Reference : Polymer: A Model-Driven Approach for Simpler, Safer, and Evolutive Multi-Objective Op...

	Document type :	Scientific congresses, symposiums and conference proceedings : Paper published in a book
	Discipline(s) :	Engineering, computing & technology : Computer science
	To cite this reference:	http://hdl.handle.net/10993/20392

Title :	Polymer: A Model-Driven Approach for Simpler, Safer, and Evolutive Multi-Objective Optimization Development
Language :	English
Author, co-author :	Moawad, Assaad [University of Luxembourg > Interdisciplinary Centre for Security, Reliability and Trust (SNT) > >]
	Hartmann, Thomas [University of Luxembourg > Interdisciplinary Centre for Security, Reliability and Trust (SNT) > >]
	Fouquet, François [University of Luxembourg > Interdisciplinary Centre for Security, Reliability and Trust (SNT) > >]
	Nain, Grégory [University of Luxembourg > Interdisciplinary Centre for Security, Reliability and Trust (SNT) > >]
	Klein, Jacques [University of Luxembourg > Faculty of Science, Technology and Communication (FSTC) > Computer Science and Communications Research Unit (CSC) >]
	Bourcier, Johann [University of Rennes > IRISA / INRIA]
Publication date :	Feb-2015
Main document title :	MODELSWARD 2015 - Proceedings of the 3rd International Conference on Model-Driven Engineering and Software Development
Editor :	Hammoudi, Slimane
	Pires, Luis Ferreira
	Desfray, Philippe
	Filipe, Joaquim
Publisher :	SCITEPRESS
Pages :	286-293
Peer reviewed :	Yes
On invitation :	No
Audience :	International
ISBN :	978-989-758-083-3
Country :	Portugal
Event name :	MODELSWARD 2015 - 3rd International Conference on Model-Driven Engineering and Software Development
Event date :	9-02-2015 to 11-02-2015
Event organizer :	ESEO, Angers, Loire Valley, France
Event place (city) :	Anger
Event country :	France
Keywords :	[en] Multi-Objective Evolutionary Algorithms ; Optimization ; Genetic Algorithms ; Model-Driven Engineering
Abstract :	[en] 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 paradigms. 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.
Target :	Researchers ; Professionals ; Students
Permalink :	http://hdl.handle.net/10993/20392

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