A graph-based multi-objective genetic algorithm for optimizing the structural performance in double-layer shell structures

Discretization for fabrication and optimizing component arrangements in shell structures are crucial for enhancing structural performance and reducing material usage. Efficient optimization can significantly improve load-bearing capacity and overall stability while minimizing weight and cost. This research proposes a graph-based multi-objective genetic algorithm to optimize the arrangement and segmentation of the support layer in double-layer shell structures made of sheet material. Each solution in the algorithm’s search space represents a double-layer thin sheet metal shell with a common main shell and a unique support layer. The support layer is composed of a set of meshes reconstructed from graph segments derived from the decomposition of the dual graph of the shell’s support layer. Various subgraph arrangements are explored through crossover and mutation operations designed for graphs. The population is refined using non-dominated sorting and front ranking to identify optimal solutions. The objectives are to minimize the maximum displacement under the structure’s own weight and to reduce its overall mass. Structural analysis is used to evaluate the alternatives. The algorithm efficiently explores the solution space, producing Pareto fronts that represent optimal trade-offs between these conflicting objectives. The results demonstrate the method’s effectiveness in achieving a balanced design that meets both structural and weight constraints. • Optimized shell structures using a graph-based multi-objective genetic algorithm. • Reduced maximum displacement and weight in double-layer shell structures. • Compared algorithmic solutions with manual designs for structural performance. • Flexibility to explore trade-offs between structural performance objectives. • Applicable for topology optimization and sustainable fabrication strategies.