Multi-Objective Optimization of Interior Ballistic Performance Using NSGA-II

This paper investigates an interior ballistic design with equal and unequal web thicknesses of seven-perforation propellant grains using optimization methods. In order to reveal the influence of the web thickness of the propellant grains on the overall interior ballistic performance, burning seven-perforation propellant grains with both equal and unequal web thickness is modeled. A currently popular evolution algorithm (EA) is used to compare two charge shapes, and to seek which one could achieve the optimal ballistic performance. Complete optimization of the interior ballistic performance is a complex process in view of the conflicting objectives to be achieved and a solution to such problems is sought by converting them into a single composite objective and using many tedious measurements. In this paper, a true multi-objective optimization of the interior ballistic charging design is carried out by considering three objectives simultaneously. The non-dominated sorting genetic algorithm version II (NSGA-II) is used to solve this multi-objective optimization problem (MOP). In order to check its implementation, both the conventional optimization algorithm-hill climbing method (HCM) and NSGA-II are used to solve the same single objective problem. The NSGA-II used to capture the full Pareto-optimal front is capable of identifying the trade-off among the conflicting objectives thereby providing alternative useful designs for a designer. Furthermore, for seven-perforation propellant grains, the results of using equal web thickness are compared with those of unequal web thickness, and it is shown that the two charge shapes produce no distinct difference in the interior ballistic performance.