Space Net Optimization
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.
Cite
@article{arxiv.2306.00043,
title = {Space Net Optimization},
author = {Chun-Wei Tsai and Yi-Cheng Yang and Tzu-Chieh Tang and Che-Wei Hsu},
journal= {arXiv preprint arXiv:2306.00043},
year = {2023}
}
Comments
12 pages, 6 figures