaBIC) (pp. 210-214).
8. [8] Baker, J.E. (1985). Adaptive Selectşon Methods for Genetic Algorithms, Proc.1st Int. Conf. Genetic Algorithms and their Applications, Lawrence Erlbaum Associates, Hillsdale, NJ, pp.100-101.

9. [9] Malhotra, R., Singh N., Singh Y. (2011). Genetic Algorithms: Concepts, Design for Optimization of Process Controllers, Computer and Information Science, Vol. 4, No.2, 39.
10. [10] Jain, A., Jain, S., Chande, P.K., (2010). Formulation of Genetic Algorithm to Generate Good Quality Cource Timetable, Intnational Journal of Innovation, Management and Technology, Vol. 1, No.3, 248.