This paper describes the so-called Differential Ant-Stigmergy Algorithm (DASA), which is an extension of the Ant-Colony Optimization for continuous domain. A performance study of the DASA on a benchmark of real-parameter optimization problems is presented. The DASA is compared with a number of evolutionary optimization algorithms including covariance matrix adaptation evolutionary strategy, differential evolution, real-coded memetic algorithm, and continuous estimation of distribution algorithm. The DASA is also compared to some other ant-based methods for continuous optimization. The result obtained indicate a promising performance of the new approach.