  Most metaheuristic algorithms rely on a few searched solutions to guide later
searches during the convergence process for a simple reason: the limited
computing resource of a computer makes it impossible to retain all the searched
solutions. This also reveals that each search of most metaheuristic algorithms
is just like a ballpark guess. To help address this issue, we present a novel
metaheuristic algorithm called space net optimization (SNO). It is equipped
with a new mechanism called space net; thus, making it possible for a
metaheuristic algorithm to use most information provided by all searched
solutions to depict the landscape of the solution space. With the space net, a
metaheuristic algorithm is kind of like having a ``vision'' on the solution
space. Simulation results show that SNO outperforms all the other metaheuristic
algorithms compared in this study for a set of well-known single objective
bound constrained problems in most cases.
