Smart Hybrid Metaheuristic Model for Enhanced Wind Energy Production

Submitted on 22 Jan 2026

Abstract

This study presents a hybrid Particle Swarm Optimization–Genetic Algorithm (PSO-GA) technology integrated into a structured three-phase strategy to address the wind farm layout optimization (WFLO) problem. In order to enhance total energy efficiency through intelligent turbine location, the proposed strategy is applied to a particular wind farm scenario. Three case studies, each representing varying degrees of wake and non-wake settings, are analyzed to assess the robustness of this method. In order to prevent severe wake interference, the system finds the best location for turbines while strictly following to industry-standard spacing standards. The suggested hybrid model consistently improves energy extraction and reduces wake losses by 20–28% in all scenarios when compared to current method like PSO-based design by [21]. The hybrid PSO-GA still has a moderate computational cost, taking about twenty seconds each simulation. This is just 10–15% more than standalone PSO, but it produces far greater convergence stability.