Hadeda Ibises optimization algorithm for microgrid energy management

• A new Hadeda Ibis Optimization (HIO) algorithm is developed. • HIO integrates clustering, adaptive velocity, and dual movement. • Demonstrated strong results on benchmark and constrained problems. • Applied to MG energy management to reduce cost, emissions, and improve reliability. The increasing penetration of renewable energy sources has made microgrid energy management a challenging optimization task. Variability in renewable generation and operational constraints leads to nonlinear and constrained scheduling problems. Metaheuristic optimization techniques are widely adopted to handle such complexity. Many existing algorithms show early convergence and limited exploration capability. This study proposes a new swarm intelligence algorithm, Hadeda Ibises Optimization (HIO), to address these issues. The behavior of Hadeda Ibises in nature shows cooperative foraging and coordinated movement within groups. These characteristics are modeled in HIO using clustering-based leader guidance and a polar coordinate velocity update to improve convergence and maintain population diversity. The performance of the proposed optimization is tested on 23 benchmark functions, including seven unimodal (F1–F7), six multimodal (F8–F13), and ten hybrid and composite functions (F14–F23). The statistical results are compared with other optimization algorithms such as Grey Wolf Optimizer (GWO), Particle Swarm Optimization (PSO), Whale Optimization Algorithm (WOA), and Dragonfly (DF). In addition, it is evaluated on ten real-world constrained engineering problems and shows strong capability to handle nonlinear and restricted optimization boundaries. Finally, HIO is applied to microgrid energy management for optimal scheduling of distributed sources under renewable uncertainty. The results show that HIO gives 2.87% lower total cost, 10.17% less greenhouse gas (GHG) emissions cost, and 10.26% better reliability than GWO. It also converges faster and gives more accurate results than PSO, indicating that it is stable and efficient for microgrid energy management.