A new cooperation-based metaheuristic is proposed for searching global optima of functions. It is based on the assumption that the dynamics of the objective function does not change significantly between iterations. It relies on a local search process coupled with a cooperative semi-local search process. Its performances are compared against four other metaheuristics on unconstrained mono-objective optimization problems. Results show that the proposed metaheuristic is able to find the global minimum of the tested functions faster than the compared methods while reducing the number of iterations and the number of calls of the objective function.