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Yapay Zeka Tabanlı Optimizasyon Algoritmalarının Araştırılması

Year 2019, Volume 1, Issue 1, 11 - 16, 13.03.2019

Abstract

Bu çalışmada yapay zeka, derin öğrenme ve makne öğrenmesi kavramları açıklanmış ve yapay zekanın optimizasyon algoritmaları ile ilişkileri incelenmiştir. Derin öğrenme ve makine öğrenmesinin temel aşamaları ve içeriği tanımlanarak, yapay zeka'nın optimizasyon ile olan ilişkisi araştırılmıştır. Bu inceleme ışığında dört adet optimizasyon algoritması ele alınmıştır. Literatürde sıklıkla kullanılan bu yapay zeka tabanlı optimizasyon algoritmalarının adımları ve işlemleri ayrıntılı olarak incelenmiştir. İncelenen algoritmaların hepsi doğa ile ilgili olarak geiştirilen algoritmaları içermektedir. Bu algoritmaları sayacak olursak; Bakteri Yiyecek Arama Optimizasyonu, Çiçek Tozlaşması Optimizasyonu, Genetik Algoritma ve Yapay Arı Kolonisi algoritmalarıdır. Literatürde yapılan çalışmalar, Bakteri Yiyecek Arama'da koli basili olarak bilinen bakterilerin yaşam döngüsünin modellenmesi, Çiçek Tozlaşmasın'da çiçekli bitkilerde meydana gelen tozlaşma olayının modellenmesi, Genetik Algoritma'da biyolojide çeşitliliği ve kaliteli bir populasyonun oluşmasını sağlayan genlerin oluşumunun modellenmesi ve Yapay Arı Kolonisi'nde arıların davranış mantıklarının modellenmesi ile gerçekleştirilmiştir. Bu çalışmalardan Yapay Arı Kolonisi dışındaki çalışmalar doğrudan doğada gerçekleşen olayların modellemesiyken, Yapay Arı Kolonisi arı kolonilerinin davranışlarıyla bir örnek uzayının içerisindeki beklenen ve beklenmeyen değerler kümesinin nasıl ayrılabileceği ile ilgili bir yorum eklenerek ortaya atılmıştır. Bu çalışma içerisinde tüm bu algoritmaların aşamaları ve her adımda uygulanan mantık irdelenmektedir. Bu algoritmalarla ilgili bazı yeni önemli uygulama alanları da ele alınmıştır. Sonuç kısmında ise bu çalışmalar ışığında gelecek çalışmalar için neler yapılabileceği ifade edilmiştir.

