Retracted: Study on Optimization of Interior Ballistic Performance of Cased Telescoped Ammunition Based on Improved Firefly Algorithm

Abstract

Considering that the evolution operators of common optimization algorithms such as genetic algorithm has a poor convergence speed and solution accuracy in the interior ballistic performance optimization of artillery, an improved firefly algorithm is selected for the interior ballistic performance optimization of cased telescoped ammunition (CTA). It is applied to the interior ballistic mathematical model of CTA to carry out single‐objective optimization and multi‐objective optimization respectively. The results show that in the single‐objective optimization, the muzzle velocity obtained by using the improved firefly algorithm is larger than other commonly used optimization algorithms. Under the constraints of maximum chamber pressure and relative burnout point, the muzzle velocity can be increased by about 30 m/s. In the multi‐objective optimization, three sub‐objectives of muzzle velocity, ballistic efficiency and muzzle pressure are considered respectively, and the multi‐objective firefly algorithm is applied to comprehensively optimize the interior ballistic performance of CTA. Then, the results are compared with the commonly used multi‐objective optimization algorithms, such as non‐dominated sorting genetic algorithm version II (NSGA‐II) and non‐dominated sorting genetic algorithm version III (NSGA‐III), the solution results are a series of Pareto optimal solutions. The obtained optimization results improved the interior ballistic performance and launch safety, providing reference for the reasonable matching of charge structure parameters and the selection of optimization algorithm upon the interior ballistic performance optimization of CTA to some extent.

Retracted: Interval uncertain optimization of cased telescoped ammunition interior ballistics considering tolerance design

“…In the performance optimization of the interior ballistics of the buried warhead, most researchers only consider the optimization under its deterministic conditions [10,11]. However, in practical engineering problems, there are inevitably errors or uncertainties related to initial conditions, boundary conditions, measurement and assembly deviations.…”