Öz
Geomatik mühendisliği dahil olmak üzere çok sayıda
mühendislik disiplininde önemli rol oynayan matematiksel programlama veya
optimizasyon bir uygulamalı matematik konusu olup bilgisayar bilimi ve yöneylem
araştırması ile yakından ilişkilidir. En basit şekliyle reel değişkenlere sahip
bir fonksiyonun, bu değerler için optimum değerlerin bulunması suretiyle
minimum veya maksimum yapılması optimizasyon olarak adlandırılır. Parametre
kestirimi, optimal jeodezik ağ tasarımı ve karayolu yatay ve düşey aliynman
optimizasyonu geomatik mühendisliğindeki önemli optimizasyon uygulamalarıdır.
Bu çalışmada optimizasyon ile ilgili temel kavramlar ve başlıca jeodezik
optimizasyon problemleri ele alınmıştır. Son zamanlarda yaygın bir şekilde
kullanılır hale gelen metasezgisel optimizasyon algoritmaları üzerinde
durulmuştur. Metasezgisel bir optimizasyon algoritması olan Kurbağa Sıçrama
Algoritması (KSA) incelenmiştir.
Anahtar Kelimeler
Optimizasyon Metasezgisel Algoritmalar Kurbağa Sıçrama Algoritması Jeodezi
Kaynakça
- Dorigo, M., Stützle, T. (2004). Ant colony optimization. MIT Press, Cambridge.
- Elbeltagi, E., Hegazy, T., Grierson, D. (2005). Comparison among five evolutionary-based optimization algorithm. Advanced Engineering Informatics, 19(1):43-53.
- Eusuff, M.M., Lansey, K.E. (2003). Optimization of water distribution network design using the shuffled frog leaping algorithm. Journal of Water Resource Planning and Management, 129(3):210-225.
- Haupt, R.L., Haupt, S.E. (2004). Practical Genetic Algorithms. Wiley, NY.
- Hekimoğlu, Ş., Berber, M. (2003). Effectiveness of robust methods in heterogeneous linear models. Journal of Geodesy, 76(11):706-713.
- Karaboga, D., Baştürk, B. (2007). A powerful and efficient algorithm for numerical optimization: artificial bee colony (ABC) algorithm. Journal of Global Optimization, 39(3):459-471.
- Kennedy, J., Eberhart, R.C. (1995). Particle swarm optimisation. Proceedings of the IEEE International Conference on Neural Networks IV:1942-1948, IEEE Service Center, Piscataway, NJ.
- Kuang, S.L. (1996). Geodetic network analysis and optimal design. Ann Arbor Press, Ann Arbor, MI.
- Simkooei, A.A. (2003). Formulation of L1 norm minimization in Gauss-Markov Models. Journal of Surveying Engineering, 129(1):37-43.
- Xu, P. (2003). A hybrid global optimization method: the multi-dimensional case. Journal of Computational and Applied Mathematics, 155(2):423-446.
- Yang, X.S. (2008). Nature-inspired metaheuristic algorithms. Luniver Press, University of Cambridge, UK.
- Yetkin, M., İnal, C., Yiğit, C.Ö. (2009). Use of the particle swarm optimization algorithm for second order design of levelling networks. Journal of Applied Geodesy, 3:171-178.
- Yetkin, M., İnal, C. (2011). L1 norm minimization in GPS networks. Survey Review, 43(323):523-532.
- Yetkin, M., İnal, C., Yiğit, C.Ö. (2011). The Optimal design of baseline configuration in GPS networks by using the Particle Swarm Optimization algorithm. Survey Review, 43(323):700-712.
- Yetkin, M., Berber, M. (2013). Application of the sign-constrained robust least squares method to surveying networks. Journal of Surveying Engineering, 139(1):59-65.
- Yetkin, M. (2014). Metaheuristic optimisation approach for designing reliable and robust geodetic networks. Survey Review, 45(329):136-140.
- Yetkin, M., Berber, M. (2014). Implementation of robust estimation in GPS networks using the artificial bee colony algorithm. Earth Science Informatics, 7(1):39-46.
Öz
Kaynakça
- Dorigo, M., Stützle, T. (2004). Ant colony optimization. MIT Press, Cambridge.
- Elbeltagi, E., Hegazy, T., Grierson, D. (2005). Comparison among five evolutionary-based optimization algorithm. Advanced Engineering Informatics, 19(1):43-53.
- Eusuff, M.M., Lansey, K.E. (2003). Optimization of water distribution network design using the shuffled frog leaping algorithm. Journal of Water Resource Planning and Management, 129(3):210-225.
- Haupt, R.L., Haupt, S.E. (2004). Practical Genetic Algorithms. Wiley, NY.
- Hekimoğlu, Ş., Berber, M. (2003). Effectiveness of robust methods in heterogeneous linear models. Journal of Geodesy, 76(11):706-713.
- Karaboga, D., Baştürk, B. (2007). A powerful and efficient algorithm for numerical optimization: artificial bee colony (ABC) algorithm. Journal of Global Optimization, 39(3):459-471.
- Kennedy, J., Eberhart, R.C. (1995). Particle swarm optimisation. Proceedings of the IEEE International Conference on Neural Networks IV:1942-1948, IEEE Service Center, Piscataway, NJ.
- Kuang, S.L. (1996). Geodetic network analysis and optimal design. Ann Arbor Press, Ann Arbor, MI.
- Simkooei, A.A. (2003). Formulation of L1 norm minimization in Gauss-Markov Models. Journal of Surveying Engineering, 129(1):37-43.
- Xu, P. (2003). A hybrid global optimization method: the multi-dimensional case. Journal of Computational and Applied Mathematics, 155(2):423-446.
- Yang, X.S. (2008). Nature-inspired metaheuristic algorithms. Luniver Press, University of Cambridge, UK.
- Yetkin, M., İnal, C., Yiğit, C.Ö. (2009). Use of the particle swarm optimization algorithm for second order design of levelling networks. Journal of Applied Geodesy, 3:171-178.
- Yetkin, M., İnal, C. (2011). L1 norm minimization in GPS networks. Survey Review, 43(323):523-532.
- Yetkin, M., İnal, C., Yiğit, C.Ö. (2011). The Optimal design of baseline configuration in GPS networks by using the Particle Swarm Optimization algorithm. Survey Review, 43(323):700-712.
- Yetkin, M., Berber, M. (2013). Application of the sign-constrained robust least squares method to surveying networks. Journal of Surveying Engineering, 139(1):59-65.
- Yetkin, M. (2014). Metaheuristic optimisation approach for designing reliable and robust geodetic networks. Survey Review, 45(329):136-140.
- Yetkin, M., Berber, M. (2014). Implementation of robust estimation in GPS networks using the artificial bee colony algorithm. Earth Science Informatics, 7(1):39-46.
Ayrıntılar
| Konular | Mühendislik |
|---|---|
| Bölüm | Makaleler |
| Yazarlar | |
| Yayımlanma Tarihi | 1 Aralık 2016 |
| Yayımlandığı Sayı | Yıl 2016 |