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Advisor(s)
Abstract(s)
In the proposed approach the robust design optimization (RDO) of composite structures is addressed as a
bi-objective optimization problem with following objective functions: (1) the weight of the structure
which optimality is associated with performance robustness; and (2) the variability of structural
response which is associated with feasibility robustness of design constraints. The determinant of the
variance–covariance matrix of the response is adopted for feasibility robustness assessment, being the
sensitivities calculated by the adjoint variable method. The design and uncertainty rules are controlled
by the following classes of variables and parameters: the deterministic design variables, the random
design variables, and the random parameters.
To solve the RDO of composite structures an evolutionary algorithm, denoted by Bi-level Dominance
Multi-Objective Genetic Algorithm (MOGA-2D) is proposed. The Pareto front is built using a hierarchical
structure where evolution is based on the exchange data between two populations: a small population
using local dominance and elitism and an enlarged population to store the non-dominated solutions.
The numerical tests show the capabilities of the approach. Although the optimal Pareto front establishes
the trade-off between performance and robustness, knowledge on the importance of each uncertainty
source can help the designer to make a decision on design space.
Description
Keywords
Multi-objective optimization Composite structures Minimum weight Feasibility robustness Bi-level dominance
Citation
Publisher
Elsevier