Re-sampling search: A seriously simple memetic approach with a high performance

In the fashion of the Ockham´s Razor principle for Memetic Computing approaches, this paper proposes an extremely simple and yet very efficient algorithm composed of two operators. The proposed approach employs a deterministic local search operator that periodically perturbs by means of a stochastic search component. The perturbation occurs by re-sampling the initial solution within the decision space. The deterministic local search is stopped by means of a precision based criterion and started over by means of the stochastic re-sampling. Although the concept of multi-start local search is not new in the optimization environment the proposed algorithm is shown to be extremely efficient on a broad set of diverse problems and competitive with complex algorithms representing the-state-of-the-art in computational intelligence optimization.