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Siyasi Parti Mitinglerinin Gezgin Satıcı Problemi Yaklaşımı ile Analizi

Yıl 2016, Cilt: 4 Sayı: 4, 223 - 238, 01.10.2016

Öz

Son yıllarda karmaşık optimizasyon ve araştırma problemlerinde doğal seçim sürecine dayalı evrim stratejileri kullanılmaktadır. Bu çalışmada evrim stratejileri kapsamındaki genetik algoritmalar konusunun temel bilgileri anlatılmıştır ve genetik algoritmalar yardımı ile Gezgin Satıcı Problemi ele alınmıştır. Gezgin satıcı problemi verilen birbirine bağlı şehir, düğüm vb. gibi noktalara ulaşımı ve başlangıç noktasına geri dönüşü ele alan kısıtlı en çok bilinen optimizasyon yöntemlerinden biridir. Gezgin satıcı problemlerine örnek oluşturabilecek siyasi partilerin mitinglerinin optimal şekilde planlaması amacıyla Travelling Salesman Problem TSP programı kullanılarak miting planlama analizi yapılmıştır. Analiz sonuçları ile elde edilebilecek maliyet ve zaman tasarrufundan bahsedilmiştir.

Kaynakça

  • Adeli, H. ve Sarma, K. C. (2006), Cost Optimization of Structures: Fuzzy Logic, Genetic Algorithms and Parallel Computing, John Wiley & Sons Ltd., Great Britain.
  • Affenzeller, M., Winkler, S., Wagner, S. ve Beham, A. (2009), Genetic Algorithms and Genetic Programming: Modern Concepts and Pratical Applications (Volume 6: Numerical Insights), Taylor & Francis Group, LLC, United States America.
  • Akar, S. B. ve Şahingöz, Ö. K. (2015), “Solving Asymmetric Traveling Salesman Problem Using Genetic Algorithm”, 23. Sinyal İşleme ve İletişim Uygulamaları, 16–19 Mayıs, Malatya
  • Alba, E. ve Dorronsoro, B. (2008), Cellular Genetic Algorithms, Operations Research / Computer Science Interfaces Series 42, Springer Business + Science Media, New York.
  • Ashokkumar, V. ve Hebbal, S. S. (2014), “Route Optimization of Automated Warehouse with the Aid of Modified Genetic Algorithms (MGA)”, International Review of Mechanical Engineering, 8, 4, s. 667-679.
  • Bandyopadhyay, S. ve Pal, S. K. (2007), Classification and Learning Using Genetic Algorithms: Applications in Bioinformatics and Web Intelligence (Natural Computing Series), Springer Business + Science Media, Berlin.
  • Borna, K. ve Hashemi, V. D. (2014), “An Improved Genetic Algorithm With A Local Optimization Strategy And An Extra Mutation Level For Solving Traveling Salesman Problem”, Computer Science, Engineering and Information Technology, 4, 4, s. 47-53.
  • Chen, Y. P. (2006), Extending the Scability of Linkage Genetic Algorithms (Studies in Fuzziness and Soft Computing, Volume 190), Springer Business + Science Media, Netherlands.
  • Cox, E. (2005), Fuzzy Modelling and Genetic Algorithms for Data Mining and Exploration (The Morgan Kaufmann Series in Data Management Systems), Elsevier Inc., United States of America.
  • Çakır, M. ve Yılmaz, G. (2015), Traveling Salesman Problem Optimization with Parallel Genetic Algorithm, 23. Sinyal İşleme ve İletişim Uygulamaları, 16–19 Mayıs, Malatya.
  • Çolak, S. (2010), Genetik Algoritmalar Yardımı ile Gezgin Satıcı Probleminin Çözümü Üzerine Bir Uygulama, Ç. Ü. Sosyal Bilimler Enstitüsü Dergisi, 19, 3, s. 423-438.
  • Dreo, J., Petrowski, A., Siarry, P. ve Taillard, E. (2006), Metaheuristics For Hard Optimization Methods and Case Studies, Springer Business + Science Media, Berlin.
  • Elmas, Ç. (2011), Yapay Zeka Uygulamaları (Yapay Sinir Ağı, Bulanık Mantık, Sinirsel Bulanık Mantık, Genetik Algoritma), 2. Baskı, Seçkin Yayıncılık, Ankara.
