In the last few years, hybridization has spread as an effective technique to solve hard optimization problems where metaheuristics algorithms have been unable to find global optima in a computational cost given. In this article, we propose the so-called cooperation strategy. This way is an alternative to hybridization in which different algorithms work together in order to find a global optimum following an intrinsically parallel approach. Different homogeneous and heterogeneous strategies using CEC benchmark functions have been designed using Brain Storm Optimization (BSO) metaheuristic in comparison with a hybrid BSO, showing that cooperation improves significantly hybridization results and the original BSO.
