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
Kaynakça
- [1] Cheng, M.Y., Prayogo, D. (2014). Symbiotic organisms search: a new metaheuristic optimization algorithm. Comput. Struct. 139, 98-112.
- [2] Goldberg, D. E., & Holland, J. H.. (1988). Genetic algorithms and machine learning. Machine learning, 3(2), 95-99,.
- [3] Kennedy, J.; Eberhart, R. C., (1995). Particle Swarm Optimization, Proc. of the IEEE Int. Conference on Neural Networks, 4, 1942-1948,.
- [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] Karaboga, D., & Basturk, B. (2008). On the performance of artificial bee colony (ABC) algorithm. Applied soft computing, 8(1), 687-697.
- [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] 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] 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] 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] 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.
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] Cheng, M.Y., Prayogo, D. (2014). Symbiotic organisms search: a new metaheuristic optimization algorithm. Comput. Struct. 139, 98-112.
- [2] Goldberg, D. E., & Holland, J. H.. (1988). Genetic algorithms and machine learning. Machine learning, 3(2), 95-99,.
- [3] Kennedy, J.; Eberhart, R. C., (1995). Particle Swarm Optimization, Proc. of the IEEE Int. Conference on Neural Networks, 4, 1942-1948,.
- [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] Karaboga, D., & Basturk, B. (2008). On the performance of artificial bee colony (ABC) algorithm. Applied soft computing, 8(1), 687-697.
- [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] 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] 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] 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] 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.
Ayrıntılar
| Birincil Dil | Türkçe |
|---|---|
| Konular | Elektrik Mühendisliği |
| Bölüm | Araştırma Makalesi |
| Yazarlar | |
| Yayımlanma Tarihi | 30 Aralık 2019 |
| Yayımlandığı Sayı | Yıl 2019 Cilt: 11 Sayı: 3 |
Dergi isminin Türkçe kısaltması "UTBD" ingilizce kısaltması "IJTS" şeklindedir.
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