Hybrid Optimisation with Quantum Inspired Evolutionary Algorithm in Multiple Drop Test Simulation – Metamodel Selection and Hyperparameter Tuning

This study introduces a hybrid optimization framework for the multi-drop testing of a lithium-ion battery enclosure. The framework integrates a Quantum-Inspired Evolutionary Algorithm (QEA) with surrogate modeling techniques. Three types of metamodels were applied—Artificial Neural Networks (ANN), Kriging, and Polynomial Regression (PNR)—using datasets generated via Latin Hypercube Sampling and from prior QEA iterations. The hyperparameters tuning methods are the main part of the paper. Two fitness functions were analyzed, including a logarithmically scaled variant designed to compress the output range for damaged cases and enhance classification accuracy near the damage/no-damage boundary. A dual-model strategy was employed for ANN with model switching determined by a plastic strain threshold. Across datasets, ANN more consistently identified superior individuals compared to Kriging, while PNR occasionally exhibited instability. Two hybrid schemes were implemented: HYBRID1, which enforces finite element model re-evaluation of the best candidate in each iteration, resulting in the lowest minima but with increased variability; and HYBRID2, which minimizes mandatory Finite Element Method (FEM) evaluations and retraining cycles, thereby improving runtime and stability at a slight cost to solution quality. Overall, the combination of QEA with ANN and the proper objective function reduced FEM computational time by an order of magnitude while maintaining decision-making effectiveness, supporting its feasibility for application in industrial design workflows.