Whale Optimization Algorithm for Numerical Constrained Optimization

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 different type of optimization test problems. In this paper, we aim to investigate and analyse 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.

___

[1] Y. Çelik, Optimizasyon problemlerinde bal arıları evlilik optimizasyonu algoritmasının performansının geliştirilmesi, Konya: Selçuk Üniversitesi Fen Bilimleri Enstitüsü Doktora Tezi, 2013.

[2] A. Govan, Introduction to Optimization, Carolina State: Carolina State University SAMSI NDHS Undergraduate workshop, 2006.

[3] A. Aydın, Metasezgisel Yöntemlerle Uçak Çizelgeleme Problemi Optimizasyonu, İstanbul: Marmara Üniversitesi Doktora Tezi, 2009.

[4] 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.

[5] N. Bacanin, M. Tuba, “Improved seeker optimization algorithm hybridized with firefly algorithm for constrained optimization problems,” Elsevier, pp. 197-207, 2014.

[6] A. Lewis, S. Mirjalili, “The Whale Optimization Algorithm,” Advances in Engineering Software, pp. 51-67, 2016.

[7] R. Özdağ, M. Canayaz, “Data Clustering Based on the Whale Optimization,” Middle East Journal of Technic, vol. 2, no. 1, 2017.

[8] M. Demir, M. Canayaz, “Balina Optimizasyon Algoritması ve Yapay Sinir Ağı ile Öznitelik Seçimi,” in Artificial Intelligence and Data Processing Symposium, Malatya, 2017.

[9] N. Devarakonda, R. Saidala, “Bubble-Net Hunting Strategy of Whales based Optimized feature selection for Email Classification,” in 2nd International Conference for Convergence in Technology, 2017.

[10] 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.

[11] 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

[12] 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.

[13] 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.

[14] X.-S. Yang, Nature-Inspired Metaheuristic Algorithms, Luniver Press, 2010.

[15] B. Akay, D. Karaboga, “A modified Artificial Bee Colony (ABC) algorithm for constrained optimization problems,” Applied Soft Computing, vol. 11, pp. 3021-3031, 2011.

[16] S. Talatahari, Xin-She Yang, “Bat algorithm for constrained optimization tasks,” Neural Comput, Applic, p. 239–1255, 2013.

[17] J. Zeng, J. Pan, C. Sun, “An improved vector particle swarm optimization for constrained optimization problems,” Information Sciences, no. 181, p. 1153–1163, 2011.

[18] C. Coello, E. Mezura-Montes, “A Simple Multimembered Evolution Strategy to Solve Constrained Optimization Problems,” IEEE TRANSACTIONS ON EVOLUTIONARY COMPUTATION, no. 9, 2005.

[19] 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.

[20] 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.
ACADEMIC PLATFORM-JOURNAL OF ENGINEERING AND SCIENCE-Cover
  • ISSN: 2147-4575
  • Yayın Aralığı: Yılda 3 Sayı
  • Başlangıç: 2013
  • Yayıncı: Akademik Perspektif Derneği
Sayıdaki Diğer Makaleler

Kompozit Malzemelerin Tornalanması Esnasında Oluşan Kesme Kuvvetlerinin Optimizasyonu

Emin SALUR, Abdullah ASLAN, Mustafa KUNTOĞLU, Aydın GÜNEŞ, Ömer Sinan ŞAHİN

LTE Ağlarda Remote-Host ile PG-W Arasındaki Kuyruk Yönetim Algoritmalarının Performans Analizi

Muhammet ÇAKMAK, Zafer ALBAYRAK

TIG Kaynak Yöntemiyle Birleştirilmiş Alüminyum 1050 Alaşımının Mekanik Ve Mikroyapı Özellikleri

Arife Kübra DEMİRBAŞ, Sinem ÇEVİK

Physical Insights into the Bio-preservation of Proteins by Glassy Solvents: Why is Glycerol better than Trehalose at low Temperatures?

Taner E. DİRAMA

Implementation of a Vibration Absorbers to Euler-Bernoulli Beam and Dynamic Analysis of Moving Car

Mehmet Akif KOÇ

Yapım Firmalarının Kurumsal Risk Yönetimi Olgunluğunda Risk Yöneticisinin Rolü

Tuğçe ERCAN, Kübra ARI

R22 ve Alternatifleri R438A ile R417A Soğutucu Akışkanları için Kızılötesi Görüntü İşleme Teknikleri Kullanarak, Soğutma Sistem Performansının İncelenmesi

Ferzan KATIRCIOĞLU, Zafer CİNGİZ, Yusuf ÇAY, ALİ ETEM GÜREL, SUAT SARIDEMİR, Ahmet KOLİP

Uyarlanabilir Çevrimiçi İngilizce Seviye Tespit Sınavı ile Türkiye’deki İngilizce Seviyesinin Analizi

M. Fatih ADAK, Mustafa AKPINAR, Ali UZUNYOLCU

Effect of the Oscillator Length on the Characteristics of a Feedback Type Fluidic Oscillator

Mehmet N. TOMAC

The Effect of the Length of the Customer Event History and the Staying Power of the Predictive Models in the Customer Churn Prediction: Case Study of Migros Sanal Market

Birol YÜCEOĞLU