IMPROVEMENT OF TRANSPORTATION ROUTES IN MUNICIPALITIES BY DNA COMPUTATION METHOD

Year 2020, Volume: 16 Issue: 4, 924 - 937, 31.12.2020

Salih Serkan Kaleli Mehmet Baygın Abdullah Naralan

https://doi.org/10.17130/ijmeb.853543

Abstract

Today, municipalities generally provide faster and better quality and more regular transportation
to the people living in those cities with the public transportation services provided in the metropolitan
cities compared to the small cities. In addition, the public transport services offered to the people by the
municipalities are constantly updated with the current road conditions and changing road routes in those
cities and are developing accordingly. In this study, a bus route actively used by Erzurum Metropolitan
Municipality was tried to be optimized and the results were shared. For this purpose, the actual data
obtained were modeled using the traveliang salesman problem, which is a type of optimization problem,
and the current route used using the DNA Computation Algorithm was tried to be shortened.

Keywords

Optimization, Shortest Path, DNA Computing Algorithm

References

• Adleman, L. M. (1994). Molecular computation of solutions to combinational problems. Science, 266(4), 1021-1025.
• Baygin, M., & Karakose, M. (2013). Immunity-based optimal estimation approach for a new real time group elevator dynamic control application for energy and time saving. The Scientific World Journal, 2013(2013), 1-12.
• Chen, J., Antipov, E., Lemieux, B., Cedeno, W., & Wood, D. H. (1999). DNA computing implementing genetic algorithms, Proceeding of DIMACS Workshop on Evolution as Computation, Princeton, NJ, USA, 39-49.
• Çiğdem, U., & Karaköse, M. (2013). Polinomal olmayan problemler için DNA hesaplama algoritması. Erciyes Üniversitesi Fen Bilimleri Enstitüsü Dergisi, 29(1), 41-48.
• Forbes, N. (2000). Biological inspired computing. Computing in Science & Engineering, 2(6), 83–87.
• Kara, İ., Güden, H., & Koç, Ö. N. (2011). Genelleştirilmiş Gezgin Satici Problemi için Polinom Büyüklükte Yeni Karar Modelleri. XI. Üretim Araştırmaları Sempozyumu, 23-24 Haziran, İstanbul, 800–811.
• Polynomial, N. E. W., Mathematical, S., For, M., Generilized, T. H. E., & Problem, S. (2006).
• Genelleştirilmiş Gezgin Satici Problemi için Polinom Büyüklükte Yeni Karar Modelleri. 800– 811.
• Karakose, M., & Cigdem, U. (2013). QPSO-based adaptive DNA computing algorithm. The Scientific World Journal, 2013(2013), 1-8.
• Kuzu, S. (2014). Gezgin satıcı problemlerinin metasezgiseller ile çözümü. İstanbul Üniversitesi İşletme Fakültesi Dergisi, 43(1), 1–27.
• Lipton, R. J. (1995). DNA solution of hard computational problems. Science, 268(5210), 542–545.
• Maley, C. C. (1998). DNA computation: Theory, practice, and prospects. Evolutionary Computation, 6(3), 201-229.
• Muhammad, M. S., Ibrahim, Z., Ono, O., & Khalid, M. (2005). Direct-proportional length-based DNA computing implementation for elevator scheduling problem. IEEE Region 10 Annual International Conference, Proceedings/TENCON, 2007 Melbourne, Australia.
• Naralan, A., Kaleli, S. S., & Baygin, M. (2017). Shortest path detection using clonal selection algorithm for Erzurum Metropolitan Municipality. Muğla Journal of Science and Technology, 3(2), 138– 142.
• Ouyang, Q., Kaplan, P. D., Liu, S., & Libchaber, A. (1997). DNA solution of the maximal clique problem. Science, 278(5337), 446–449.
• Özkır, V., & Topçu, B. (2018). Application of the random key based electromagnetism-like heuristic for solving travelling salesman problems. Pamukkale University Journal of Engineering Sciences, 24(1), 76–82.
• Wood, D., Chen, J., Antipov, E., Lemieux, B., & Cedeño, W. (1999). A {DNA} Implementation of the Max 1s Problem. GECCO-99: Proceedings of the Genetic and Evolutionary Computation Conference, 2, 1835–1841.
• Wood, D., Chen, J., Antipov, E., Lemieux, B., & Cedeño, W. (1999). A {DNA} Implementation of the Max 1s Problem. Proc. of the {G}enetic and {E}volutionary {C}omputation {C}onf. {GECCO}-99, 1835–1841.
• Yoshikawa, T., Furuhashi, T., & Uchikawa, Y. (1996). DNA coding method and a mechanism of development for acquisition of fuzzy control rules. Proceedings of IEEE 5th International Fuzzy Systems, September 8-11 New Orleans, LA, USA, 2194-2200.
• Yoshikawa, T., Furuhashi, T., & Uchikawa, Y. (n.d.). Tomohiro Yoshikawa, Takeshi Furuhashi, Yoshiki Uchikawa. 2194–2200.