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Arazi Toplulaştırma Çalışmasındaki Dağıtım Problemi Içın NSGA-II Algoritması

Year 2020, Ejosat Special Issue 2020 (ARACONF), 412 - 417, 01.04.2020
https://doi.org/10.31590/ejosat.araconf54

Abstract

Gerçek hayatta karşımıza çıkan problemlerin çözümleri için bazen optimizasyon algoritmalarına ihtiyaç duyarız. Bu problemlerin bazıları tek bir amaca sahip olurken bazıları da birden fazla amaca sahiptirler. Eğer tek bir amaç varsa, problem “tek amaçlı optimizasyon problemi” birden fazla amaç var ise “çok amaçlı optimizasyon problemi” olarak tanımlanır.
Günümüzde arazilere parçalı ve dağınık haldedirler. Bu da tarım yapmayı zor ve maliyetli hale getirmektedir. Bu sorunların önüne geçmek için Arazi Toplulaştırma (AT) çalışmaları yapılmaktadır. AT aşamalardan biri olan dağıtım aşaması da çok amaçlı optimizasyon problemi olarak tanımlanabilir.
Bu çalışmada dağıtım problemine çok amaçlı optimizasyon tekniklerinden biri olan NSGA-II algoritması uygulanmıştır. Literatürde yapılan çalışmalar ile kıyaslanabilir sonuçlar elde edilmiştir.

References

  • Agarwal, A., & Nanavati, N. (2016). Association rule mining using hybrid GA-PSO for multi-objective optimisation. Paper presented at the 2016 IEEE International Conference on Computational Intelligence and Computing Research (ICCIC).
  • Alikar, N., Mousavi, S. M., Ghazilla, R. A. R., Tavana, M., & Olugu, E. U. (2017). Application of the NSGA-II algorithm to a multi-period inventory-redundancy allocation problem in a series-parallel system. Reliability Engineering & System Safety, 160, 1-10.
  • Bandyopadhyay, S., & Bhattacharya, R. (2013). Applying modified NSGA-II for bi-objective supply chain problem. Journal of Intelligent Manufacturing, 24(4), 707-716.
  • Bhattacharya, R., & Bandyopadhyay, S. (2010). Solving conflicting bi-objective facility location problem by NSGA II evolutionary algorithm. The International Journal of Advanced Manufacturing Technology, 51(1-4), 397-414.
  • Deb, K. (2014). Multi-objective optimization. In Search methodologies (pp. 403-449): Springer.
  • Deb, K., Agrawal, S., Pratap, A., & Meyarivan, T. (2000). A fast elitist non-dominated sorting genetic algorithm for multi-objective optimization: NSGA-II. Paper presented at the International conference on parallel problem solving from nature.
  • Deb, K., Pratap, A., Agarwal, S., & Meyarivan, T. (2002). A fast and elitist multiobjective genetic algorithm: NSGA-II. IEEE transactions on Evolutionary Computation, 6(2), 182-197.
  • Gao, X., Chen, B., He, X., Qiu, T., Li, J., Wang, C., & Zhang, L. (2008). Multi-objective optimization for the periodic operation of the naphtha pyrolysis process using a new parallel hybrid algorithm combining NSGA-II with SQP. Computers & Chemical Engineering, 32(11), 2801-2811.
  • Girgin, İ. (1982). Arazi Toplulaştırmasında En Uygun Parsel Dağılım Deseninin Saptanması Üzerine Bir araştırma. Doçentlik Tezi, AÜ Ziraat Fakültesi (Yayınlanmamış), Ankara.
  • Haklı, H. (2017). Arazi toplulaştırma için optimizasyon tabanlı yeni bir dağıtım ve parselasyon modelinin geliştirilmesi. Selçuk Üniversitesi Fen Bilimleri Enstitüsü,
  • Hughes, E. J. (2003). Multiple single objective Pareto sampling. Paper presented at the The 2003 Congress on Evolutionary Computation, 2003. CEC'03.
  • Javanshir, H., Ebrahimnejad, S., & Nouri, S. (2012). Bi-objective supply chain problem using MOPSO and NSGA-II. International Journal of Industrial Engineering Computations, 3(4), 681-694.
  • Jemai, J., Zekri, M., & Mellouli, K. (2012). An NSGA-II algorithm for the green vehicle routing problem. Paper presented at the European Conference on Evolutionary Computation in Combinatorial Optimization.
  • Konak, A., Coit, D. W., & Smith, A. E. (2006). Multi-objective optimization using genetic algorithms: A tutorial. Reliability Engineering & System Safety, 91(9), 992-1007.
  • Murata, T., & Ishibuchi, H. (1995). MOGA: Multi-objective genetic algorithms. Paper presented at the IEEE international conference on evolutionary computation.
  • Ozsarı , Ş. (2018). Arazi Toplulaştırma Optimizasyon Tabanlı Mülakat Selçuk Üniversitesi,
  • rey Horn, J., Nafpliotis, N., & Goldberg, D. E. (1994). A niched Pareto genetic algorithm for multiobjective optimization. Paper presented at the Proceedings of the first IEEE conference on evolutionary computation, IEEE world congress on computational intelligence.
  • Sağ, T. (2008). Çok kriterli optimizasyon için genetik algoritma yaklaşımları. Selçuk Üniversitesi Fen Bilimleri Enstitüsü,
  • Sağ, T., & Çunkaş , M. (2009). Çok Amaçlı Genetik Algoritmalar İçin Bir Çevrimdışı Performans Değerlendirmesi. 5. Uluslararası İleri Teknolojiler Sempozyumu (IATS’09).
  • Schaffer, J. D. (1986). SOME EXPERIMENTS IN MACHINE LEARNING USING VECTOR EVALUATED GENETIC ALGORITHMS (ARTIFICIAL INTELLIGENCE, OPTIMIZATION, ADAPTATION, PATTERN RECOGNITION).
  • Takka, S. (1993). Arazi Toplulaştırma. Kültür Teknik Derneği Yayınları(1).
  • Zeng, S., Yao, S., Kang, L., & Liu, Y. (2005). An efficient multi-objective evolutionary algorithm: OMOEA-II. Paper presented at the International Conference on Evolutionary Multi-Criterion Optimization.
  • Zitzler, E., Laumanns, M., & Thiele, L. (2001). SPEA2: Improving the strength Pareto evolutionary algorithm. TIK-report, 103.

