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Software Cost Estimation by a New Hybrid Model of Particle Swarm Optimization and K-Nearest Neighbor Algorithms | ||
| Journal of Electrical and Computer Engineering Innovations (JECEI) | ||
| مقاله 7، دوره 4، شماره 1 - شماره پیاپی 7، فروردین 2016، صفحه 49-55 اصل مقاله (997.53 K) | ||
| نوع مقاله: Original Research Paper | ||
| شناسه دیجیتال (DOI): 10.22061/jecei.2016.556 | ||
| نویسندگان | ||
| M. Hasanluo؛ F. Soleimanian Gharehchopogh* | ||
| Department of Computer Engineering, Urmia Branch, Islamic Azad University, Urmia, Iran | ||
| تاریخ دریافت: 26 مهر 1395، تاریخ بازنگری: 17 آذر 1395، تاریخ پذیرش: 21 آذر 1395 | ||
| چکیده | ||
| A successful software should be finalized with determined and predetermined cost and time. Software is a production which its approximate cost is expert workforce and professionals. The most important and approximate software cost estimation (SCE) is related to the trained workforce. Creative nature of software projects and its abstract nature make extremely cost and time of projects difficult to estimate. Various methods have been presented in the software project cost estimation for performing a software project in the area of software engineering. COCOMO II model is one of the most documented models among template-based methods that has been proposed by Bohm. Common methods for estimating the time and cost are essentially abstract, accordingly, providing new methods for SCE is required and necessary. In this paper, a new method is presented to solve the problem of SCE by using hybrid particle swarm optimization (PSO) algorithm and K-nearest neighbor (KNN) algorithm. The method was evaluated on 6 multiple datasets with 8 different evaluation criteria. Obtained results show the more accurate performance of the proposed method. | ||
| کلیدواژه‌ها | ||
| Software cost estimation؛ PSO؛ KNN؛ Hybrid Method؛ Optimization | ||
| مراجع | ||
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[3] F. S.Gharehchopogh, L. Ebrahimi, I. Maleki, and S. Jodati, “A new novel PSObased approach with hybrid of fuzzy c-means and learning automata in software cost estimation,” Indian Journal of Science and Technology, vol. 7, pp. 795-803, 2014.
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[7] F. S.Gharehchopogh, Z.A. Dizaji, “A new approach in software cost estimation with hybrid of bee colony and chaos optimizations algorithms,” MAGNT Research Report, vol. 2, no. 6, pp. 1263-127, 2014.
[8] Z.A.Khalifelu, F.S.Gharehchopogh,“Comparison and evaluation data mining techniques with algorithmic models in software cost estimation,” Procedia-Technology Journal, vol. 1, pp. 65- 71, 2012.
[9] Z.A. Dizaji, F. S.Gharehchopogh, “A hybrid of ant colony optimization and chaos optimization algorithms approach for software cost estimation,” Indian Journal of Science and Technology, vol. 8, no.2, pp.128–133,2015.
[10] E.E. Miandoab, F.S. GHAREHCHOPOGH, “A novel hybrid algorithm for software cost estimation based on cuckoo optimization and k-nearest neighbors algorithms,” Engineering, Technology & Applied Science Research, vol. 6, no. 3, pp. 1018-1022, 2016.
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[15] F.D. Mokri, M. Molanli, “software cost estimation using adaptive neuro fuzzy inference system,” International Journal of Academic Research in Computer Engineering, vol. 1, no. 1, pp. 34-39, 2016.
[16] F. S.Gharehchopogh, S. Jodati, and I. Maleki, “object oriented software engineering models in software industry,” International Journal of Computer Applications, vol. 95,no.3, pp. 13-16, 2014. | ||
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