Gradient-Based Algorithms in the Brachistochrone Problem Having a Black-Box Represented Mathematical Model

Abstract

Trajectory optimization problems with black-box represented objective functions are often solved with the use of some meta-heuristic algorithms. The aim of this paper is to show that gradient-based algorithms, when applied correctly, can be effective for such problems as well. One of the key aspects of successful application is choosing, in the search space, a basis appropriate for the problem. In an experiment to demonstrate this, three simple adaptations of gradient-based algorithms were executed in the forty-dimensional search space to solve the brachistochrone problem having a blackbox represented mathematical model. This experiment was repeated for two different bases spanning the search space. The best of the algorithms, despite its very basic implementation, needed only about 100 iterations to find very accurate solutions. 100 iterations means about 2000 objective functional evaluations (simulations). This corresponds to about 20 iterations of a typical evolutionary algorithm, e.g. ES(µ,,,λ).