NSGA-II Algorithm For The Reallocation Problem In Land Consolidation

Year 2020, Ejosat Special Issue 2020 (ARACONF), 412 - 417, 01.04.2020
https://doi.org/10.31590/ejosat.araconf54

Abstract

To solve problems encountered in real life, we sometimes need optimization algorithms. Some of these problems have single objective, while others have multiple objectives. If there is a single objective, the problem is defined as a single-objective optimization problem and if there are more than one objective it is called multi-objective optimization problem.
Today, lands are fragmented and scattered. This makes agriculture difficult and costly. To prevent these problems, Land Consolidation (LC) studies are being carried out. The reallocation stage, which is part of LC, can be defined as a multi objective optimization problem.
In this study, one of the multi objective optimization techniques, NSGA-II algorithm, is applied to the reallocation problem. The results are comparable with the studies in the literature.

References

  • Agarwal, A., & Nanavati, N. (2016). Association rule mining using hybrid GA-PSO for multi-objective optimisation. Paper presented at the 2016 IEEE International Conference on Computational Intelligence and Computing Research (ICCIC).
  • Alikar, N., Mousavi, S. M., Ghazilla, R. A. R., Tavana, M., & Olugu, E. U. (2017). Application of the NSGA-II algorithm to a multi-period inventory-redundancy allocation problem in a series-parallel system. Reliability Engineering & System Safety, 160, 1-10.
  • Bandyopadhyay, S., & Bhattacharya, R. (2013). Applying modified NSGA-II for bi-objective supply chain problem. Journal of Intelligent Manufacturing, 24(4), 707-716.
  • Bhattacharya, R., & Bandyopadhyay, S. (2010). Solving conflicting bi-objective facility location problem by NSGA II evolutionary algorithm. The International Journal of Advanced Manufacturing Technology, 51(1-4), 397-414.
  • Deb, K. (2014). Multi-objective optimization. In Search methodologies (pp. 403-449): Springer.
  • Deb, K., Agrawal, S., Pratap, A., & Meyarivan, T. (2000). A fast elitist non-dominated sorting genetic algorithm for multi-objective optimization: NSGA-II. Paper presented at the International conference on parallel problem solving from nature.
  • Deb, K., Pratap, A., Agarwal, S., & Meyarivan, T. (2002). A fast and elitist multiobjective genetic algorithm: NSGA-II. IEEE transactions on Evolutionary Computation, 6(2), 182-197.
  • Gao, X., Chen, B., He, X., Qiu, T., Li, J., Wang, C., & Zhang, L. (2008). Multi-objective optimization for the periodic operation of the naphtha pyrolysis process using a new parallel hybrid algorithm combining NSGA-II with SQP. Computers & Chemical Engineering, 32(11), 2801-2811.
  • Girgin, İ. (1982). Arazi Toplulaştırmasında En Uygun Parsel Dağılım Deseninin Saptanması Üzerine Bir araştırma. Doçentlik Tezi, AÜ Ziraat Fakültesi (Yayınlanmamış), Ankara.
