Optimizing composite shell with neural network surrogate models and genetic algorithms: Balancing efficiency and fidelity
Bartosz Miller, Leonard Ziemiański
Abstract
This study addresses the challenge of multi-objective optimization of a composite shell structure while adhering to constraints on the number of calls to a pseudo-experimental model, simulating real experiments. Two considered objective functions are defined to determine the investigated structure’s dynamic properties and material costs; the optimization involves genetic algorithms, neural surrogate model and multi-fidelity finite-element models. The results of multi-objective optimization were presented as Pareto fronts. A new strategy for preliminary result verification is proposed, significantly reducing the need for a computationally intensive complete verification that requires complex models or experimental investigations. Two different indicators are applied to assess the quality of the obtained Pareto fronts; one is a new one proposed in the paper. Moreover, a multi-fidelity approach is discussed, and three finite element models with different mesh densities are employed, together with a pseudo-experimental model constructed using high-fidelity results and incorporating a nonlinear transformation. However, challenges arise due to the arbitrarily constrained number of pseudo-experiments, limiting future experiments is crucial. The study highlights the need for further analysis of Pareto front indicators and statistical analysis of applied tools like deep neural networks and genetic algorithms. Future research directions include exploring ensemble learning in surrogate models for potential optimization benefits. • Multi-Fidelity Modeling: Demonstrated the effectiveness of a multi-fidelity approach. • Deep Network Surrogate Models: Showed the ability to capture complex data structures. • Multi-Objective Optimization: Determined dynamic properties and material costs. • Curriculum Learning: An innovative strategy for surrogate model improvement. • Pareto Front Quality: Evaluated using relevant indicators.