Research Article
Year 2020,
Volume: 8 Issue: 3, 547 - 554, 30.09.2020
Abstract
Balina optimizasyon
algoritması (BOA) son zamanlarda geliştirilen nature-inspired tabanlı
meta-sezgisel bir optimizasyon algoritmasıdır. Algoritma kambur balinaların
avlanırken kullandıkları kabarcık avlanma stratejilerinden esinlenerek 2016 yılında
geliştirilmiştir. Geliştirilen her algoritmanın performansını ortaya çıkarmak
için, optimizasyon test problemleri üzerinde test edilmesi gerekmektedir. Biz
bu çalışmamızda, BOA sınırlamasız optimizasyona adapte ederek, literatürde
sıkça kullanılan 13 sınırlamalı test problemleri üzerinde test edilmiştir. Elde edilen deneysel test sonuçları ayrıntılı
olarak sunulmuştur. Ayrıca BOA’ nın deneysel test sonuçları, literatürden
alınan diğer meta-sezgisel optimizasyon algoritmalarının sonuçları ile
kıyaslanmış ve algoritmanın başarımı
gösterilmiştir.
References
- Y. Çelik, Optimizasyon problemlerinde bal arıları evlilik optimizasyonu algoritmasının performansının geliştirilmesi, Konya: Selçuk Üniversitesi Fen Bilimleri Enstitüsü Doktora Tezi, 2013.
- A. Govan, Introduction to Optimization, Carolina State : Carolina State University SAMSI NDHS Undergraduate workshop, 2006.
- A. Aydın, Metasezgisel Yöntemlerle Uçak Çizelgeleme Problemi Optimizasyonu, İstanbul: Marmara Üniversitesi Doktora Tezi, 2009.
- O. Engin, M.C. Akkoyunlu, “Kesikli harmoni arama algoritması ile optimizasyon problemlerinin çözümü, Literatür araştırması,” S.Ü. Mühendislik ve Mimarlık Fakültesi Dergisi, vol. 26, no. 4, 2011.
- N. Bacanin, M. Tuba, “Improved seeker optimization algorithm hybridized with firefly algorithm for constrained optimization problems,” Elsevier, pp. 197-207, 2014.
- A. Lewis, S. Mirjalili, “The Whale Optimization Algorithm,” Advances in Engineering Software, pp. 51-67, 2016.
- R. Özdağ, M. Canayaz, “Data Clustering Based on the Whale Optimization,” Middle East Journal of Technic, vol. 2, no. 1, 2017.
- M. Demir, M. Canayaz, “Balina Optimizasyon Algoritması ve Yapay Sinir Ağı ile Öznitelik Seçimi,” in Artificial Intelligence and Data Processing Symposium, Malatya, 2017.
- N. Devarakonda, R. Saidala, “Bubble-Net Hunting Strategy of Whales based Optimized feature selection for E-mail Classification,” in 2nd International Conference for Convergence in Technology, 2017.
- A. Mostafa, H. Hefny, M. Houseni, A. Hassanien, “Liver segmentation in MRI images based on whale optimization algorithm,” Multimedia Tools and Applications, no. 76, p. 24931–24954, 2017.
- I. Aljarah, H. Faris, S. Mirjalili, “Optimizing connection weights in neural networks using the whale optimization algorithm,” Soft Computing, vol. 22, pp. 1-15, 2016.
- J. Liang,, T. Runarsson, E. Mezura-Montes, M. Clerc, P. N. Suganthan, A. Coello, K. Deb, “Problem De¯nitions and Evaluation Criteria for the CEC 2006 Special Session on Constrained Real-Parameter Optimization,” Indian Institute of Technology, Kanpur, 2006.
- A. Goldbogen, S. Friedlaender, J. Calambokidis, F. McKenna, M. Simon, P. Nowacek, “Integrative Approaches to the Study of Baleen Whale Diving Behavior, Feeding,” BioScience, vol. 2, no. 63, pp. 90-100, 2013.
- B. Akay, D. Karaboga, “A modified Artificial Bee Colony (ABC) algorithm for constrained optimization problems,” Applied Soft Computing, vol. 11, pp. 3021-3031, 2011.
- S. Talatahari, Xin-She Yang, “Bat algorithm for constrained optimization tasks,” Neural Comput, Applic, p. 239–1255, 2013. J. Zeng, J. Pan, C. Sun, “An improved vector particle swarm optimization for constrained optimization problems,” Information Sciences, no. 181, p. 1153–1163, 2011. C. Coello, E. Mezura-Montes, “A Simple Multimembered Evolution Strategy to Solve Constrained Optimization Problems,” IEEE TRANSACTIONS ON EVOLUTIONARY COMPUTATION, no. 9, 2005.
- L. Gao, D. Zoua, H. Liu, S. Li, “A novel modified differential evolution algorithm for constrained optimization problems,” Computers and Mathematics with Applications, no. 61, pp. 1608-1623, 2011.
- G. Jia, Y. Wang, Z. Cai, Y. Jin, “An improved (μ + λ)-constrained differential evolution for constrained optimization,” Information Sciences, vol. 222, pp. 302-322, 2013.
