Title: Space Net Optimization Show Chinese title

Title: 空间网络优化

Abstract: 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. Show Chinese abstract

Abstract: 大多数元启发式算法在收敛过程中依靠少量的搜索解来引导后续的搜索，这是出于一个简单的原因：计算机的计算资源有限，无法保留所有已搜索的解。 这也表明，大多数元启发式算法的每次搜索都类似于一种粗略的猜测。 为了解决这个问题，我们提出了一种新的元启发式算法，称为空间网优化（SNO）。 它配备了一种称为空间网的新机制，从而使元启发式算法能够利用所有已搜索解所提供的大部分信息来描绘解空间的地形。 有了空间网，元启发式算法就像在解空间上拥有了一种“视觉”。 仿真结果表明，在大多数情况下，SNO在一组著名的单目标约束问题上优于本研究中比较的其他所有元启发式算法。