A review of PSO-GA hybrid optimization algorithm in aero-engine quality assessment

As the core power unit of aircraft, the quality assessment of aero-engines is of great significance for ensuring flight safety. Traditional quality assessment methods often struggle to handle complex system problems characterized by multi-parameter coupling and strong nonlinearity. Particle Swarm Optimization (PSO) and Genetic Algorithm (GA), as two typical intelligent optimization algorithms, have demonstrated unique advantages in their respective fields but also exhibit obvious limitations. In recent years, PSO-GA hybrid optimization algorithms have made significant progress in the field of aero-engine quality assessment by integrating the advantages of both algorithms. This paper systematically reviews the basic principles of PSO-GA hybrid optimization algorithms, hybrid strategies, and typical applications in aero-engine quality assessment. It analyzes current research problems and challenges, and looks forward to future development trends, providing references for related research.