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A Hybrid of Ant Colony Optimization and Chaos Optimization Algorithms Approach for Software Cost Estimation


  • Department of Computer Engineering, Science and Research Branch, Islamic Azad University, West Azerbaijan, Iran, Islamic Republic of


The main challenge in the production and development of large and complex software projects is the cost estimation with high precision. Thus it can be said that estimating the cost of software projects play an important role in the organization productivity. With the increasing size and complexity of software projects the demand to offer new techniques to accomplish this important task increases day by day. Therefore, researchers have long attempted to provide models to fulfill this important task. The most documented algorithmic model is the Constructive Cost Model (COCOMO), which was introduced in 1981 by Barry W. Boehm. But due to the lack of values for the constant parameters in this model, it cannot meet the high precision for all software projects.

Nowadays, regarding the increasing researches on machine learning algorithms and the success of these studies, in this paper, we have tried to estimate the cost of software projects according to meta-heuristic algorithms. In this paper, Ant Colony Optimization (ACO) and Lorentz transformation have been used as Chaos Optimization Algorithm (COA) and NASA datasets as training and testing sets.

To compare and evaluate the results of the proposed method with COCOMO model, MARE is used, and the results show a decline in MARE to 0.078%.


Ant Colony Optimization Algorithm, Chaos Optimization Algorithm, Meta-Heuristic Algorithms, Software Project Cost Estimation.

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  • Kim G-H, An S-H, Kang K-I. Comparison Of Construction Cost Estimating Models Based On Regression Analysis, Neural Networks, And Case-Based Reasoning. Build Environ. 2004 Oct; 39(10):1235-42.
  • Sharma A, Kushwaha DS. Estimation of Software Development Effort from Requirements Based Complexity. Procedia Technology. 2012; 4:716-22.
  • Boehm BW. Software Engineering Economics. Prentice- Hall; 1981.
  • Park H, Baek S. An Empirical Validation of a Neural Network Model for Software Effort Estimation. Expert Syst Appl. 2008; 35(3):929-37.
  • Leungh, Zhangf. Software cost estimation. Handbook of Software Engineering and Knowledge Engineering. World Scientific Pub Co; 2001.
  • Jones C. Estimating Software Costs. Tata Mc-Graw, Hill Edition; 2007.
  • Khatibi V, Jawawi .DNA Software Cost Estimation Methods: A Review. J Emerg Trends Comput Inform Sci. 2010-11; 2(1):21-9.
  • Kumari S, Pushkar S. Performance Analysis of the Software Cost Estimation Methods: A Review. Int J Adv Res Comput Sci Software Eng. 2013 Jul; 3(7):229-38.
  • Benala TR, DehuriS , Satapathy SC, Sudha Raghavi CH. Genetic Algorithm for Optimizing Neural Network Based Software Cost Estimation. Lecture Notes in Computer Science. 2011; 7076:233-9.
  • Mittal A, Parkash K, Mittal H. Software Cost Estimation Using Fuzzy Logic.ACM SIGSOFT Software Engineering Notes. 2010 Nov; 35(1):1-7.
  • Ziauddin N, Kamal S, Khan S, Abdul J. A Fuzzy Logic Based Software Cost Estimation Model. International Journal of Software Engineering and Its Applications. 2013 Mar; 7(2):7-18.
  • Mittal A, Parkash K, Mittal H. Software Cost Estimation Using Fuzzy Logic. ACM SIGSOFT Software Engineering. 2010 Nov; 35(1)1-7.
  • Dorigo M, Gambardella LM. Ant Colony System: A cooperative learning approach to the traveling salesman roblem. IEEE Trans Evol Comput. 1997; 1:53-66.
  • Deneubourg JL, Aron S, Goss S, Pasteels JM. The self-organizing exploratory pattern of the Argentine ant. Insect Behaviour. 1990; 3:159-164.
  • Peitgen H-O, Jurgens H, Saupe D. Chaos and Fractals. United States of America: Springer Science and Business Media; 2004.
  • Gharehchopogh FS, Dizaji ZA. A new chaos agent based approach in prediction of the road accidents with hybrid of pso optimization and chaos optimization algorithms: a case study. Int J Acad Res. 2014 Mar; 6(2):108.
  • Gharehchopogh FS, Maleki I, Khaze SR. A Novel Particle Swarm Optimization Approach for Software Effort Estimation. Int J Acad Res. 2014 Mar; 6(2):69.


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