Numerical optimization techniques are applied to a variety of engineering problems. The cost-function evaluation is an important part of any numerical optimization and is usually realized as a black-box simulator. For the efficient solving of the numerical optimization problem on multi-core systems, new shared-memory and distributed-memory approaches are proposed. The algorithms are based on an ant-stigmergy meta-heuristics, where indirect coordination between the ants drives the search procedure towards the optimal solution. Indirect coordination offers a high degree of parallelism and therefore relatively straightforward shared-memory and distributed-memory implementations. The Intel-OpenMP 3.0 and MPICH2 libraries are used for the inter-thread and inter-process communications, respectively. It is shown that speed-up strongly depends on the simulation time. This is especially evident in a distributed-memory implementation. Therefore, the algorithms’ performances, according to the simulator’s time complexity, are experimentally evaluated and discussed.