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Integrating firefly algorithm in artificial neural network models for accurate software cost predictions.

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Title: Integrating firefly algorithm in artificial neural network models for accurate software cost predictions.
Authors: Kaushik, Anupama1,2, Tayal, Devendra Kr.3, Yadav, Kalpana4, Kaur, Arvinder5
Source: Journal of Software: Evolution & Process. Aug2016, Vol. 28 Issue 8, p665-688. 24p.
Subjects: Cost estimates, Computer software costs, Artificial neural networks, Fuzzy control systems, Evolutionary computation
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 © 2016 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]
Copyright of Journal of Software: Evolution & Process is the property of Wiley-Blackwell and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
Database: Engineering Source
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  Data: 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. [ABSTRACT FROM AUTHOR]
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  Data: <i>Copyright of Journal of Software: Evolution & Process is the property of Wiley-Blackwell and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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        Text: English
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      – SubjectFull: Computer software costs
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      – SubjectFull: Artificial neural networks
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      – SubjectFull: Fuzzy control systems
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              Text: Aug2016
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