Adaptive Initial Condition Search for Projectile Motion Simulations with Locally Sparse Terminal Conditions

This study presents an adaptive search algorithm to effectively sample the initial conditions of projectiles exhibiting a locally sparse distribution. The method introduces two key concepts: parameter setting based on the physical characteristics of projectile motion and adaptive adjusting of the explosion radius to tackle the challenges inherent in the locally sparse distribution. Using these concepts with parallel processing, the algorithm significantly reduces unnecessary computations compared to conventional resolution refinement search algorithms. The results reveal that the proposed algorithm samples the optimal values approximately 40 times faster than the comparison algorithm, and also shows reliable and robust performance across various target ranges and different types of projectiles.