Implementation of Genetic Algorithm Using the Traveling Salesman Problem in Cloud

Authors

  • Aadil Bashir  M.Tech (Scholar), CSE Depatment Desh Bhagat University, Mandi Gobindgarh, Punjab, India
  • Manoj Kumar Srivastava  CSE Department, Desh Bhagat University, Mandi Gobindgarh, Punjab, India

Keywords:

NP-complete, problem of a travelling salesman, an algorithm based on genetics, Cross-web-program sequencing and constructive.

Abstract

The paper below is utilised to create a novel cross-operator (SCX) for an algorithm which creates premium solutions for problem of travelling salesmen (TSP). In cross-operative sequential and constructive operator method it creates a new offspring from a parent with increased constraints depending on its standards, which may be found in the composition of parents while maintaining the parent chromosomes' node order. The Internet connects the entire world. Artificial intelligence (AI) is in high demand, thanks to the large number of web users and the growing popularity of cloud computing research. Through natural selection and genetic development, genetic algorithms (GA) are applied as an AI optimisation technique in this study. There are numerous GA applications, such as web mining, load balancing, routing and planning, and online service selection. As a result, determining whether code has a significant impact on GA server speed and web-based language technology is a difficult undertaking. The Travel Salesman (TSP) specified as a Non Polynomial-Hard difficulty with the aim can be solved with GA (NP-hard). Although many academics use Python to implement GA, other popular high-level programming languages for interpreters, such as PHP, are also often used (PHP Hypertext Preprocessor). Different programming languages had different line of GA implementation and runtime codes, file sizes, and performance. The use of Python in GA implementation is suggested based on the findings.

