Space Net Optimization
This addresses a bottleneck in metaheuristic optimization for researchers and practitioners, though it appears incremental as it builds on existing metaheuristic frameworks.
The paper tackles the problem of metaheuristic algorithms relying on limited information from few solutions due to computing constraints, by introducing Space Net Optimization (SNO) with a space net mechanism that uses most information from all searched solutions to depict the solution space landscape, resulting in outperforming other algorithms in most cases for single objective bound constrained problems.
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.