  • Fogel, L. J., Owens, A. J. ve Walsh, M. J. (1966) Artificial Intelligence Through Simulated Evolution, John Wiley & Sons, Inc., New York. Gen, M., Cheng, R. ve Lin, L. (2008), Network Models and Optimization Multiobjective Genetic Algorithm Approach (Decision Engineering), Springer-Verlag London Limited, London.
  • Goldberg, D. E. (1989), Genetic Algorithms in Search, Optimization and Machine Learning, Addison-Wesley Publishing Company, Inc., New York.
  • Haupt, R. L. ve Haupt, S. E. (2004), Pratical Genetic Algorithms, 2nd Edition, John Wiley & Sons, Inc., New Jersey.
  • Holland, J. H. (1975), Adaptation In Natural And Artificial Systems: An Introductory Analysis With Applications To Biology, Control, And Artificial Intelligence, University of Michigan Press, Ann Arbor.
  • Jiang, Y., Weise, T., Lassig, J., Chiong, R. ve Athauda, R. (2014), “Comparing a Hybrid Branch and Bound Algorithm with Evolutionary Computation Methods, Local Search and their Hybrids on the TSP”, IEEE Symposium on Computational Intelligence in Production and Logistics Systems, 9–12 December, Orlando, Florida.
  • Kiraly, A. ve Abonyi, J. (2015), Redesign of the Supply of Mobile Mechanics Based on a Novel Genetic Optimization Algorithm Using Google Maps API, Engineering Applications of Artificial Intelligence, 38, 2015, s. 122–130.
  • Kubalik, J. ve Snizek, M. (2014), “A Novel Evolutionary Algorithm with Indirect Representation and Extended Nearest Neighbor Constructive Procedure for Solving Routing Problems”, International Conference on Intelligent Systems Design and Applications, 28–30 November, Okinawa.
  • Nabiyev, V. V. (2012), Yapay Zeka, 4. Baskı, Seçkin Yayıncılık, Ankara. Pan, Y. ve Xia, Y. (2014), “Solving TSP by Dismantling Cross Paths”, IEEE International Conference on Orange Technologies, 20–23 September, Xian.
  • Rekiek, B. ve Delchambre, A. (2006), Assembly Line Design: The Balancing of Mixed-Model Hybrid Assembly Lines with Genetic Algorithms (Springer Series in Advanced Manufacturing), Springer Business + Science Media, Germany.
  • Satyhan, A., Ernest, N. ve Cohen, K. (2015) “Genetic Fuzzy Approach for Control and Task Planning Applications”, American Institute of Aeronautics and Astronautics Infotech @ Aerospace, January, 2015, s. 1-9.
  • Schwefel, H. P. (1993), Evolution And Optimum Seeking: The Sixth Generation, John Wiley & Sons, Inc., New York.
  • Sivanandam, S. N. ve Deepa, S. N. (2008), Introduction to Genetic Algorithms, Springer Business + Science Media, Berlin.
  • Tabatabaei, N. M., Asadian, K. ve Boushehri, N. S. (2014), “Short Term Power Load Forecasting Based On Comparison Of Acs To Probabilistic Traveling Salesman Problem”, Technical and Physical Problems of Engineering, 21, 6, s. 66-74.
  • Taşkın, Ç. ve Emel, G. G. (2009), Sayısal Yöntemlerde Genetik Algoritmalar, Alfa Aktüel, Bursa.
  • Ünal, M., Ak, A., Topuz, V. ve Erdal, H. (2013), Optimization of PID Controllers Using Ant Colony and Genetic Algorithms, Springer Business + Science Media, Berlin.
  • Yılmaz, H., Doğan, Ş. ve Koca, G. Ö. (2015), “Mayın İmhası için Optimum Mesafe Tespitinde Küre Yüzeyinde 3 Boyutlu Gezgin Satıcı Probleminin Kullanılması”, 23. Sinyal İşleme ve İletişim Uygulamaları, 16–19 Mayıs, Malatya.
  • T. C. Ulaştırma Denizcilik ve Haberleşme Bakanlığı, KGM, (2015), http://www.kgm.gov.tr/Sayfalar/KGM/SiteTr/Uzakliklar/illerArasiM esafe.aspx, 27.08.2015.

