Game theory and evolutionary algorithms applied to MDO in the AGILE European project. Della Vecchia, P., Stingo, L., Corcione, S., Ciliberti, D., Nicolosi, F., De Marco, A., & Nardone, G. In 18th AIAA/ISSMO Multidisciplinary Analysis and Optimization Conference, 6, 2017. American Institute of Aeronautics and Astronautics.
Game theory and evolutionary algorithms applied to MDO in the AGILE European project [link]Website  doi  abstract   bibtex   
© 2017 American Institute of Aeronautics and Astronautics Inc, AIAA. All rights reserved. In this paper, an optimization technique in aircraft design field, based on game theory and evolutionary algorithms to define the key variables for Multi-Disciplinary aircraft Optimization (MDO) into AGILE (Aircraft 3rd Generation MDO for Innovative Collaboration of Heterogeneous Teams of Experts) European project, is presented. This work represents one of the contributions given by UniNa (University of Naples “Federico II”) research group within the AGILE project, which is coordinated by the DLR and funded by EU through the project HORIZON 2020 that aims to create an evolution of MDO, promoting a novel approach based on collaborative remote design and knowledge dissemination among various teams of experts. Since the aircraft design field is very complex in terms of number of involved variables and the dimension of the space of variation, it is not feasible to perform an optimization process on all the design parameters; this leads to the need to reduce the number of the parameters to the most significant ones. A multi-objective optimization approach allows many different variables, which could be a constraint or an objective function for the specific investigation; thus, setting the constraints and objectives to reach, it is possible to perform an optimization process and control which parameters significantly affect the final result. Within AGILE project, UniNa research group aims to perform wing optimization processes in a preliminary design stage, coupling Nash game theory (N) with typical genetic evolutionary algorithm (GA), reducing computational time and allowing a more realistic association among objective functions and variables, to identify the main ones that significantly affect final result and that consequently must be considered by the partners of the AGILE consortium to perform MDO in the final part of project, applying the proposed optimization technique to novel aircraft configuration.
@inproceedings{
 title = {Game theory and evolutionary algorithms applied to MDO in the AGILE European project},
 type = {inproceedings},
 year = {2017},
 keywords = {Collaborative aircraft design,Evolutionary algorithm,Game theory,Multidisciplinary design optimization (MDO)},
 websites = {https://arc.aiaa.org/doi/10.2514/6.2017-4330},
 month = {6},
 publisher = {American Institute of Aeronautics and Astronautics},
 day = {5},
 city = {Denver, Colorado},
 id = {a83ab2c2-1821-31eb-8c5f-9c785e8a4e2e},
 created = {2019-03-29T08:50:33.227Z},
 accessed = {2017-08-22},
 file_attached = {false},
 profile_id = {f8b1d016-0aa7-3bc4-bc5a-9effb6337891},
 last_modified = {2022-04-04T08:03:14.349Z},
 read = {false},
 starred = {false},
 authored = {true},
 confirmed = {true},
 hidden = {false},
 citation_key = {DellaVecchia2017},
 folder_uuids = {3c21352d-4636-4066-a894-176c1b7d9488},
 private_publication = {false},
 abstract = {© 2017 American Institute of Aeronautics and Astronautics Inc, AIAA. All rights reserved. In this paper, an optimization technique in aircraft design field, based on game theory and evolutionary algorithms to define the key variables for Multi-Disciplinary aircraft Optimization (MDO) into AGILE (Aircraft 3rd Generation MDO for Innovative Collaboration of Heterogeneous Teams of Experts) European project, is presented. This work represents one of the contributions given by UniNa (University of Naples “Federico II”) research group within the AGILE project, which is coordinated by the DLR and funded by EU through the project HORIZON 2020 that aims to create an evolution of MDO, promoting a novel approach based on collaborative remote design and knowledge dissemination among various teams of experts. Since the aircraft design field is very complex in terms of number of involved variables and the dimension of the space of variation, it is not feasible to perform an optimization process on all the design parameters; this leads to the need to reduce the number of the parameters to the most significant ones. A multi-objective optimization approach allows many different variables, which could be a constraint or an objective function for the specific investigation; thus, setting the constraints and objectives to reach, it is possible to perform an optimization process and control which parameters significantly affect the final result. Within AGILE project, UniNa research group aims to perform wing optimization processes in a preliminary design stage, coupling Nash game theory (N) with typical genetic evolutionary algorithm (GA), reducing computational time and allowing a more realistic association among objective functions and variables, to identify the main ones that significantly affect final result and that consequently must be considered by the partners of the AGILE consortium to perform MDO in the final part of project, applying the proposed optimization technique to novel aircraft configuration.},
 bibtype = {inproceedings},
 author = {Della Vecchia, Pierluigi and Stingo, Luca and Corcione, Salvatore and Ciliberti, Danilo and Nicolosi, Fabrizio and De Marco, Agostino and Nardone, Giovanni},
 doi = {10.2514/6.2017-4330},
 booktitle = {18th AIAA/ISSMO Multidisciplinary Analysis and Optimization Conference}
}
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