Title
Integrating firefly algorithm in artificial neural network models for accurate software cost predictions.
Abstract
Human effort is one of the main resources of software cost estimation. A successful software project development primarily relies on accurate effort prediction at an early stage of development. There are many effort prediction models in the literature. Deciding which model to choose is a challenge for the project managers. This paper investigates whether it is possible to improve the accuracy of software cost estimations by coupling firefly algorithm with the existing artificial neural network (ANN) models used in software cost predictions. The firefly algorithm is one of the recent evolutionary computing models inspired by the behaviour of fireflies in nature. This is compared with particle swarm optimization used already in literature for software cost estimations. The ANN models examined in this work include radial basis function network and functional link artificial neural networks models. The experimental results show that ANN models perform extremely well by incorporating firefly algorithm and intuitionistic fuzzy C-means for data preprocessing. The proposed approach is empirically validated through a statistical framework. Copyright (C) 2016 John Wiley & Sons, Ltd.
Year
DOI
Venue
2016
10.1002/smr.1792
JOURNAL OF SOFTWARE-EVOLUTION AND PROCESS
Keywords
Field
DocType
software cost estimation,radial basis function neural network,functional link artificial neural network,firefly algorithm,particle swarm optimization,intuitionistic fuzzy C means
Particle swarm optimization,Data mining,Radial basis function network,Computer science,Evolutionary computation,Cost estimate,Firefly algorithm,Software,Artificial intelligence,Artificial neural network,Machine learning,Project management
Journal
Volume
Issue
ISSN
28.0
8.0
2047-7473
Citations 
PageRank 
References 
4
0.48
20
Authors
4
Name
Order
Citations
PageRank
Anupama Kaushik1152.56
Devendra K. Tayal25710.59
Kalpana Yadav340.48
Arvinder Kaur437026.99