The purpose of this paper is to present an algorithm for global optimization of high-dimensional real-parameter cost functions. This optimization algorithm, called Differential Ant-Stigmergy Algorithm (DASA), based on a stigmergy observed in colonies of real ants. Stigmergy is a method of communication in decentralized systems in which the individual parts of the system communicate with one another by modifying their local environment. The DASA outperformed the included differential evolution type algorithm in convergence on all test functions and also obtained better solutions on some test functions. The DASA may find applications in challenging real-life optimization problems such as maximizing the empirical area under the receiver operating characteristic curve of glycomics mass spectrometry data and minimizing the logistic leave-one-out calculation measure for the gene selection criterion. The DASA is one of the first ACO-based algorithms proposed for global optimization of the high-dimensional real-parameter problems.