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

Yıl 2018, Cilt: 4 Sayı: 2, 115 - 123, 16.08.2018

Nursal Arıcı Nurhan Gül

Öz

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.

Anahtar Kelimeler

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

Software Project Scheduling Using Ant Colony and Artificial Bee Colony Algorithm

Öz

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.

Anahtar Kelimeler

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

Kaynakça

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