Many real-world problems are dynamic, requiring an optimization algorithm which should not only be able to locate the optimum, as it does in the static sense, but also be capable of detecting when the environment changes and tracking the new optimum. This paper presented the differential ant-based stigmergy algorithm (DASA) developed for numerical optimization problems. The DASA was applied to dynamic optimization problems with continuous variables proposed for the special session on evolutionary computation in dynamic and uncertain environments at the 2009 IEEE Congress on Evolutionary Computation. The results showed that the DASA can find reasonable solutions for all of the problems. One obvious advantage is that was no need any changes to the original algorithm. So, it can be used as such for both cases of numerical optimization, static and dynamic. Furthermore, the DASA is unsusceptible to different types of changes and can be used with very limited knowledge about problem, only maximal dimension and input problem parameters. The performance of the DASA is compared with four algorithms: a clustering particle swarm algorithm, a self-adaptive differential evolution, an evolutionary programming with an ensemble of memories, and with a dynamic artificial immune algorithm. It can be seen that the DASA performs not much worse than a self-adaptive differential evolution and much better than the other three algorithms.