References

  • algorithms, Proceedings of an International Conference on Genetic Algorithms and their applications, 101-111.
  • Bayraktar, D. ve Çebi, F., 2003, Yöneylem Araştırması-1 – Ders Notları (İstanbul Teknik Üniversitesi – İşletme Fakültesi), http://web.itu.edu.tr/~cebife/TUM_YA1notlari.pdf.
  • Buchanan, B. G., 2005, A (Very) Brief History of Artificial Intelligence. AI Magazine, 26(4), 25th Anniversary Edition. 53-60, http://www.aaai.org/ojs/index.php/aimagazine/article/view/1848/1746.
  • Darwin, C., 2015 (Çev.), Türlerin Kökeni, İstanbul, Evrensel Basım Yayın.
  • Das, S., Biswas, A., Dasgupta, S. ve Abraham, A., 2009, Bacterial foraging optimization algorithm: theoretical foundations, analysis, and applications, In: Foundations of Computational Intelligence, Eds: Springer, p. 23-55.
  • Fisher, R. A., 1958:1930, The Genetical Theory of Natural Selection, (1930: Clarendon Press) Oxford University Press.
  • Floudas, C. A., 2013, Deterministic Global Optimization: Theory, Methods and Applications, Springer Science & Business Media.
  • Geem, Z. W., Kim, J. H. ve Loganathan, G., 2001, A new heuristic optimization algorithm: harmony search, Simulation, 76 (2), 60-68.
  • Hezer, S., & Kara, Y. (2013). Eşzamanli Dağıtımlı ve Toplamalı Araç Rotalama Problemlerinin Çözümü için Bakteriyel Besin Arama Optimizasyonu Tabanlı bir Algoritma. Journal of The Faculty of Engineering & Architecture of Gazi University, 28(2).
  • Hoffman K. ve Padberg M., “Combinatorial and Integer Optimization”, www.iris.gmu.edu/~khoffma/papers/newcomb1.html. (07/05/2002)., s. 1-10.
  • Holland, J. H., 2012, Genetic Algorithms. Scholarpedia, 7(12), 1482, http://www.scholarpedia.org/article/Genetic_algorithms.
  • Kaplan, W., 2011, Maxima and minima with applications: practical optimization and duality, John Wiley & Sons.
  • Karaboğa, D., Akay, B. ve Öztürk, C., 2007, Artificial bee colony (ABC) optimization algorithm for training feed-forward neural networks, International Conference on Modeling Decisions for Artificial Intelligence, 318-329.
  • Karaboğa, D. ve Baştürk, B., 2007, A powerful and efficient algorithm for numerical function optimization: artificial bee colony (ABC) algorithm, Journal of global optimization, 39 (3), 459-471.
  • Karaboğa, D., 2014, Yapay Zekâ Optimizasyon Algoritmaları, Ankara, Nobel Akademik Yayıncılık.
  • Kaymaz, İ., 2016, Optimizasyon Teknikleri – Ders Notları (Atatürk Üniversitesi – Fen Bilimleri Enstitüsü), http://194.27.49.11/makine/ikaymaz/optimizasyon/. Kearfott, R. B., 2013, Rigorous global search: continuous problems, Springer Science & Business Media.
  • Korkmaz, E., Akgüngör, A. P., Türkiye'deki Araç Sahipliğinin Çiçek Tozlaşma Algoritması ile Tahmini, Gazi Mühendislik Bilimleri Dergisi 2018, 4(1): 39-45. Kramer, O., 2017, Genetic Algorithm Essentials, Springer.
  • Küçüksille, E. U., Tokmak, M., 2011, Yapay Arı Kolonisi Algoritması Kullanarak Otomatik Ders Çizelgeleme, Süleyman Demirel Üniversitesi, Fen Bilimleri Enstitüsü Dergisi, 15-3( 2011),203-210.
  • Lee, K. Y. ve El-Sharkawi, M. A., 2008, Modern heuristic optimization techniques: theory and applications to power systems, John Wiley & Sons.
  • Makridakis, S., 2017, The Forthcoming Artificial Intelligence (AI) Revolution: Its Impact on Society and Firms. Neapolis University - HEPHAESTUS Repository, http://hephaestus.nup.ac.cy/bitstream/handle/11728/9254/THE_FORTHCOMING ...2017_Full.pdf?sequence=1&isAllowed=y.
  • McCarthy, J., Minsky, M. L., Rochester, N. ve Shannon, C. E., 2006:1955, A proposal for the dartmouth summer research project on artificial intelligence, 31 Ağustos 1955. AI Magazine, 27(4), 12, http://www.aaai.org/ojs/index.php/aimagazine/article/view/1904/1802.
  • Mitchell, M., 1998, An Introduction to Genetic Algorithms, MIT Press.
  • Passino, K. M., 2012, Bacterial foraging optimization, Innovations and Developments of Swarm Intelligence Applications, 219-233.
  • Rao, S. S., 2009, Classical optimization techniques, In: Engineering Optimization: Theory and Practice, Eds, p. 63-118.
  • Tereshko, V., 2000, Reaction-diffusion model of a honeybee colony’s foraging behaviour, International Conference on Parallel Problem Solving from Nature, 807-816.
  • Tereshko, V. ve Loengarov, A., 2005, Collective decision making in honey-bee foraging dynamics, Computing and Information Systems, 9 (3), 1.
  • Weise, T., 2009, Global Optimization Algorithms – Theory and Application, http://www.it-weise.de/projects/book.pdf.
  • Yang, X.-S., 2010a, A new metaheuristic bat-inspired algorithm. Nature inspired cooperative strategies for optimization (NICSO 2010): 65-74.
  • Yang, X.-S., 2010b, Nature-Inspired Metaheuristic Algorithms, Luniver Press.
  • Yang, X.-S., 2012, Flower pollination algorithm for global optimization, International Conference on Unconventional Computing and Natural Computation, 240-249.
  • Yang, X.-S., Karamanoglu, M. ve He, X., 2014, Flower pollination algorithm: A novel approach for multiobjective optimization, Engineering optimization, 46 (9), 1222- 1237.
  • Zelinka, I., Snasel, V. ve Abraham, A., 2012, Handbook of optimization: from classical to modern approach, Springer Science & Business Media.

