A Multi-Population Ivy Algorithm for Solving the Grid-Based Wind Turbine Layout Optimization Problem

Optimizing wind turbine layout is a critical aspect of wind farm construction planning, and it directly affects the power generation efficiency, economic performance and environmental adaptability of the wind farm. Aimed at effectively solving grid-based wind turbine layout optimization problems, a modified Ivy algorithm is proposed in this study by introducing a multi-population mechanism, roulette fitness-distance balance (roulette FDB) selection mechanism, and a one-to-one replacement mechanism into the basic IVY algorithm. The proposed algorithm better balances the global search and the local exploitation, thus achieving better optimization capability. Four 10 × 10 grid-based wind turbine layout optimization (WTLO) benchmark cases covering full-wake and partial-wake models are adopted to validate the performance of the proposed algorithm via comparative analyses against six state-of-the-art meta-heuristic algorithms. The Jensen wake model is adopted to characterize wind turbine wake effects, and the Mosetti cost model serves as the optimization objective. Experimental and comparative results demonstrate that the proposed algorithm exhibits strong competitiveness and outperforms other algorithms in terms of convergence rate, accuracy and stability.