We propose a heuristic global optimization technique which combines combinatorial and continuous local search. The combinatorial component, based on Reactive Search Optimization, generates a trajectory of binary strings describing search districts. Each district is evaluated by random sampling and by selective runs of continuous local search. A reactive prohibition mechanisms guarantees that the search is not stuck at locally optimal districts.

The continuous stochastic local search is based on the Inertial Shaker method: candidate points are generated in an adaptive search box and a moving average of the steps filters out evaluation noise and high-frequency oscillations.

The overall subdivision of the input space in a tree of non-overlapping search districts is adaptive, with a finer subdivision in the more interesting input zones, potentially leading to lower local minima.

Finally, a portfolio of independent CoRSO search streams (P-CoRSO) is proposed to increase the robustness of the algorithm.

An extensive experimental comparison with Genetic Algorithms and Particle Swarm demonstrates that CoRSO and P-CoRSO reach results which are fully competitive and in some cases significantly more robust.