Escaping Local Optima via Parallelization and Migration

We present a new nature-inspired algorithm, mt−GA ,w hich is a parallelized version of a simple GA, where subpopulations evolve in- dependently from each other and on different threads. The overall goal is to develop a population-based algorithm capable to escape from local optima. In doing so, we used complex trap functions, and we provide ex- perimental answers to some crucial implementation decision problems. The obtained results show the robustness and efficiency of the proposed algorithm, even when compared to well-known state-of-the art optimiza- tion algorithms based on the clonal selection principle.