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Swarm Intelligence
Erschienen in: Swarm Intelligence 2/2014

01.06.2014

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

verfasst von: Christopher L. Simons, Jim Smith, Paul White

Erschienen in: Swarm Intelligence | Ausgabe 2/2014

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Abstract

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.
Vorheriger Artikel Revisiting wavefront construction with collective agents: an approach to foraging

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Fußnoten
1
The use of anthropocentric language is deliberate here, and mimics users’ vocal responses during interaction sessions.
 
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Metadaten
Titel
Interactive ant colony optimization (iACO) for early lifecycle software design
verfasst von
Christopher L. Simons
Jim Smith
Paul White
Publikationsdatum
01.06.2014
Verlag
Springer US
Erschienen in
Swarm Intelligence / Ausgabe 2/2014
Print ISSN: 1935-3812
Elektronische ISSN: 1935-3820
DOI
https://doi.org/10.1007/s11721-014-0094-2

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