Efficient static aeroelastic wing optimization based on PSO for preliminary design

Purpose This paper aims to present an efficient method for static aeroelastic wing optimization suitable for early-stage aircraft design. The focus is on integrating structural stiffness and aerodynamic performance to improve the glide ratio, using Particle Swarm Optimization (PSO) and low-fidelity simulation tools. Design/methodology/approach A parametric model of a flexible glider wing was developed, enabling simultaneous optimization of wing geometry and stiffness distribution using a custom (E)MOPTAX platform. Aeroelastic behavior was evaluated through an iterative coupling of vortex lattice aerodynamic models (AVL/XFOIL) and an Euler–Bernoulli beam-based structural model. Multiple design scenarios and load cases were explored using the PSO algorithm. Findings The results demonstrate that combining geometry and stiffness optimization yields significantly better aerodynamic performance than optimizing stiffness alone. Improvements in the lift-to-drag ratio of up to 20% were achieved across multiple flight conditions. This study also identifies trends in optimal stiffness distributions and shows the potential for reducing torsional discontinuities between wing segments. Research limitations/implications This study focuses on static aeroelasticity and low-fidelity modeling; dynamic phenomena such as flutter and high-fidelity simulations are not considered. Future research should address these limitations and incorporate manufacturing constraints, material stress limits and profile shape optimization. Practical implications The method offers a computationally efficient optimization framework that can be implemented in the early phases of aircraft design. It enables rapid trade-off analysis and supports preliminary decisions for wing configuration under aeroelastic constraints, especially when computational resources are limited. Originality/value This work introduces a novel, low-cost, parallelizable optimization approach that captures the coupled aerostructural behavior of flexible wings. It demonstrates the practical effectiveness of PSO in multi-parameter wing design and fills a gap in early-stage design tools for static aeroelastic optimization.