Call for Paper - August 2020 Edition
IJCA solicits original research papers for the August 2020 Edition. Last date of manuscript submission is July 20, 2020. Read More

A Comparative Analysis of Optimization Techniques

International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Year of Publication: 2015
Kanika Tyagi, Kirti Tyagi

Kanika Tyagi and Kirti Tyagi. Article: A Comparative Analysis of Optimization Techniques. International Journal of Computer Applications 131(10):6-12, December 2015. Published by Foundation of Computer Science (FCS), NY, USA. BibTeX

	author = {Kanika Tyagi and Kirti Tyagi},
	title = {Article: A Comparative Analysis of Optimization Techniques},
	journal = {International Journal of Computer Applications},
	year = {2015},
	volume = {131},
	number = {10},
	pages = {6-12},
	month = {December},
	note = {Published by Foundation of Computer Science (FCS), NY, USA}


Regression testing is an inescapable and very expensive task to be performed, often in a resource and time constrained environment. The goal is to minimize the time spent in the process of testing by reduction in the number of test cases to be used. Thus various techniques are being used for test case optimization, to select the less indistinguishable test cases while providing the best possible fault coverage. This paper presents a comparative analysis of the different test case optimization techniques. There are various optimization techniques available for the context. This review explains about the different optimization techniques on the basis of their evolution, methodology, performance and applications.


