Premium
Integrating firefly algorithm in artificial neural network models for accurate software cost predictions
Author(s) -
Kaushik Anupama,
Tayal Devendra Kr.,
Yadav Kalpana,
Kaur Arvinder
Publication year - 2016
Publication title -
journal of software: evolution and process
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.371
H-Index - 29
eISSN - 2047-7481
pISSN - 2047-7473
DOI - 10.1002/smr.1792
Subject(s) - firefly algorithm , computer science , artificial neural network , software , particle swarm optimization , machine learning , artificial intelligence , fitness function , preprocessor , data mining , genetic algorithm , programming language
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 © 2016 John Wiley & Sons, Ltd.