This paper presents a solution to the global optimization of continuous functions by the Differential Ant-Stigmergy Algorithm (DASA). The DASA is a newly developed algorithm for continuous optimization problems, utilizing the stigmergic behavior of the artificial ant colonies. It is applied to the high-dimensional real-parameter optimization with low number of function evaluations. The performance of the DASA is evaluated on the set of 25 benchmark functions provided by CEC’2005 Special Session on Real Parameter Optimization. Furthermore, non-parametric statistical comparisons with eleven state-of-the-art algorithms demonstrate the effectiveness and efficiency of the DASA.