Investigation of Artificial Intelligence Based Optimization Algorithm

Year 2019, Volume 1, Issue 1, 11 - 16, 13.03.2019

Abstract

In this study, the concept of artificial intelligence (AI), deep learning and machine learning are explained and the relation between AI and optimization algorithms are examined. By defining the basic stages and the content of deep learning and machine learning, the relationship of AI with optimization is investigated. In the light of this study, four optimization algorithms were considered. The steps and the operations of these artificial intelligence-based optimization algorithms which were widely used in the literature have been examined in detail. All the algorithms discussed in this study are related to nature. These algorithms are; Bacterial Foraging Optimization, Flower Pollination Optimization, Genetic Algorithm and Artificial Bee Colony Algorithm. The studies in the literature carried out by modelling the life cycle of bacteria known as koli basil in Bacterial Foraging, the pollination event in flower pollination plants in Flower Pollination, genetics in Genetic Algorithm and the formation of genes that lead to the formation of a high quality population and bees' behavioural logic in Artificial Bee Colony. While the studies except the Artificial Bee Colony were the direct models of the phenomenon in nature, the Artificial Bee Colony was put forward by adding a comment about how the behaviours of bee colonies can be separated from the expected and unexpected values ​​in a sample space. In this study, the stages of all these algorithms and the logic used in each step are examined. Some recent important application domains related to these algorithms are also discussed. In the conclusion part, what can be done in the light of these studies as a future work is mentioned.