  • Haklı, H. (2017). Arazi toplulaştırma için optimizasyon tabanlı yeni bir dağıtım ve parselasyon modelinin geliştirilmesi. Selçuk Üniversitesi Fen Bilimleri Enstitüsü,
  • Hughes, E. J. (2003). Multiple single objective Pareto sampling. Paper presented at the The 2003 Congress on Evolutionary Computation, 2003. CEC'03.
  • Javanshir, H., Ebrahimnejad, S., & Nouri, S. (2012). Bi-objective supply chain problem using MOPSO and NSGA-II. International Journal of Industrial Engineering Computations, 3(4), 681-694.
  • Jemai, J., Zekri, M., & Mellouli, K. (2012). An NSGA-II algorithm for the green vehicle routing problem. Paper presented at the European Conference on Evolutionary Computation in Combinatorial Optimization.
  • Konak, A., Coit, D. W., & Smith, A. E. (2006). Multi-objective optimization using genetic algorithms: A tutorial. Reliability Engineering & System Safety, 91(9), 992-1007.
  • Murata, T., & Ishibuchi, H. (1995). MOGA: Multi-objective genetic algorithms. Paper presented at the IEEE international conference on evolutionary computation.
  • Ozsarı , Ş. (2018). Arazi Toplulaştırma Optimizasyon Tabanlı Mülakat Selçuk Üniversitesi,
  • rey Horn, J., Nafpliotis, N., & Goldberg, D. E. (1994). A niched Pareto genetic algorithm for multiobjective optimization. Paper presented at the Proceedings of the first IEEE conference on evolutionary computation, IEEE world congress on computational intelligence.
  • Sağ, T. (2008). Çok kriterli optimizasyon için genetik algoritma yaklaşımları. Selçuk Üniversitesi Fen Bilimleri Enstitüsü,
  • Sağ, T., & Çunkaş , M. (2009). Çok Amaçlı Genetik Algoritmalar İçin Bir Çevrimdışı Performans Değerlendirmesi. 5. Uluslararası İleri Teknolojiler Sempozyumu (IATS’09).
  • Schaffer, J. D. (1986). SOME EXPERIMENTS IN MACHINE LEARNING USING VECTOR EVALUATED GENETIC ALGORITHMS (ARTIFICIAL INTELLIGENCE, OPTIMIZATION, ADAPTATION, PATTERN RECOGNITION).
  • Takka, S. (1993). Arazi Toplulaştırma. Kültür Teknik Derneği Yayınları(1).
  • Zeng, S., Yao, S., Kang, L., & Liu, Y. (2005). An efficient multi-objective evolutionary algorithm: OMOEA-II. Paper presented at the International Conference on Evolutionary Multi-Criterion Optimization.
  • Zitzler, E., Laumanns, M., & Thiele, L. (2001). SPEA2: Improving the strength Pareto evolutionary algorithm. TIK-report, 103.
There are 23 citations in total.

Details

Primary Language English
Subjects Engineering
Journal Section Articles
Authors

Zeynep Ortaçağ This is me 0000-0003-3563-0435

Harun Uğuz This is me

Hüseyin Haklı This is me

Publication Date April 1, 2020
Published in Issue Year 2020 Ejosat Special Issue 2020 (ARACONF)

Cite

APA Ortaçağ, Z., Uğuz, H., & Haklı, H. (2020). NSGA-II Algorithm For The Reallocation Problem In Land Consolidation. Avrupa Bilim Ve Teknoloji Dergisi412-417. https://doi.org/10.31590/ejosat.araconf54