Metaheuristic algorithms (MH's) are referred to algorithms which has a two-level design - 'meta' is upper-level procedure that controls the underlying 'heuristic' which learns and improves a solution iteratively until a sufficiently good solution is obtained for an optimization problem. Since 2008, MH's started to receive attention from researchers around the globe. Variants and new species of MH algorithms emerged. Most of them are claimed to be inspired from the nature or biology. The logics of the search algorithms are mimicked from animal behaviors or nature phenomenon. However, the necessity for creating more new species of such algorithms is doubted. Instead of inventing extra MH's which are similar to one another, we start to ponder if several classical MH's can be used together or in an ensemble. In this paper, the possibilities of putting several MH's into an ensemble are discussed. Different from ensemble in machine learning, we coin this unique collection of MH's which may fuse together or function cooperatively in solving optimization problems, 'meta-zoo-heuristic'. The term 'zoo' here simply means that the selected MH's are to be kept under control. A preliminary simulation test is conducted, which demonstrates how suitable MH's are selected for a specific problem to solve.