Dao, D. A.D. A.DaoChmelnizkij, A.A.ChmelnizkijCerek, K.K.CerekHadjiloo, E.E.HadjilooGrabe, J.J.GrabeSmarsly, K.K.Smarsly2026-06-162026-06-162026-04-23International Journal of Naval Architecture and Ocean Engineering 18: 100766 (2026)https://hdl.handle.net/11420/63499Floating 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.en2092-67902026Elsevierhttps://creativecommons.org/licenses/by/4.0/Drag embedment anchors (DEAs)Floating structuresMulti-objective evolutionary algorithmOffshore renewablesOptimisationNatural Sciences and Mathematics::519: Applied Mathematics, ProbabilitiesTechnology::624: Civil Engineering, Environmental Engineering::624.1: Structural Engineering::624.15: Geotechnical EngineeringJournal Articlehttps://doi.org/10.15480/882.1731910.1016/j.ijnaoe.2026.10076610.15480/882.17319