This dataset contains the full algorithms implemented in Python of the newly proposed deflation constraints, here used for the global optimization of laminated composite structures. Three examples are herein provided, being the first a demonstration of the deflation constraint to find all minima points of a double-cosine function. The second example named “Case_Study_1” applies the deflation constraint together with the ghost layer approach for the discrete optimization of laminated plate layups. Finally, the third example named “Case_Study_2” deals with variable-stiffness composites, comparing a gradient-descent deflated optimization with a genetic algorithm, to obtain optimum lamination parameters and to retrieve optimum layups. The codes “interior_opt.py” and “interior_opt_autograd.py” implement the interior-point optimization described in the main manuscript. All methods are implemented in Python. This is the reference paper explaining everything about the proposed deflation constraints. In case you use this dataset, please cite the dataset and the paper: Bangera S.S., Castro S.G.P. “Deflation constraints for global optimization of composite structures”. Composite Structures, 2025. DOI: https://doi.org/10.1016/j.compstruct.2025.118916. Bangera S.S., Castro S.G.P. "Dataset: Deflation constraints for global optimization of composite structures" [Data set]. Zenodo, 2024. 10.5281/zenodo.14511012