Improved snake optimization algorithm for parameter identification based on the genetic algorithm

Abstract

To address the issue that traditional snake optimization (SO) algorithms tend to become trapped in local optima when identifying aerodynamic parameters of high-spin projectiles – where complex flight dynamics and measurement noise further complicate the process – this paper proposes an enhanced snake optimization algorithm integrated with genetic algorithm (GA) mechanisms. Specifically, the improved algorithm incorporates GA-based selection and crossover operations into the SO framework, aiming to strengthen global search capability by simulating not only snakes’ natural foraging and combat behaviors but also the evolutionary characteristics of genetic algorithms. For handling noisy trajectory data, Kalman filtering is applied to denoise measured information, laying a reliable foundation for subsequent parameter identification. The method utilizes segmented trajectory data of high-spin projectiles across different speed stages for analysis. Comparative experiments with the traditional SO algorithm and other optimized variants demonstrate that the proposed approach reduces identification errors by 49%, significantly outperforming conventional methods in accuracy. Further validation with full trajectory measured data shows that when the identified aerodynamic parameters are substituted into ballistic equations, the deviation between calculated and actual impact point coordinates is minimal, confirming their effectiveness. Notably, the improved algorithm does not rely on precise initial parameter settings, enhancing its adaptability in practical scenarios. In summary, it provides a robust solution for accurately identifying projectile aerodynamic parameters and holds promises for engineering applications