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Authors: Assaad Moawad 1 ; Thomas Hartmann 1 ; Francois Fouquet 1 ; Gregory Nain 1 ; Jacques Klein 1 and Johann Bourcier 2

Affiliations: 1 University of Luxembourg, Luxembourg ; 2 Université de Rennes 1, France

ISBN: 978-989-758-083-3

Keyword(s): Multi-objective Evolutionary Algorithms, Optimization, Genetic Algorithms, Model-driven Engineering.

Related Ontology Subjects/Areas/Topics: Frameworks for Model-Driven Development ; Languages, Tools and Architectures ; Methodologies, Processes and Platforms ; Model Transformations and Generative Approaches ; Model-Driven Architecture ; Model-Driven Software Development ; Modeling for the Cloud ; Software Engineering ; Software Process Modeling, Enactment and Execution

Abstract: Multi-Objective Evolutionary Algorithms (MOEAs) have been successfully used to optimize various domains such as finance, science, engineering, logistics and software engineering. Nevertheless, MOEAs are still very complex to apply and require detailed knowledge about problem encoding and mutation operators to obtain an effective implementation. Software engineering paradigms such as domain-driven design aim to tackle this complexity by allowing domain experts to focus on domain logic over technical details. Similarly, in order to handle MOEA complexity, we propose an approach, using model-driven software engineering (MDE) techniques, to define fitness functions and mutation operators without MOEA encoding knowledge. Integrated into an open source modelling framework, our approach can significantly simplify development and maintenance of multi-objective optimizations. By leveraging modeling methods, our approach allows reusable optimizations and seamlessly connects MOEA and MDE paradig ms. We evaluate our approach on a cloud case study and show its suitability in terms of i) complexity to implement an MOO problem, ii) complexity to adapt (maintain) this implementation caused by changes in the domain model and/or optimization goals, and iii) show that the efficiency and effectiveness of our approach remains comparable to ad-hoc implementations. (More)

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Paper citation in several formats:
Moawad, A.; Hartmann, T.; Fouquet, F.; Nain, G.; Klein, J. and Bourcier, J. (2015). Polymer - A Model-driven Approach for Simpler, Safer, and Evolutive Multi-objective Optimization Development.In Proceedings of the 3rd International Conference on Model-Driven Engineering and Software Development - Volume 1: MODELSWARD, ISBN 978-989-758-083-3, pages 286-293. DOI: 10.5220/0005243202860293

@conference{modelsward15,
author={Assaad Moawad. and Thomas Hartmann. and Francois Fouquet. and Gregory Nain. and Jacques Klein. and Johann Bourcier.},
title={Polymer - A Model-driven Approach for Simpler, Safer, and Evolutive Multi-objective Optimization Development},
booktitle={Proceedings of the 3rd International Conference on Model-Driven Engineering and Software Development - Volume 1: MODELSWARD,},
year={2015},
pages={286-293},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0005243202860293},
isbn={978-989-758-083-3},
}

TY - CONF

JO - Proceedings of the 3rd International Conference on Model-Driven Engineering and Software Development - Volume 1: MODELSWARD,
TI - Polymer - A Model-driven Approach for Simpler, Safer, and Evolutive Multi-objective Optimization Development
SN - 978-989-758-083-3
AU - Moawad, A.
AU - Hartmann, T.
AU - Fouquet, F.
AU - Nain, G.
AU - Klein, J.
AU - Bourcier, J.
PY - 2015
SP - 286
EP - 293
DO - 10.5220/0005243202860293

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