The Analysis of Political Parties' Public Meetings with Travelling Salesman Problem Approach

Yıl 2016, Cilt: 4 Sayı: 4, 223 - 238, 01.10.2016

Öz

In recent years, solutions is sought by evolution strategies based on natural selection for complex optimization and research problems. This study discusses the basics of the topics genetic algorithm covered in the evolution strategies and Travelling Salesman Problem are tackled with the help of genetic algorithm. Traveling Salesman Problem handling of the connected point such as city, node and so on. transport and return to the starting point, is one of the most well-known restricted optimization methods. The public meeting planning could be an example of travelling salesman problem is analysed for optimality by using Travelling Salesman Problem TSP program. Analysis results is mentioned with cost and time savings can be obtained

Kaynakça

  • Adeli, H. ve Sarma, K. C. (2006), Cost Optimization of Structures: Fuzzy Logic, Genetic Algorithms and Parallel Computing, John Wiley & Sons Ltd., Great Britain.
  • Affenzeller, M., Winkler, S., Wagner, S. ve Beham, A. (2009), Genetic Algorithms and Genetic Programming: Modern Concepts and Pratical Applications (Volume 6: Numerical Insights), Taylor & Francis Group, LLC, United States America.
  • Akar, S. B. ve Şahingöz, Ö. K. (2015), “Solving Asymmetric Traveling Salesman Problem Using Genetic Algorithm”, 23. Sinyal İşleme ve İletişim Uygulamaları, 16–19 Mayıs, Malatya
  • Alba, E. ve Dorronsoro, B. (2008), Cellular Genetic Algorithms, Operations Research / Computer Science Interfaces Series 42, Springer Business + Science Media, New York.
  • Ashokkumar, V. ve Hebbal, S. S. (2014), “Route Optimization of Automated Warehouse with the Aid of Modified Genetic Algorithms (MGA)”, International Review of Mechanical Engineering, 8, 4, s. 667-679.
  • Bandyopadhyay, S. ve Pal, S. K. (2007), Classification and Learning Using Genetic Algorithms: Applications in Bioinformatics and Web Intelligence (Natural Computing Series), Springer Business + Science Media, Berlin.
  • Borna, K. ve Hashemi, V. D. (2014), “An Improved Genetic Algorithm With A Local Optimization Strategy And An Extra Mutation Level For Solving Traveling Salesman Problem”, Computer Science, Engineering and Information Technology, 4, 4, s. 47-53.
  • Chen, Y. P. (2006), Extending the Scability of Linkage Genetic Algorithms (Studies in Fuzziness and Soft Computing, Volume 190), Springer Business + Science Media, Netherlands.
  • Cox, E. (2005), Fuzzy Modelling and Genetic Algorithms for Data Mining and Exploration (The Morgan Kaufmann Series in Data Management Systems), Elsevier Inc., United States of America.
  • Çakır, M. ve Yılmaz, G. (2015), Traveling Salesman Problem Optimization with Parallel Genetic Algorithm, 23. Sinyal İşleme ve İletişim Uygulamaları, 16–19 Mayıs, Malatya.
  • Çolak, S. (2010), Genetik Algoritmalar Yardımı ile Gezgin Satıcı Probleminin Çözümü Üzerine Bir Uygulama, Ç. Ü. Sosyal Bilimler Enstitüsü Dergisi, 19, 3, s. 423-438.
  • Dreo, J., Petrowski, A., Siarry, P. ve Taillard, E. (2006), Metaheuristics For Hard Optimization Methods and Case Studies, Springer Business + Science Media, Berlin.
  • Elmas, Ç. (2011), Yapay Zeka Uygulamaları (Yapay Sinir Ağı, Bulanık Mantık, Sinirsel Bulanık Mantık, Genetik Algoritma), 2. Baskı, Seçkin Yayıncılık, Ankara.
  • Fogel, L. J., Owens, A. J. ve Walsh, M. J. (1966) Artificial Intelligence Through Simulated Evolution, John Wiley & Sons, Inc., New York. Gen, M., Cheng, R. ve Lin, L. (2008), Network Models and Optimization Multiobjective Genetic Algorithm Approach (Decision Engineering), Springer-Verlag London Limited, London.
