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Hybridation of Bayesian networks and evolutionary algorithms for multi-objective optimization in an integrated product design and project management context

Pitiot, Paul and Coudert, Thierry and Geneste, Laurent and Baron, Claude Hybridation of Bayesian networks and evolutionary algorithms for multi-objective optimization in an integrated product design and project management context. (2010) Engineering Applications of Artificial Intelligence, vol. 23 (n° 5). pp. 830 - 843. ISSN 0952-1976

(Document in English)

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Official URL: http://www.sciencedirect.com/science/article/pii/S0952197610000370


A better integration of preliminary product design and project management processes at early steps of system design is nowadays a key industrial issue. Therefore, the aim is to make firms evolve from classical sequential approach (first product design the project design and management) to new integrated approaches. In this paper, a model for integrated product/project optimization is first proposed which allows taking into account simultaneously decisions coming from the product and project managers. However, the resulting model has an important underlying complexity, and a multi-objective optimization technique is required to provide managers with appropriate scenarios in a reasonable amount of time. The proposed approach is based on an original evolutionary algorithm called evolutionary algorithm oriented by knowledge (EAOK). This algorithm is based on the interaction between an adapted evolutionary algorithm and a model of knowledge (MoK) used for giving relevant orientations during the search process. The evolutionary operators of the EA are modified in order to take into account these orientations. The MoK is based on the Bayesian Network formalism and is built both from expert knowledge and from individuals generated by the EA. A learning process permits to update probabilities of the BN from a set of selected individuals. At each cycle of the EA, probabilities contained into the MoK are used to give some bias to the new evolutionary operators. This method ensures both a faster and effective optimization, but it also provides the decision maker with a graphic and interactive model of knowledge linked to the studied project. An experimental platform has been developed to experiment the algorithm and a large campaign of tests permits to compare different strategies as well as the benefits of this novel approach in comparison with a classical EA.

Item Type:Article
Additional Information:Thanks to Elsevier editor. The definitive version is available at http://www.sciencedirect.com The original PDF of the article can be found at Engineering Applications of Artificial Intelligence website : http://www.sciencedirect.com/science/article/pii/S0952197610000370
Audience (journal):International peer-reviewed journal
Uncontrolled Keywords:
Institution: Université de Toulouse > Ecole des Mines d'Albi-Carmaux - EMAC
Université de Toulouse > Institut National Polytechnique de Toulouse - INPT
Université de Toulouse > Institut National des Sciences Appliquées de Toulouse - INSA
Université de Toulouse > Université de Toulouse II-Le Mirail - UTM
Laboratory name:
Deposited By:Thierry Coudert

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