References

  • algorithms, Proceedings of an International Conference on Genetic Algorithms and their applications, 101-111.
  • Bayraktar, D. ve Çebi, F., 2003, Yöneylem Araştırması-1 – Ders Notları (İstanbul Teknik Üniversitesi – İşletme Fakültesi), http://web.itu.edu.tr/~cebife/TUM_YA1notlari.pdf.
  • Buchanan, B. G., 2005, A (Very) Brief History of Artificial Intelligence. AI Magazine, 26(4), 25th Anniversary Edition. 53-60, http://www.aaai.org/ojs/index.php/aimagazine/article/view/1848/1746.
  • Darwin, C., 2015 (Çev.), Türlerin Kökeni, İstanbul, Evrensel Basım Yayın.
  • Das, S., Biswas, A., Dasgupta, S. ve Abraham, A., 2009, Bacterial foraging optimization algorithm: theoretical foundations, analysis, and applications, In: Foundations of Computational Intelligence, Eds: Springer, p. 23-55.
  • Fisher, R. A., 1958:1930, The Genetical Theory of Natural Selection, (1930: Clarendon Press) Oxford University Press.
  • Floudas, C. A., 2013, Deterministic Global Optimization: Theory, Methods and Applications, Springer Science & Business Media.
  • Geem, Z. W., Kim, J. H. ve Loganathan, G., 2001, A new heuristic optimization algorithm: harmony search, Simulation, 76 (2), 60-68.
  • Hezer, S., & Kara, Y. (2013). Eşzamanli Dağıtımlı ve Toplamalı Araç Rotalama Problemlerinin Çözümü için Bakteriyel Besin Arama Optimizasyonu Tabanlı bir Algoritma. Journal of The Faculty of Engineering & Architecture of Gazi University, 28(2).
  • Hoffman K. ve Padberg M., “Combinatorial and Integer Optimization”, www.iris.gmu.edu/~khoffma/papers/newcomb1.html. (07/05/2002)., s. 1-10.
  • Holland, J. H., 2012, Genetic Algorithms. Scholarpedia, 7(12), 1482, http://www.scholarpedia.org/article/Genetic_algorithms.
  • Kaplan, W., 2011, Maxima and minima with applications: practical optimization and duality, John Wiley & Sons.
  • Karaboğa, D., Akay, B. ve Öztürk, C., 2007, Artificial bee colony (ABC) optimization algorithm for training feed-forward neural networks, International Conference on Modeling Decisions for Artificial Intelligence, 318-329.
  • Karaboğa, D. ve Baştürk, B., 2007, A powerful and efficient algorithm for numerical function optimization: artificial bee colony (ABC) algorithm, Journal of global optimization, 39 (3), 459-471.
  • Karaboğa, D., 2014, Yapay Zekâ Optimizasyon Algoritmaları, Ankara, Nobel Akademik Yayıncılık.
  • Kaymaz, İ., 2016, Optimizasyon Teknikleri – Ders Notları (Atatürk Üniversitesi – Fen Bilimleri Enstitüsü), http://194.27.49.11/makine/ikaymaz/optimizasyon/. Kearfott, R. B., 2013, Rigorous global search: continuous problems, Springer Science & Business Media.
  • Korkmaz, E., Akgüngör, A. P., Türkiye'deki Araç Sahipliğinin Çiçek Tozlaşma Algoritması ile Tahmini, Gazi Mühendislik Bilimleri Dergisi 2018, 4(1): 39-45. Kramer, O., 2017, Genetic Algorithm Essentials, Springer.
  • Küçüksille, E. U., Tokmak, M., 2011, Yapay Arı Kolonisi Algoritması Kullanarak Otomatik Ders Çizelgeleme, Süleyman Demirel Üniversitesi, Fen Bilimleri Enstitüsü Dergisi, 15-3( 2011),203-210.
  • Lee, K. Y. ve El-Sharkawi, M. A., 2008, Modern heuristic optimization techniques: theory and applications to power systems, John Wiley & Sons.
  • Makridakis, S., 2017, The Forthcoming Artificial Intelligence (AI) Revolution: Its Impact on Society and Firms. Neapolis University - HEPHAESTUS Repository, http://hephaestus.nup.ac.cy/bitstream/handle/11728/9254/THE_FORTHCOMING ...2017_Full.pdf?sequence=1&isAllowed=y.
  • McCarthy, J., Minsky, M. L., Rochester, N. ve Shannon, C. E., 2006:1955, A proposal for the dartmouth summer research project on artificial intelligence, 31 Ağustos 1955. AI Magazine, 27(4), 12, http://www.aaai.org/ojs/index.php/aimagazine/article/view/1904/1802.
  • Mitchell, M., 1998, An Introduction to Genetic Algorithms, MIT Press.
  • Passino, K. M., 2012, Bacterial foraging optimization, Innovations and Developments of Swarm Intelligence Applications, 219-233.
  • Rao, S. S., 2009, Classical optimization techniques, In: Engineering Optimization: Theory and Practice, Eds, p. 63-118.
  • Tereshko, V., 2000, Reaction-diffusion model of a honeybee colony’s foraging behaviour, International Conference on Parallel Problem Solving from Nature, 807-816.
  • Tereshko, V. ve Loengarov, A., 2005, Collective decision making in honey-bee foraging dynamics, Computing and Information Systems, 9 (3), 1.
  • Weise, T., 2009, Global Optimization Algorithms – Theory and Application, http://www.it-weise.de/projects/book.pdf.
  • Yang, X.-S., 2010a, A new metaheuristic bat-inspired algorithm. Nature inspired cooperative strategies for optimization (NICSO 2010): 65-74.
  • Yang, X.-S., 2010b, Nature-Inspired Metaheuristic Algorithms, Luniver Press.
  • Yang, X.-S., 2012, Flower pollination algorithm for global optimization, International Conference on Unconventional Computing and Natural Computation, 240-249.
  • Yang, X.-S., Karamanoglu, M. ve He, X., 2014, Flower pollination algorithm: A novel approach for multiobjective optimization, Engineering optimization, 46 (9), 1222- 1237.
  • Zelinka, I., Snasel, V. ve Abraham, A., 2012, Handbook of optimization: from classical to modern approach, Springer Science & Business Media.

Details

Primary Language English
Subjects Engineering
Journal Section Articles
Authors

Şükrü OKUL> (Primary Author)
İSTANBUL ÜNİVERSİTESİ - CERRAHPAŞA
0000-0001-6645-7933
Türkiye


Doğukan AKSU>
İSTANBUL ÜNİVERSİTESİ - CERRAHPAŞA
Türkiye


Zeynep ORMAN>
İSTANBUL ÜNİVERSİTESİ - CERRAHPAŞA
0000-0002-0205-4198
Türkiye

Publication Date March 13, 2019
Application Date November 5, 2018
Acceptance Date February 7, 2019
Published in Issue Year 2019, Volume 1, Issue 1

Cite

APA Okul, Ş. , Aksu, D. & Orman, Z. (2019). Investigation of Artificial Intelligence Based Optimization Algorithm . İstanbul Sabahattin Zaim Üniversitesi Fen Bilimleri Enstitüsü Dergisi , 1 (1) , 11-16 . Retrieved from https://dergipark.org.tr/en/pub/izufbed/issue/43839/479082

20503

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