  1. K.K.Aggarwal & Yogesh Singh, “Software Engineering Programs Documentation, Operating Procedures,” New Age International Publishers, Revised Second Edition – 2005.
  2. Chen, Jun, and Mahdi Mahfouf. "Artificial immune systems as a bio-inspired optimization technique and its engineering applications." Handbook of Research on Artificial Immune Systems and Natural Computing: Applying Complex Adaptive Technologies: Applying Complex Adaptive Technologies (2009): 22.
  3. Philip T Cox and Baoming Song, “A formal Model for Component-Based Software”, IEEE Computer Society, Document number 07695-474-4/01, 2001, pp.304-310.
  4. Karaboga, Dervis. An idea based on honey bee swarm for numerical optimization. Vol. 200. Technical report-tr06, Erciyes university, engineering faculty, computer engineering department, 2005.
  5. Alaya, I., C. Solnon, and K. Ghedira. "Ant Colony Optimization for Multi-Objective Optimization Problems." In Tools with Artificial Intelligence, 2007. ICTAI 2007. 19th IEEE International Conference on, vol. 1, pp. 450-457. IEEE, 2007.
  6. Kulkarni, Nandakishore J., K. Venkat Naveen, Puneet Singh, and Praveen Ranjan Srivastava. "Test Case Optimization Using Artificial Bee Colony Algorithm." Advances in Computing and Communications (2011): 570-579.
  7. Karaboga, Dervis, and Bahriye Akay. "A comparative study of artificial bee colony algorithm." Applied Mathematics and Computation 214, no. 1 (2009): 108-132.
  8. Zhu, Guopu, and Sam Kwong. "Gbest-guided artificial bee colony algorithm for numerical function optimization." Applied Mathematics and Computation 217, no. 7 (2010): 3166-3173.
  9. Karaboga, Dervis, and Beyza Gorkemli. "A combinatorial artificial bee colony algorithm for traveling salesman problem." In Innovations in Intelligent Systems and Applications (INISTA), 2011 International Symposium on, pp. 50-53. IEEE, 2011.
  10. Li, Jun-Qing, Quan-Ke Pan, and Kai-Zhou Gao. "Pareto-based discrete artificial bee colony algorithm for multi-objective flexible job shop scheduling problems."The International Journal of Advanced Manufacturing Technology 55, no. 9-12 (2011): 1159-1169.
  11. Li, Junqing, Quanke Pan, and Shengxian Xie. "Flexible job shop scheduling problems by a hybrid artificial bee colony algorithm." In Evolutionary Computation (CEC), 2011 IEEE Congress on, pp. 78-83. IEEE, 2011.
  12. Karaboga, Dervis, and Celal Ozturk. "Fuzzy clustering with artificial bee colony algorithm." Scientific research and Essays 5, no. 14 (2010): 1899-1902.
  13. Akay, Bahriye, and Dervis Karaboga. "Solving integer programming problems by using artificial bee colony algorithm." In AI* IA 2009: Emergent Perspectives in Artificial Intelligence, pp. 355-364. Springer Berlin Heidelberg, 2009.
  14. Jiao, Jian, Shan Yao, and Chunehe Xia. "Application for artificial bee colony algorithm in migration of mobile agent." In Advanced Intelligent Computing Theories and Applications, pp. 232-238. Springer Berlin Heidelberg, 2010.
  15. C. Vargas Ben´ıtez and H. Lopes. Parallel artificial bee colony algorithm approaches for protein structure prediction using the 3dhp-sc model. Intelligent Distributed Computing IV, pages 255-264, 2010.
  16. J. Kennedy and R. C. Eberhart. Particle swarm optimization. In Proceedings of the IEEE International Conference on Neural Networks, volume 4, pages 1942–1948 vol.4. IEEE Press, 1995.
  17. Windisch, Andreas, Stefan Wappler, and Joachim Wegener. "Applying particle swarm optimization to software testing." In Proceedings of the 9th annual conference on Genetic and evolutionary computation, pp. 1121-1128. ACM, 2007.
  18. Hemlata S Urade and Prof. Rahila Patel. Article: Study and Analysis of Particle Swarm Optimization: A Review. IJCA Proceedings on 2nd National Conference on Information and Communication Technology NCICT(4):1-5, November 2011.
  19. van den Bergh, Frans, and A. P. Engelbrecht. "A new locally convergent particle swarm optimizer." In Proceedings of the IEEE international conference on systems, man, and cybernetics, vol. 7, pp. 6-9. 2002.
  20. Parsopoulos, Konstantinos E., and Michael N. Vrahatis. "Particle swarm optimization method in multiobjective problems." In Proceedings of the 2002 ACM symposium on Applied computing, pp. 603-607. ACM, 2002.
  21. Ahmet, Bestoun, and Kamal Zamli. "A Greedy Particle Swarm Optimization Strategy for T-way Software Testing." Journal of Artificial Intelligence 5.2 (2012): 85-90.
  22. Laskari, E. C., K. E. Parsopoulos, and M. N. Vrahatis. "Particle swarm optimization for minimax problems." In Evolutionary Computation, 2002. CEC'02. Proceedings of the 2002 Congress on, vol. 2, pp. 1576-1581. IEEE, 2002.
  23. Dorigo, Marco, and Luca Maria Gambardella. "Ant colony system: a cooperative learning approach to the traveling salesman problem." Evolutionary Computation, IEEE Transactions on 1, no. 1 (1997): 53-66.
  24. Bullnheimer B, Hartl RF, Strauß C ” A new rank based version of the ant system - a computational study.” Central European Journal for Operations Research and Economics 7(1997): 25–38.
  25. Dorigo M, Di Caro G,” The ant colony optimization meta-heuristic”, McGraw-Hill Ltd., UK, Maidenhead, UK, England, (1999) pp 11–32.
  26. Hong Liu, Ping Li and Yu Wen, “Parallel Ant colony optimization algorithm,” in Proc. WCICA, 2006, pp 3222-3226.
  27. Maniezzo, Vittorio, and Alberto Colorni. "The ant system applied to the quadratic assignment problem." Knowledge and Data Engineering, IEEE Transactions on 11.5 (1999): 769-778.
  28. Colorni, Alberto, et al. "Ant system for job-shop scheduling." Belgian Journal of Operations Research, Statistics and Computer Science 34.1 (1994): 39-53.
  29. R. Schoonderwoerd, O. Holland, J. Bruten et L. Rothkrantz, Ant-based load balancing in telecommunication networks, Adaptive Behaviour, volume 5, numéro 2, pages 169-207, 1997.
  30. Bullnheimer, Bernd, Richard F. Hartl, and Christine Strauss. "An improved ant System algorithm for thevehicle Routing Problem." Annals of operations research 89 (1999): 319-328.
  31. Holland, J.H., “Adaptation in natural and artificial systems”, The university of Michigan press, 1975.
  32. Davis, L. (1991): Handbook of Genetic Algorithms. Van Nostrand Reinhold. New York, NY.
  33. Zhang, J., Lo, W.L., and Chung, H., "Pseudocoevolutionary Genetic Algorithms for Power Electronic Circuits Optimization", IEEE Trans Systems, Man, and Cybernetics, Part C., Vol.36, No.4, July 2006, pp. 590–598.
  34. Minglun G. and Yee-Hong Y., “Multi-resolution Stereo Matching using Genetic Algorithm”, Stereo and Multi-Baseline Vision, 2001. (SMBV 2001). Proceedings. IEEE Workshop on, pp 21 –29, 2001.
  35. Madureira, A.; Ramos, C.; do Carmo Silva, S.,”A Coordination Mechanism for Real World Scheduling Problems using Genetic algorithms”, Evolutionary Computation, 2002. CEC ’02. Proceedings of the 2002Congress on, 1, pp 175 –180, 2002.
  36. Krause, Jonas, Jelson Cordeiro, Rafael Stubs Parpinelli, and Heitor Silverio Lopes. "A survey of swarm algorithms applied to discrete optimization problems." Swarm Intelligence and Bio-inspired Computation: Theory and Applications. Elsevier Science & Technology Books (2013): 169-191.


Optimization techniques, evolution, applications, regression testing.