This paper presents dynamic version of the tree growth algorithm. Tree growth algorithm is a novel optimization approach that belongs to the group of swarm intelligence metaheuristics. Only few papers addressed this method so far. This algorithm simulates the competition between the trees for resources such as food and light. The dynamic version of the tree growth algorithm introduces dynamical adjustment of exploitation and exploration search parameters. The efficiency and robustness of the proposed method were tested on a well-known set of standard global unconstrained benchmarks. Besides numerical results obtained by dynamic tree growth algorithm, in the experimental part of this paper, we have also shown comparative analysis with the original tree growth algorithm, as well as comparison with other methods, which were tested on the same benchmark set. Since many problems from the domains of industrial and service systems can be modeled as global optimization tasks, dynamic tree growth algorithm shows great potential in this area and can be further adapted for tackling many real-world unconstrained and constrained optimization challenges.