Dynamic selection of zero-revolution Lambert algorithms using performance comparison map

This paper proposes a dynamic algorithm selection procedure to accelerate the Lambert's problem routine composed of two phases — the preparation phase and the execution phase. In the preparation phase, a performance comparison framework for different Lambert algorithms using their characteristic parameters and cost formulae is used to generate a performance map containing the information on best algorithms for test problem instances. In the execution phase, the best algorithm for the given problem instance is selected based on the algorithm selection parameters and the performance map created in the preparation phase, and the instance is solved using the selected algorithm. The comparison results of experiments to solve a large number of test problem instances using the proposed procedure and the traditional static algorithm selection for three popular Lambert algorithms demonstrate the effectiveness of the dynamics algorithm selection.