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Hazem El Sadek, Xiaobing Zhang, Mahmoud Rashad, Cheng Cheng. Improvement of interior ballistic performance utilizing particle swarm optimization

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Hazem El Sadek, Xiaobing Zhang, Mahmoud Rashad, Cheng Cheng. Improvement of interior ballistic performance utilizing particle swarm optimization
Research Article. Mathematical Problems in Engineering. Hindawi Publishing Corporation. Volume 2014, Article ID 156103, 10 pages. http://dx.doi.org/10.1155/2014/156103.
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.
Content:
Introduction.
Problem formulation.
Formulation of particle swarm optimization algorithm.
Application of PSO algorithm to interior ballistic model.
Conclusion.
Acknowledgments.
References.
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