Ant-colony optimization (ACO) is a popular swarm intelligence scheme known for its efficiency in solving combinatorial optimization problems. However, despite some extensions of this approach to continuous optimization, high-dimensional problems remain a challenge for ACO. This paper presents an ACO-based algorithm for numerical optimization capable of solving high-dimensional real-parameter optimization problems. The algorithm, called the Differential Ant-Stigmergy Algorithm (DASA), transforms a real-parameter optimization problem into a graph-search problem. The parameters' differences assigned to the graph vertices are used to navigate through the search space. We compare the algorithm results with the results of previous studies on recent benchmark functions and show that the DASA is a competitive continuous optimization algorithm that solves high-dimensional problems effectively and efficiently.