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Interactive ant colony optimization (iACO) for early lifecycle software design

Simons, Chris; Smith, Jim; White, Paul

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Jim Smith
Professor in Interactive Artificial Intelligence

Paul White
Professor in Applied Statistics


Finding good designs in the early stages of the software development lifecycle is a demanding multi-objective problem that is crucial to success. Previously, both interactive and non-interactive techniques based on evolutionary algorithms (EAs) have been successfully applied to assist the designer. However, recently ant colony optimization was shown to outperform EAs at optimising quantitative measures of software designs with a limited computational budget. In this paper, we propose a novel interactive ACO (iACO) approach, in which the search is steered jointly by an adaptive model that combines subjective and objective measures. Results show that iACO is speedy, responsive and effective in enabling interactive, dynamic multi-objective search. Indeed, study participants rate the iACO search experience as compelling. Moreover, inspection of the learned model facilitates understanding of factors affecting users' judgements, such as the interplay between a design's elegance and the interdependencies between its components. © 2014 Springer Science+Business Media New York.


Simons, C., Smith, J., & White, P. (2014). Interactive ant colony optimization (iACO) for early lifecycle software design. Swarm Intelligence, 8(2), 139-157.

Journal Article Type Article
Acceptance Date May 30, 2014
Online Publication Date Jun 19, 2014
Publication Date Jun 30, 2014
Publicly Available Date Jun 6, 2019
Journal Swarm Intelligence
Print ISSN 1935-3812
Electronic ISSN 1935-3820
Publisher Springer Verlag
Peer Reviewed Peer Reviewed
Volume 8
Issue 2
Pages 139-157
Keywords ant colony optimization, software design, interactive search
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Additional Information Additional Information : The final publication is available at Springer via


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