A Custom Genetic Algorithm Framework for Early-Stage Optimization of Electromechanical Actuators

This work presents a systematic methodology for the preliminary design and optimization of electromechanical actuators, aimed at minimizing overall mass and rotational inertia while satisfying torque and speed requirements. The proposed approach integrates dimensionless scaling relationships, derived and corrected from catalog data, with a genetic algorithm that performs multi-parameter optimization across different actuator architectures. The algorithm enables the exploration of non-linear and multi-modal design spaces, allowing the identification of balanced solutions between mechanical efficiency and dynamic performance, employing custom functions for individual generation, constraint handling, and compatibility verification to ensure feasible and consistent architecture designs throughout the optimization process. A case study on the steering system of an aircraft nose landing gear illustrates the method's ability to define optimal design parameters in real mechanical systems. Linear and non-linear dynamic analyses confirmed the compliance of the optimized design with control and stability requirements. The study demonstrates how the developed custom constrained genetic optimization approach can effectively support the early design phase, reducing the computational effort required in further stages and improving the overall consistency of electromechanical actuator development.