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Conference object . Other literature type . Article . 2021 . Peer-reviewed
License: CC BY
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effectiveness of surrogate based optimization algorithms for system architecture optimization

Authors: Bussemaker, Jasper; Bartoli, Nathalie; Lefebvre, Thierry; Ciampa, Pier Davide; Nagel, Björn;

effectiveness of surrogate based optimization algorithms for system architecture optimization

Abstract

International audience; The design of complex system architectures brings with it a number of challenging issues, among others large combinatorial design spaces. Optimization can be applied to explore the design space, however gradient-based optimization algorithms cannot be applied due to the mixed-discrete nature of the design variables. It is investigated how effective surrogate-based optimization algorithms are for solving the black-box, hierarchical, mixed-discrete, multi-objective system architecture optimization problems. Performance is compared to the NSGA-II multi-objective evolutionary algorithm. An analytical benchmark problem that exhibits most important characteristics of architecture optimization is defined. First, an investigation into algorithm effectiveness is performed by measuring how accurately a known Pareto-front can be approximated for a fixed number of function evaluations. Then, algorithm efficiency is investigated by applying various multi-objective convergence criteria to the algorithms and establishing the possible trade-off between result quality and function evaluations needed. Finally, the impact of hidden constraints on algorithm performance is investigated. The code used for this paper has been published.

Countries
France, Germany
Keywords

Optimization, Search Algorithm, Optimization Algorithm, system architecting, surrogate-based optimization, Surrogate Modeling, Machine Learning, [SPI]Engineering Sciences [physics], Artificial Intelligence, Information, and Communication, kriging, [MATH]Mathematics [math], [PHYS]Physics [physics], Genetic Algorithm, Computer Programming and Language, Data Science, Computing, Evolutionary Algorithm, rbf, mdo, Supercomputers, Avionics Computers, Computing and Informatics, optimization, Algorithms and Data Structures

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