  This paper analyzes the performance of the Fractal Decomposition Algorithm
(FDA) metaheuristic applied to low-dimensional continuous optimization
problems. This algorithm was originally developed specifically to deal
efficiently with high-dimensional continuous optimization problems by building
a fractal-based search tree with a branching factor linearly proportional to
the number of dimensions. Here, we aim to answer the question of whether FDA
could be equally effective for low-dimensional problems. For this purpose, we
evaluate the performance of FDA on the Black Box Optimization Benchmark (BBOB)
for dimensions 2, 3, 5, 10, 20, and 40. The experimental results show that
overall the FDA in its current form does not perform well enough. Among
different function groups, FDA shows its best performance on Misc. moderate and
Weak structure functions.
