Offshore wind farm layout and cable routing are intrinsically coupled, and optimizing them separately often leads to suboptimal solutions. To address this issue, a geometry-guided genetic algorithm (GGA) is proposed to jointly address wind farm layout optimization (WFLO) and electrical cable routing (ECR) with the levelized cost of energy (LCOE) as the unified objective. The proposed geometry-guided crossover partitions the wind farm into geometric sectors to preserve spatial structures and enhance search efficiency, while a balance-sector routing (BSR) strategy ensures balanced branch loading under cable capacity constraints. A benchmark dataset of eight real offshore wind farms with measured wind resources and irregular boundaries is constructed to support reproducible evaluation. Experimental results demonstrate that GGA consistently outperforms nine state-of-the-art algorithms, achieving an average LCOE reduction of 2.88% across all cases and up to 4.09% in the best case, with stable convergence across all sites. Compared with conventional routing methods, BSR further reduces cable costs by 2.84%–4.51% across all algorithms and sites. • A geometry-guided crossover operator improves layout search in irregular sites. • A balance-sector routing strategy reduces cable costs by 2.84%–4.51%. • Integrated optimization reduces the levelized cost of energy by up to 4.09%. • A public benchmark dataset for eight-site wind farms is now accessible. • Geometry-aware search yields stable convergence across nine competing algorithms.
Zhang et al. (2026) studied this question.