Firdews Alsalman
ABSTRACT
Estimation is one of the most important factors in building and designing software. This research to provide a study of the principles of reducing the cost of software and understanding how to apply these techniques to the general programs. The swarm intelligence algorithms have been used because they have community-based methods where there is a range of potential solutions in this community. The ideal solution is sought through repetitive steps, these methods are based on solving complex problems by simple factors without central control or a comprehensive model and possess distributed control, the most important characteristics of the swarm intelligence, It will produce a self-contained, adaptive, scalable, flexible, powerful, parallel, self-regulatory and cost-effective system.
The traditional cat algorithm has been developed by assuming that the flavour is a location within the coordinates (X, Y) to reduce the search time and to find the optimal solution. The results obtained from the implementation of the tool display the adoption of (NASA) data Excellence than the developed cats swarm optimization (DCSO) algorithm, In terms of shortening the number of search cycles and the time required to reach the best solution, The best rate was the error(0.25), by fixing the search ratio and the highest fitness value for the number of successful cat copies and increasing the number of repetitions of implementation and the stability of the search site and speed. The tool built Swarm Effort Estimation Tool --SEET Using a language (Matlab 2017) The tool is described based on (Enterprise Architect 9).
- Abdulalqader, A., Firdews, Aseel W.Ali, (2018)," Comparing Different Estimation Methods for Software effort", In Software Engineering, College of Computer Sciences and Mathematics, University of Mosul, 1ST Annual International Conference on Information and Sciences NOV. 20TH - 21TH, 2018 FALLUJAH / IRAQIEEE Record # 46152.Google Scholar
- Abraham A., Grosan C., Ramos V., (2006), "Swarm Intelligence in Data Mining", Springer-Verlag Berlin Heidelberg, 101--123.Google ScholarCross Ref
- Ahmed S. Moussa, (2003)," The Implementation of Intelligent OoS Networking by the Development and Utilization of Novel Cross-Disciplinary Soft Computing Theories- and Techniques", Dissertations in Doctor of Philosophy, Florida State University, College of Arts and Sciences.Google Scholar
- Alrefaee, R, Taghreed,(2017) "Software Effort Estimation using Evolutionary Computation", MASTER'S THESIS, In Software Engineering, College of Computer Sciences and Mathematics, University of Mosul.Google Scholar
- Al-Taie M. Yahya, (2018), "Effort Estimation in Software Engineering Projects Using Artificial Intelligence Techniques", MASTER'S THESIS, In Software Engineering, College of Computer Sciences and Mathematics, University of Mosul.Google Scholar
- Blum C. and Merkle D. eds, (2008), Swarm Intelligence -- Introduction and Applications. Natural Computing. Springer, Berlin.Google Scholar
- Buglione, L., Ebert, Christof.,(2011), "Estimation Tools and Techniques", IEEE Software, ISSN:0740-7459, PP. 15--18.Google Scholar
- Engelbrecht A. P., (2007), "Computational Intelligence An Introduction", Second Edition, John Wiley & Sons Ltd, West Sussex, England.Google ScholarCross Ref
- Ganapati Panda, (2009), "Cat Swarm Optimization: Theory and Application to Direct and Inverse Modeling", School of Electrical Sciences, Indian Institute of Technology Bhubaneswar.Google Scholar
- Guillermo, S., Donatti, (2005), "Software Development Effort Estimations Through Neural Networks", Faculty of Mathematics, Astronomy and Physics Cordoba National University.Google Scholar
- K.Geetha, K. Thanushkodi, (2008), "Particle Swarm Optimization for Automatic Detection of Breast Cancer", International Journal of soft computing 3(2):155--158.Google Scholar
- Mahdi Bahrami, Omid B., Haddad and Xuefeng Chu (2016) "Cat Swarm Optimization (CSO) Algorithm" Springer Nature Singapore Pte Ltd.Google Scholar
- Majumder, P., & Eldho, T. I. (2016). A new groundwater management model by coupling the analytic element method and reverse particle tracking with cat swarm optimization. WaterGoogle Scholar
- Maysam Orouskhani, Mohammad Mansouri, Mohammad Teshnehlab, (2011)," Average-Inertia Weighted Cat Swarm Optimization", Springer-Verlag Berlin Heidelberg.Google ScholarCross Ref
- Mohanty. S.K., Bisoi. A.K., (2012), "Software Effort Estimation Approaches A Review", International Journal.Google Scholar
- Mohapatra, P., Chakravarty, S., & Dash, P. K. (2016). Microarray medical data classification using kernel ridge regression and modified cat swarm optimization based gene selection system. Swarm and Evolutionary Computation, 28, 144--160.Google ScholarCross Ref
- Panigrahi B. K., Shi, Y., and Lim, eds, M., (2011), "Handbook of Swarm Intelligence". Series: Adaptation, Learning, and Optimization, Vol 7, Springer-Verlag Berlin Heidelberg. ISBN 978-3-642-17389-9.Google Scholar
- Quba, E. Zuhair, (2012), "Software Projects Estimation using Artificial intelligent Approaches", MASTER'S THESIS, In Software Engineering, College of Computer Sciences and Mathematics, University of Mosul.Google Scholar
- Roger S. Pressman, (2010), "Software Engineering A Practitioner's Approach, Seventh Edition", 7th, McGraw-Hill Company.Google Scholar
- Selvakumar, K., Vijayakumar, K., and C.S. Boopathi (2017)," CSO Based Solution for Load Kickback Effect in Deregulated Power Systems", IN SRM University, Department of Electrical and Electronics Engineering, India.Google ScholarCross Ref
- Web page: Asproni, Giovanni, (2008), "Fingers in the air: a Gentle Introduction to Software Estimation", Methods & Tools, http://www.methodsandtools.com/archive/archive.phpid=79Google Scholar
- Xiaohui Cui, Thomas E. Potok, (2006), "Swarm Intelligence in Text Document Clustering", Computational Sciences and Engineering Division Oak Ridge National Laboratory.Google Scholar
- Xu G., Zhang Z. and Li L., (2011)," Web Mining and Social Networking", Web Information Systems Engineering and Internet Technologies, Springer Science Business Media, Australia.Google Scholar
- Yoon-Teck Bau, Hong-Tat Ewe, Chin-Kuan Ho, (2012), "Particle Swarm Optimization Algorithms to Continuous Problem", Faculty of Information Technology-Multimedia University, Malaysia.Google Scholar
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