TY - JOUR T1 - Rulet Tekerleği Yöntemi Kullanılarak Simbiyotik Organizmalar Arama Algoritmasının Geliştirilmesi TT - Improving Symbiotic Organisms Search Algorithm Using Roulette Wheel Method AU - Sönmez, Yusuf AU - Ünal, Mesut PY - 2019 DA - December JF - Uluslararası Teknolojik Bilimler Dergisi JO - IJTS PB - Isparta University of Applied Sciences WT - DergiPark SN - 1309-1220 SP - 186 EP - 200 VL - 11 IS - 3 LA - tr AB - SimbiyotikOrganizmalar Arama (Symbiotic Organisms Search-SOS) Algoritması, doğadaki canlıların simbiyotikilişkilerini taklit ederek geliştirilmiş güçlü bir meta-sezgisel optimizasyonalgoritmasıdır. Bu çalışmada SOS algoritmasına rulet tekerleği yöntemikullanılarak geliştirilmesi amaçlamıştır. Geliştirilen R-SOS algoritması ileçözümün olması beklenen optimum noktaya daha da yaklaşması sağlanmıştır.Geliştirilen algoritma 30 benchmark üzerinde test edilmiş ve sonuçların klasikSOS algoritmasına göre daha güçlü olduğu görülmüştür. KW - Simbiyotik Organizmalar KW - Arama Algoritması KW - Optimizasyon KW - Rulet Tekerleği N2 - SymbioticOrganisms Search (SOS) Algorithm is a powerful meta-heuristic optimizationalgorithm developed by simulating the symbiotic relationships of livingcreatures in nature. In this study, it was aimed to develop SOS algorithm byusing roulette wheel method. With the R-SOS algorithm developed, the solutionis approached to the expected optimum point. The developed algorithm was testedon 30 benchmarks and the results were found to be stronger than the classicalSOS algorithm. CR - [1] Cheng, M.Y., Prayogo, D. (2014). Symbiotic organisms search: a new metaheuristic optimization algorithm. Comput. Struct. 139, 98-112. CR - [2] Goldberg, D. E., & Holland, J. H.. (1988). Genetic algorithms and machine learning. Machine learning, 3(2), 95-99,. CR - [3] Kennedy, J.; Eberhart, R. C., (1995). Particle Swarm Optimization, Proc. of the IEEE Int. Conference on Neural Networks, 4, 1942-1948,. CR - [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. CR - [5] Karaboga, D., & Basturk, B. (2008). On the performance of artificial bee colony (ABC) algorithm. Applied soft computing, 8(1), 687-697. CR - [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. CR - [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). CR - [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. CR - [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. CR - [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. UR - https://dergipark.org.tr/en/pub/utbd/issue//569045 L1 - https://dergipark.org.tr/en/download/article-file/918024 ER -