Space Net Optimization

• URL: http://arxiv.org/abs/2306.00043v1
• Date: Wed, 31 May 2023 15:44:18 GMT
• Title: Space Net Optimization
• Authors: Chun-Wei Tsai, Yi-Cheng Yang, Tzu-Chieh Tang and Che-Wei Hsu
• Abstract summary: Most metaheuristic algorithms rely on a few searched solutions to guide later searches during the convergence process. 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.
• Score: 1.0323063834827415
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• 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.

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