Cerek, KacperKacperCerekHadjiloo, ElnazElnazHadjilooGrabe, JürgenJürgenGrabeDao, Duy AnhDuy AnhDao2025-08-192025-08-192025-12Journal of Ocean Engineering and Science 10 (6): 1019-1030 (2025)https://hdl.handle.net/11420/57048Establishing renewables on a floating platform in the deep sea needs secure anchoring to the seabed, commonly achieved with drag embedment anchors (DEAs). The conventional design process relies heavily on empirical testing and is often time and resource-intensive, potentially leading to suboptimal designs. This research aims to overcome these limitations by applying an evolutionary optimization algorithm to existing analytical solutions for DEAs, identifying optimal anchor fluke and shank lengths. By leveraging an optimization strategy, we aim to enhance the design process while diminishing the dependency on exhaustive physical testing and high computational cost. We employ the Non-Dominated Sorting Genetic Algorithm II (NSGA-II) to optimize anchor shapes, with a focus on three key objectives: maximizing embedment depth and bearing capacity, and minimizing anchor volume. The methodology presents a Pareto front, encompassing all optimal solutions based on the formulated objectives, and demonstrates the efficiency of NSGA-II as a tool for optimizing anchor shapes.en2468-01332025610191030Elsevierhttps://creativecommons.org/licenses/by/4.0/Drag embedment anchorFloating renewablesMulti-objective optimizationNSGA-IISustainable designSocial Sciences::333: Economics of Land and EnergyTechnology::629: Other BranchesTechnology::620: EngineeringJournal Articlehttps://doi.org/10.15480/882.1579210.1016/j.joes.2025.07.00210.15480/882.15792Journal Article