Year 2020,
Volume: 8 Issue: 3, 547 - 554, 30.09.2020
Abstract
Whale Optimization
Algorithm (WOA), WOA is a recently developed, nature-inspired, meta-heuristic
optimization algorithm. The algorithm was developed
in 2016, inspired by bubble hunting strategies used by humpback whales.To
determine the performance of each optimization algorithm developed, they should
be tested on a differenttype of optimization test problems. In this paper, we aim to investigate and analyze WOA logarithm on constrained
optimization the performance and accuracy of the proposed method are examined on 13 (G1-G13) constrained numerical
benchmark functions, and the obtained results are compared with other meta-heuristic optimization algorithms
which taken from the literature. The
experimental results show that WOA has low performance on constrained
optimization
References
- Y. Çelik, Optimizasyon problemlerinde bal arıları evlilik optimizasyonu algoritmasının performansının geliştirilmesi, Konya: Selçuk Üniversitesi Fen Bilimleri Enstitüsü Doktora Tezi, 2013.
- A. Govan, Introduction to Optimization, Carolina State : Carolina State University SAMSI NDHS Undergraduate workshop, 2006.
- A. Aydın, Metasezgisel Yöntemlerle Uçak Çizelgeleme Problemi Optimizasyonu, İstanbul: Marmara Üniversitesi Doktora Tezi, 2009.
- O. Engin, M.C. Akkoyunlu, “Kesikli harmoni arama algoritması ile optimizasyon problemlerinin çözümü, Literatür araştırması,” S.Ü. Mühendislik ve Mimarlık Fakültesi Dergisi, vol. 26, no. 4, 2011.
- N. Bacanin, M. Tuba, “Improved seeker optimization algorithm hybridized with firefly algorithm for constrained optimization problems,” Elsevier, pp. 197-207, 2014.
- A. Lewis, S. Mirjalili, “The Whale Optimization Algorithm,” Advances in Engineering Software, pp. 51-67, 2016.
- R. Özdağ, M. Canayaz, “Data Clustering Based on the Whale Optimization,” Middle East Journal of Technic, vol. 2, no. 1, 2017.
- M. Demir, M. Canayaz, “Balina Optimizasyon Algoritması ve Yapay Sinir Ağı ile Öznitelik Seçimi,” in Artificial Intelligence and Data Processing Symposium, Malatya, 2017.
- N. Devarakonda, R. Saidala, “Bubble-Net Hunting Strategy of Whales based Optimized feature selection for E-mail Classification,” in 2nd International Conference for Convergence in Technology, 2017.
- A. Mostafa, H. Hefny, M. Houseni, A. Hassanien, “Liver segmentation in MRI images based on whale optimization algorithm,” Multimedia Tools and Applications, no. 76, p. 24931–24954, 2017.
- I. Aljarah, H. Faris, S. Mirjalili, “Optimizing connection weights in neural networks using the whale optimization algorithm,” Soft Computing, vol. 22, pp. 1-15, 2016.
- J. Liang,, T. Runarsson, E. Mezura-Montes, M. Clerc, P. N. Suganthan, A. Coello, K. Deb, “Problem De¯nitions and Evaluation Criteria for the CEC 2006 Special Session on Constrained Real-Parameter Optimization,” Indian Institute of Technology, Kanpur, 2006.
- A. Goldbogen, S. Friedlaender, J. Calambokidis, F. McKenna, M. Simon, P. Nowacek, “Integrative Approaches to the Study of Baleen Whale Diving Behavior, Feeding,” BioScience, vol. 2, no. 63, pp. 90-100, 2013.
- B. Akay, D. Karaboga, “A modified Artificial Bee Colony (ABC) algorithm for constrained optimization problems,” Applied Soft Computing, vol. 11, pp. 3021-3031, 2011.
- S. Talatahari, Xin-She Yang, “Bat algorithm for constrained optimization tasks,” Neural Comput, Applic, p. 239–1255, 2013. J. Zeng, J. Pan, C. Sun, “An improved vector particle swarm optimization for constrained optimization problems,” Information Sciences, no. 181, p. 1153–1163, 2011. C. Coello, E. Mezura-Montes, “A Simple Multimembered Evolution Strategy to Solve Constrained Optimization Problems,” IEEE TRANSACTIONS ON EVOLUTIONARY COMPUTATION, no. 9, 2005.
- L. Gao, D. Zoua, H. Liu, S. Li, “A novel modified differential evolution algorithm for constrained optimization problems,” Computers and Mathematics with Applications, no. 61, pp. 1608-1623, 2011.
- G. Jia, Y. Wang, Z. Cai, Y. Jin, “An improved (μ + λ)-constrained differential evolution for constrained optimization,” Information Sciences, vol. 222, pp. 302-322, 2013.
There are 17 citations in total.
Details
| Primary Language | English |
|---|---|
| Journal Section | Articles |
| Authors | |
| Publication Date | September 30, 2020 |
| Submission Date | April 9, 2019 |
| Published in Issue | Year 2020 Volume: 8 Issue: 3 |
