Application of an Improved Ant Colony Optimization Algorithm to Wind-Farm Topology Design

As modern wind farms scale up, an optimal configuration of intra-farm submarine cables has become pivotal to both capital expenditure and operational efficiency. Conventional routing techniques rarely incorporate the full suite of real-world constraints, leading to unnecessary costs and construction challenges. To overcome these limitations, we formulate a cable-routing optimisation model that explicitly minimises total expenditure while embedding practical restrictions such as cable-capacity limits, branch-access regulations, non-crossing requirements, and branch-count control to guarantee topological feasibility. Building on this model, we introduce a Hybrid Particle Swarm Optimiser with Genetic Inheritance (YPSO). The proposed algorithm augments the canonical particle-swarm update rules with structural inheritance and parent-learning strategies, thereby reinforcing global exploration and stabilising convergence. A comprehensive benchmark against three state-of-the-art methods—standard PSO, Kinetic-Energy-Compensated PSO (DPSO), and Adaptive PSO (ZPSO)—demonstrates pronounced differences in convergence speed, structural rationality, and cost control. Empirical results show that YPSO consistently outperforms competing algorithms in layout compactness, branch organisation, and objective-function optimisation, achieving an average cable-routing cost reduction of 3.82 % across multiple test cases while maintaining excellent stability and robustness. The proposed modelling approach and optimisation strategy thus provide an effective decision-support framework for the practical design of wind-farm cabling systems