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.

翻译：大多数元启发式算法在收敛过程中仅依赖少量已搜索的解来引导后续搜索，原因很简单：计算机有限的算力无法保留所有已搜索的解。这也揭示了大多数元启发式算法的每次搜索本质上都类似于粗略猜测。为解决这一问题，我们提出了一种新型元启发式算法——空间网络优化（SNO）。该算法配备了一种称为"空间网络"的新机制，使得元启发式算法能够利用所有已搜索解提供的大部分信息来刻画解空间的地形。借助空间网络，元启发式算法仿佛对解空间拥有了一种"视觉感知"。仿真结果表明，在针对一组经典单目标有界约束问题的多数测试案例中，SNO的性能优于本研究中所对比的所有其他元启发式算法。