Abstract
Kuş ve balık sürülerinin davranışlarından esinlenilerek geliştirilmiş bir yöntem olan Parçacık Sürü Optimizasyonu (PSO) hızlı yakınsayan bir algoritma olması sebebi ile son yıllarda Genetik Algoritma (GA) ve Benzetim Tavlama (BT) algoritmalarının ardından en çok çalışılan sezgisel optimizasyon algoritması olmuştur. Bu çalışmada, literatürde yer alan test problemleri önce standart PSO ile ve daha sonra PSO’da yaptığımız bir iyileştirme ile geliştirilen yeni algoritma ile çeşitli değişken sayıları için çözülmüş ve bu iki algoritmanın performansı mukayese edilmiştir. Problemlerin optimum çözümlerinin standart sapması, en iyi çözüm, ortalama çözüm süreleri tablo halinde sunulmuştur. Sonuçlardan görüleceği üzere geliştirilen PSO algoritmasının standart PSO’ya göre daha iyi sonuçlar verdiği görülmüştür.
References
- Rajabioun, R., (2011). Cuckoo Optimization Algorithm, Applied Soft Computing, 11(8), 5508-5518.
- Mishra, S., Shaw, K., Mishra, D., (2012). A New Meta-heuristic Bat Inspired Classification Approach for Microarray Data, Procedia Technology, 4, 802-806.
- Yang, X. S, Hosseini,S. S. S, Gandomi, A. H., (2012) Firefly Algorithm for solving non- convex economic dispatch problems with valve loading effect, Applied Soft Computing, 12(3), 1180-1186.
- Clerc, M., (1999). The swarm and the queen: towards a deterministic and adaptive particle swarm optimization, Evolutionary Computation, CEC 99. Proceedings of the 1999 Congress on, 3, 1951-1957.
- Parrott, D., Xiaodong L., (2006). Locating and tracking multiple dynamic optima by a particle swarm model using speciation, Evolutionary Computation, IEEE Transactions on, 10(4), 440,458.
- http://www-optima.amp.i.kyoto-u.ac.jp/member/student/hedar/Hedar_files/TestGO.htm
- (Erişim tarihi: 3th of September, 2014).
Abstract
Inspired from birds and flocks, Particle Swarm Optimization (PSO) is the most studied optimization methods after Genetic Algorithm and Simulated Annealing, because of the fact that PSO converges the optimum rapidly. In this study, some benchmark problems for various variable numbers given in the literature have been solved firstly with PSO, later a novel algorithm developed with an improvement from PSO and the performances of these two algorithms have been compared. The standard deviation of the optimum solutions, best optimum value, mean of the solution time have been presented with tables. Comparing with the PSO, a novel PSO outperformed PSO
References
- Rajabioun, R., (2011). Cuckoo Optimization Algorithm, Applied Soft Computing, 11(8), 5508-5518.
- Mishra, S., Shaw, K., Mishra, D., (2012). A New Meta-heuristic Bat Inspired Classification Approach for Microarray Data, Procedia Technology, 4, 802-806.
- Yang, X. S, Hosseini,S. S. S, Gandomi, A. H., (2012) Firefly Algorithm for solving non- convex economic dispatch problems with valve loading effect, Applied Soft Computing, 12(3), 1180-1186.
- Clerc, M., (1999). The swarm and the queen: towards a deterministic and adaptive particle swarm optimization, Evolutionary Computation, CEC 99. Proceedings of the 1999 Congress on, 3, 1951-1957.
- Parrott, D., Xiaodong L., (2006). Locating and tracking multiple dynamic optima by a particle swarm model using speciation, Evolutionary Computation, IEEE Transactions on, 10(4), 440,458.
- http://www-optima.amp.i.kyoto-u.ac.jp/member/student/hedar/Hedar_files/TestGO.htm
- (Erişim tarihi: 3th of September, 2014).
Details
| Primary Language | Turkish |
|---|---|
| Subjects | Engineering |
| Journal Section | Articles |
| Authors | |
| Publication Date | March 20, 2015 |
| Published in Issue | Year 2015 Volume: 4 Issue: 1 |