  • Goldberg, D. E. (1989), Genetic Algorithms in Search, Optimization and Machine Learning, Addison-Wesley Publishing Company, Inc., New York.
  • Haupt, R. L. ve Haupt, S. E. (2004), Pratical Genetic Algorithms, 2nd Edition, John Wiley & Sons, Inc., New Jersey.
  • Holland, J. H. (1975), Adaptation In Natural And Artificial Systems: An Introductory Analysis With Applications To Biology, Control, And Artificial Intelligence, University of Michigan Press, Ann Arbor.
  • Jiang, Y., Weise, T., Lassig, J., Chiong, R. ve Athauda, R. (2014), “Comparing a Hybrid Branch and Bound Algorithm with Evolutionary Computation Methods, Local Search and their Hybrids on the TSP”, IEEE Symposium on Computational Intelligence in Production and Logistics Systems, 9–12 December, Orlando, Florida.
  • Kiraly, A. ve Abonyi, J. (2015), Redesign of the Supply of Mobile Mechanics Based on a Novel Genetic Optimization Algorithm Using Google Maps API, Engineering Applications of Artificial Intelligence, 38, 2015, s. 122–130.
  • Kubalik, J. ve Snizek, M. (2014), “A Novel Evolutionary Algorithm with Indirect Representation and Extended Nearest Neighbor Constructive Procedure for Solving Routing Problems”, International Conference on Intelligent Systems Design and Applications, 28–30 November, Okinawa.
  • Nabiyev, V. V. (2012), Yapay Zeka, 4. Baskı, Seçkin Yayıncılık, Ankara. Pan, Y. ve Xia, Y. (2014), “Solving TSP by Dismantling Cross Paths”, IEEE International Conference on Orange Technologies, 20–23 September, Xian.
  • Rekiek, B. ve Delchambre, A. (2006), Assembly Line Design: The Balancing of Mixed-Model Hybrid Assembly Lines with Genetic Algorithms (Springer Series in Advanced Manufacturing), Springer Business + Science Media, Germany.
  • Satyhan, A., Ernest, N. ve Cohen, K. (2015) “Genetic Fuzzy Approach for Control and Task Planning Applications”, American Institute of Aeronautics and Astronautics Infotech @ Aerospace, January, 2015, s. 1-9.
  • Schwefel, H. P. (1993), Evolution And Optimum Seeking: The Sixth Generation, John Wiley & Sons, Inc., New York.
  • Sivanandam, S. N. ve Deepa, S. N. (2008), Introduction to Genetic Algorithms, Springer Business + Science Media, Berlin.
  • Tabatabaei, N. M., Asadian, K. ve Boushehri, N. S. (2014), “Short Term Power Load Forecasting Based On Comparison Of Acs To Probabilistic Traveling Salesman Problem”, Technical and Physical Problems of Engineering, 21, 6, s. 66-74.
  • Taşkın, Ç. ve Emel, G. G. (2009), Sayısal Yöntemlerde Genetik Algoritmalar, Alfa Aktüel, Bursa.
  • Ünal, M., Ak, A., Topuz, V. ve Erdal, H. (2013), Optimization of PID Controllers Using Ant Colony and Genetic Algorithms, Springer Business + Science Media, Berlin.
  • Yılmaz, H., Doğan, Ş. ve Koca, G. Ö. (2015), “Mayın İmhası için Optimum Mesafe Tespitinde Küre Yüzeyinde 3 Boyutlu Gezgin Satıcı Probleminin Kullanılması”, 23. Sinyal İşleme ve İletişim Uygulamaları, 16–19 Mayıs, Malatya.
  • T. C. Ulaştırma Denizcilik ve Haberleşme Bakanlığı, KGM, (2015), http://www.kgm.gov.tr/Sayfalar/KGM/SiteTr/Uzakliklar/illerArasiM esafe.aspx, 27.08.2015.
Toplam 30 adet kaynakça vardır.

Ayrıntılar

Birincil Dil Türkçe
Bölüm Research Article
Yazarlar

İrfan Ertuğrul Bu kişi benim

Abdullah Özçil Bu kişi benim

Yayımlanma Tarihi 1 Ekim 2016
Yayımlandığı Sayı Yıl 2016 Cilt: 4 Sayı: 4

Kaynak Göster

ISNAD Ertuğrul, İrfan - Özçil, Abdullah. “Siyasi Parti Mitinglerinin Gezgin Satıcı Problemi Yaklaşımı Ile Analizi”. Siyaset, Ekonomi ve Yönetim Araştırmaları Dergisi 4/4 (Ekim 2016), 223-238.