Karınca Kolonisi ve Yapay Arı Kolonisi Algoritmaları ile Yazılım Proje Takvimi Oluşturma

Year 2018, Volume: 4 Issue: 2, 115 - 123, 16.08.2018

Nursal Arıcı , Nurhan Gül

Abstract

Yazılım projelerinin başarıya ulaşma
oranı teknolojik gelişmelere rağmen hala istenen düzeyde değildir. Yazılım
projelerinin büyük çoğunluğu ya istenen özelliklerde teslim edilememekte ya da
planlanan bütçeyi ve zamanı aşarak teslim edilebilmektedir. Yazılım proje
takvimi bu başarıya etki eden önemli faktörlerden biridir. İnsan kaynağı, zaman,
maliyet ve aktivitelerin işlem sırası gibi parametreler içerdiğinden dolayı,
yazılım proje takvimi oluşturmada durum uzayı çok büyüktür. Bu nedenle de proje
yöneticisinin manuel olarak başarılı bir proje takvimi oluşturması oldukça
zordur. Bu çalışmada insan kaynağı ve insan kaynağının aktiviteleri
gerçekleştirme süreleri ele alınarak yazılım proje takvimi, minimum tamamlanma
süresini sağlayacak şekilde oluşturulmaya çalışılmıştır. Yöntem olarak yapay
zeka optimizasyon algoritmalarından karınca kolonisi algoritması(ACO) ve yapay
arı kolonisi algoritması(ABC) kullanılmış ve sonuçlar analiz edilmiştir. Elde
edilen sonuçlara göre, her iki yöntem minimum proje süresini elde etmede
başarılı olmuştur. Yapay arı kolonisi algoritmasının işlem süresinin daha yavaş
olmasına karşın, koloni/yiyecek kaynağı sayısı arttığında karınca kolonisi
algoritmasına oranla sonuca daha hızlı yakınsadığı belirlenmiştir.

Keywords

Yazılım Proje Takvimi, Karınca Kolonisi Algoritması, Yapay Arı Kolonisi Algoritması

Software Project Scheduling Using Ant Colony and Artificial Bee Colony Algorithm

Abstract

The success rate of software projects is still not at the desired level despite of the technological advances. The vast majority of software projects can not be delivered at the desired specifications or can be delivered beyond the planned budget and time. The software project schedule is one of the important factors that influence this success. Due to it includes parameters such as human resource, time, cost, and process sequence of activities, state space is too big for software project scheduling. So it is difficult to create a software project schedule for software project managers. In this study, using human resource and activities these resource can do, it is tried to obtain minimum project completion time while creating software project schedule using ant colony and artificial bee colony optimization algorithm and results are analyzed. According the results obtained, both methods are successful in software project scheduling. Although the processing time of the artificial bee colony algorithm is slower than ant colony optimization algorithm, it has been determined that when the number of colony / food source is increased, it is converged to minimum project completion time faster than the ant colony algorithm.

Keywords

Software project scheduling, Ant colony optimization, Artificial bee colony algorithm, Optimization algorithm

