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According to the limitation of the interior ballistic charge design with genetic algorithms and some other direct optimization methods, which has complex evolution operators such as crossover and mutation or has poor perferance in solution accuracy and speed, a modified particle swarm optimizer is proposed which is based on a geometrical way and a fuzzy multi-objective optimization. The modified particle swarm optimizer is used to both single-objective and multi-objective optimization problems of interior ballistic charge design for a guided projectile. The solution results show that the modified particle swarm optimizer has a better convergence rate and accuracy than the original particle swarm optimizer and other ever used optimization methods. Combined with deterred propellant technique, the interior ballistic charge design for a guided projectile is optimized by the modified particle swarm optimizer. The optimization results improve the interior ballistic performance and launch safety and provide theoretical direction for the interior ballistic charge design of guided projectile.

This paper investigates the interior ballistic propelling charge design using the optimization methods to select the optimum charge design and to improve the interior ballistic performance. The propelling charge consists of a mixture propellant of seven-perforated granular propellant and one-hole tubular propellant. The genetic algorithms and some other evolutionary algorithms have complex evolution operators such as crossover, mutation, encoding, and decoding. These evolution operators have a bad performance represented in convergence speed and accuracy of the solution. Hence, the particle swarm optimization technique is developed. It is carried out in conjunction with interior ballistic lumped-parameter model with the mixture propellant. This technique is applied to both single-objective and multiobjective problems. In the single-objective problem, the optimization results are compared with genetic algorithm and the experimental results. The particle swarm optimization introduces a better performance of solution quality and convergence speed. In the multiobjective problem, the feasible region provides a set of available choices to the charge's designer. Hence, a linear analysis method is adopted to give an appropriate set of the weight coefficients for the objective functions. The results of particle swarm optimization improved the interior ballistic performance and provided a modern direction for interior ballistic propelling charge design of guided projectile.

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