References

  1. Fozia Hanif Khan, Nasiruddin Khan, Syed Inayatullah and Shaikh Tajuddin Nizami‘SOLVING TSP PROBLEM BY USING GENETIC ALGORITHM’- International Journal of Basic & Applied Sciences IJBAS Vol: 9 No: 10.
  2. IEEE paper by Mahdieh Poostchi on “A new Approach to Solve Traveling Salesman Problem Using Genetic Algorithm”
  3. Crossover(Genetic Algorithm), http://www.wikipedia.org/genetic_algorithms.
  4. Zakir H. Ahmed ‘Genetic Algorithm for the Traveling Salesman Problem using Sequential Constructive Crossover Operator’- International Journal of Biometrics & Bioinformatics (IJBB) Volume (3): Issue (6).
  5. Richard Johnsonbaugh , Marcus Schaefer, “Algorithms“, Pearson Education, 2006 3rd edition.
  6. Wikipedia: http://en.wikipedia.org/.
  7. Genetic algorithms - A business perspective: Fritz H. Grupe and Simon Jooste (University of Nevada, Reno, Nevada, USA); Journal of Information Management & Computer Security.
  8. http://lancet.mit.edu/.
  9. http://www.iba.k.u-tokyo.ac.jp/ : Graduate School of Frontier Sciences, The University of Tokyo.
  10. Practical Handbook of Genetic Algorithm Complex Coding System by Lance D Chambers.
  11. H. Koepke. "10 reasons python rocks for research (and a few reasons it doesn’t), " 2010.
  12. Jafar, Anderson, and Abdullat. "Comparison of dynamic web content processing language performance under a LAMP architecture," West Texas A&M University Canyon, 2008.
  13. W. Wang, B. Li, and B. Liang, “Dominant resource fairness in cloud computing systems with heterogeneous servers,” in Proceedings of the IEEE Conference on Computer Communications (INFOCOM ’14), pp. 583–591, IEEE, Toronto, Canada, May 2014.
  14. J. Guo, F. Liu, J. C. S. Lui, and H. Jin, “Fair network bandwidth allocation in IaaS datacenters via a cooperative game approach,” IEEE/ACM Transactions on Networking, vol. 24, no. 2, pp. 873–886, 2016.
  15. D. Lo, L. Cheng, R. Govindaraju, P. Ranganathan, and C. Kozyrakis, “Improving resource efficiency at scale with heracles,” ACM Transactions on Computer Systems, vol. 34, no. 2, 2016.
  16. S. Singh and I. Chana, “QoS-aware autonomic resource management in cloud computing: a systematic review,” ACM Computing Surveys, vol. 48, no. 3, article 42, 2016.
  17. K. H. Park, W. Hwang, H. Seok et al., “MN-MATE: elastic resource management of manycores and a hybrid memory hierarchy for a cloud node,” ACM Journal on Emerging Technologies in Computing Systems, vol. 12, no. 1, article 5, 2015.
  18. R. C. Chiang, S. Rajasekaran, N. Zhang, and H. H. Huang, “Swiper: exploiting virtual machine vulnerability in third-party clouds with competition for I/O resources,” IEEE Transactions on Parallel and Distributed Systems, vol. 26, no. 6, pp. 1732–1742, 2015.
  19. N. Jain, I. Menache, J. Naor, and J. Yaniv, “Near-optimal scheduling mechanisms for deadline-sensitive jobs in large computing clusters,” ACM Transactions on Parallel Computing, vol. 2, no. 1, 2015.
  20. J. Ghaderi, S. Shakkottai, and R. Srikant, “Scheduling storms and streams in the cloud,” ACM Transactions on Modeling and Performance Evaluation of Computing Systems, vol. 1, no. 4, 2016.
  21. T. Wu, W. Dou, F. Wu, S. Tang, C. Hu, and J. Chen, “A deployment optimization scheme over multimedia big data for large-scale media streaming application,” ACM Transactions on Multimedia Computing, Communications, and Applications, vol.12, no. 5, article 73, 2016.
  22. J. Xu, C. Liu, X. Zhao, S. Yongchareon, and Z. Ding, “Resource management for business process scheduling in the presence of availability constraints,” ACM Transactions on Management Information Systems, vol. 7, no. 3, article 9, 2016
  23. L. Zhang, Z. Li, and C. Wu, “Dynamic resource provisioning in cloud computing: a randomized auction approach,” in Proceedings of the 33rd IEEE Conference on Computer Communications (’INFOCOM ’14), pp. 433–441, Ontario, Canada, May 2014.
  24. Z. Zhou, F. Liu, Z. Li, and H. Jin, “When smart grid meets geodistributed cloud: an auction approach to datacenter demand response,” in Proceedings of the IEEE Conference on Computer Communications (INFOCOM ’15), pp. 2650–2658, IEEE, May 2015.
  25. Python. http://www.python.org.
  26. PHP. http://www.php.net
  27. D. Dunlop, S. Varrette, and Pascal. "On the use of a genetic algorithm in high performance computer benchmark tuning," University of Luxem- bourg, 2008.
  28. A. G. Najera. "TSP: three evolutionary approaches vs. local search," University of Birmingham, 2009
  29. P. J. Guo and D. Engler. "Toward practical incremental recomputation for scientists: an implementation for python language, " Stanford Uni- versity, 2010.
  30. S. Branigan. "Risk with web programming technologies, " Lucent Tech- nologies, 2000.

IInternational Journal of Scientific Research in Computer Science, Engineering and Information Technology

Downloads

Published

2021-08-30

Issue

Volume 7, Issue 4, July-August-2021

Section

Research Articles

License

Copyright (c) IJSRCSEIT

Creative Commons License

This work is licensed under a Creative Commons Attribution 4.0 International License.

How to Cite

[1]
Aadil Bashir, Manoj Kumar Srivastava, " Implementation of Genetic Algorithm Using the Traveling Salesman Problem in Cloud " International Journal of Scientific Research in Computer Science, Engineering and Information Technology(IJSRCSEIT), ISSN : 2456-3307, Volume 7, Issue 4, pp.350-358, July-August-2021.