The Application of Improved Ant Colony Algorithm in the Optimal Design of Actuator Stiffness

The stiffness based design is more and more widely used in the development of civil aircraft actuators. The stiffness, strength, weight and other performance parameters restrict each other, so it is necessary to find a parameter optimization method to coordinate this constraint relationship. In this paper, based on the real coding and the traditional ant colony algorithm, the population diversity index is introduced, and the adaptive optimization of crossover probability and mutation probability is used to improve the global search ability of the ant colony algorithm with the idea of genetic algorithm. Taking the stiffness design of the piston of an actuator as an example, the improved ant colony algorithm of this paper is realized by MATLAB programming. The results show that the improved ant colony algorithm can find a group of solutions that meet the performance requirements, and the convergence speed and optimization ability of the algorithm are better than the traditional ant colony algorithm. Thus, a parameter optimization method for actuator stiffness optimization design is found.