Floating offshore renewable energy systems rely on secure anchoring, e.g. drag embedment anchors (DEAs). DEA design involves balancing multiple objectives and considering various geometric parameters. Although multi-objective optimisation packages are widely available, DEA optimisation is still frequently handled heuristically. This study introduces a workflow that couples established semi-analytical DEA kinematics in MATLAB with multi-objective evolutionary optimisation (gamultiobj, based on NSGA-II) to identify anchor geometries that minimise anchor volume while maximising bearing capacity and penetration depth. Radar charts and the visualisation of the Pareto front across the design space provide interpretable structures that are not directly apparent from the governing equations alone. Three case studies are presented. The first identifies Pareto-optimal fluke and shank lengths and examines the influence of the boundary on the solutions. The second expands the design space to include fluke thickness, junction length, and fluke–shank angle. Clustering of Pareto solutions reveals three consistent geometric groups: (i) generally large anchors with small junctions, (ii) anchors with larger junctions and variable proportions, and (iii) anchors with broad flukes. A high fluke-shank angle is consistently present across all groups and appears beneficial for all objectives. The third case applies a weighted-sum formulation to produce a single design. Overall, this study provides a workflow for optimisation-driven exploration of DEA design, including a classification of Pareto-optimal geometries that offers a compact way to assess trade-offs from concept to sizing. • Workflow couples DEA analytics with multi-objective evolutionary optimisation in MATLAB. • Pareto-optimal anchor design considering volume, bearing capacity and penetration depth. • Full Pareto front resolved, and radar charts reveal interpretable trade-offs. • Three Pareto groups found: small junctions, larger junctions, broad flukes. • In clay, a high fluke–shank angle is consistently beneficial across objectives.
Dao et al. (Wed,) studied this question.