References

• [1] S. Hastiwe, S. Wojewoda, Standish Group 2015 “Chaos Report Q&A with Jennifer Lynch.InfoQ.”, www.infoq.com, Erişilebilir: https://www.infoq.com/articles/standish-chaos-2015, [Erişim Tarihi: 07.03.2018].
• [2] Z. Gül, “Yazılım Geliştirme Sürecinin İyileştirilmesi ve Türkiye Uygulamaları”, Yüksek Lisans Tezi, İstanbul Teknik Üniversitesi, Fen Bilimleri Enstitüsü, İstanbul, 2006.
• [3] B. Crawford, R. Soto, G. Astorga, C. Castro, F. Paredes, S. Misra and J. Rubio, “Solving the Software Project Scheduling Problem Using Intelligent Water Drops”, Tehnički vjesnik, vol. 25(2), 2018. [4] A. Barreto, M. de O. Barros and C. M. L Werner, “Staffing a software project: A constraint satisfaction and optimization-based approach” Computers & Operations Research, vol. 35(10), pp. 3073-3089, 2008.
• [5] M. Peker, B. Sen and S. Bayir, “Using Artificial Intelligence Techniques for Large Scale Set Partitioning Problems” Procedia Technology, vol. 1, pp. 44–49, 2012
• [6] J. Xiao, X. Ao and Y. Tang, “Solving software project scheduling problems with ant colony optimization”, Computers & Operations Research, vol. 40, pp. 33-46, 2013 [7] W. Chen and J. Zhang, “Ant colony optimization for software project scheduling and staffing with an event-based scheduler”, IEEE Transactions on Software Engineering, vol. 39(1), pp. 1-17, 2013
• [8] Y. Singh, A. Kaur and B. Suri, “Test case prioritization using ant colony optimization”, ACM SIGNSOFT Software Engineering Notes, vol. 35(4), pp. 1-7, 2010.
• [9] B. Suri and S. Singal, “Implementing ant colony optimization for test case selection and prioritization”, International Journal on Computer Science and Engineering (IJCSE), vol. 3(5), pp. 1924-1932, 2011
• [10] B. Suri and P. Jajoria, “Using ant colony optimization in software development project scheduling”, 2013 International Conference on Advances in Computing, Communications and Informatics (ICACCI), Ağustos 22-25, 2013, Mysore, İndia, 2013, pp. 2101-2106.
• [11] B. Crawford, R. Soto, F. Johnson, E. Monfroye, E. And F. Paredes, “A Max–Min Ant System algorithm to solve the Software Project Scheduling Problem”, Expert Systems with Applications, vol. 41, pp. 6634-6645, 2014.
• [12] R. Akbaria, V. Zeighamib and K. Ziaratia, “Artificial Bee colony for resource constrained project scheduling problem”, International Journal of Industrial Engineering Computations, vol. 2(2011), pp. 45-60, 2011.
• [13] V. Nayak, H. Suthar and J. Gadit, “Implementation of Artificial Bee Colony Algorithm”, IAES International Journal of Artificial Intelligence, vol. 1(3), pp. 112-120, 2012.
• [14] J. C. Bansal, H. Sharma and S. S. Jadon, “Artificial bee colony algorithm: a survey”, Int. J. Advanced Intelligence Paradigms, vol. 5, pp. 123-159, 2013.
• [15] N. F. B. M. Pauzi, “Flowshop Scheduling Using Artificial Bee Colony (Abc) Algorithm With Varying Onlooker Bees Approaches”, Yüksek Lisans Tezi, Faculty of Mechanical and Manufacturing Engineering Universiti Tun Hussein Onn Malaysia, Johore, Malezya, 2015.
• [16] N. F. B. M. Pauzi and S. A. Bareduan, “Scheduling Analysis For Flowship Using Artifical Bee Colony (Abc) Algorithm With Varying Onlooker Approaches”, ARPN Journal of Engineering and Applied Sciences, vol. 11(10), pp. 6472- 6477, 2016.
• [17] M. F. Amer, Optimization Algorithms in Project Scheduling, “Optimization Algorithms - Methods and Applications”, Associate Prof. Ozgur Baskan (Ed.), InTech, DOI: 10.5772/63108, Available from: https://www.intechopen.com/books/optimization-algorithms-methods-and-applications/optimization-algorithms-in-project-scheduling, 2016
• [18] M. Dorigo and G. Di Caro, “Ant colony optimization: A new meta-heuristic”, In Proceedings of the 1999 congress on evolutionary computation, July 6-9, 1999, Washington, DC, USA, IEEE Press, pp. 1470–1477, 1999.
• [19] M. Dorigo, V. Maniezzo and A. Colorni, “Ant system: Optimization by a colony of cooperating agents”, IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics, vol. 26(1), pp. 29–41, 1996.
• [20] M. Dorigo and L. M. Gambardella, “Ant colony system: A cooperative learning approach to the traveling salesman problem”, IEEE Transactions on Evolutionary Computation, vol 1 (1), pp. 53-66, 1997.
• [21] T. Keskintürk and H. Söyler, “Global karınca kolonisi optimizasyon algoritması”, Gazi Üniversitesi Mühendislik Mimarlık Fakültesi Dergisi, vol. 12(4), pp. 689-698, 2006.
• [22] O. Mohammadrezapour and M. JavadZeynali, “Comparison of ant colony, elite ant system and maximum – minimum ant system algorithms for optimizing coefficients of sediment rating curve (case study: Sistan river)”, Journal of Applied Hydrology, vol. 1(2), pp. 55-66, 2014.
• [23] T. Stützle and H. H. Hoos, “Max min ant system”, Journal of Future Generation Computer Systems, vol. 8(16), pp. 889–914, 2000
• [24] N. Gül and N. Arıcı, “Constitution of Software Project Schedule with Ant Colony Algorithm”, Journal of New Results in Engineering and Natural Science, vol. 8, pp. 38-47, 2018
• [25] D. Karaboga, “An idea based on honey bee swarm for numerical optimization”, Technical report, Computer Engineering Department, Engineering Faculty, Erciyes University, 2005.
• [26] D. Karaboğa, Yapay Zeka Optimizasyon Algoritmaları, Dördüncü Baskı, Türkiye, Nobel Akademik Yayıncılık, 2017, pp. 201-222.
• [27] D. Karaboga, B. Gorkemli, C. Ozturk and N. Karaboga, “A comprehensive survey: Artificial bee colony (ABC) algorithm and applications”, Artif. Intell. Rev., pp. 1-37, 2012.