System parameter optimization of solid propellant rocket motors based on ant colony algorithm

In order to construct an optimization algorithm supporting continuous and discrete mixed-variables for system parameter optimization of solid propellant rocket motors, three strategies, mesh strategy, local search and dummy strategy were merged into basic ant colony algorithm to improve optimization performance and search precision, where the local search algorithm itself was still an ant colony algorithm. Several classical optimization algorithm testing functions, which are very entrapping were tested to verify the performance of the algorithm. The results indicate that probability is high for the algorithm to find global optimum value. The improved algorithm was applied to solve the two important system parameters, combustion chamber pressure and nozzle expansion ratio, of solid propellant rocket motor system design, and the results are satisfactory. All the results indicate improvements in three aspects, which are supporting mixed-variables, steady global optimization performance and higher search precision, respectively. Thus, the improved ant colony algorithm presents better optimization performance and better adaptability to engineering optimization design problems.