Space Net Optimization

TL;DR

Space Net Optimization (SNO) introduces a novel mechanism called space net that enables metaheuristic algorithms to utilize most searched solutions, providing a comprehensive view of the solution landscape and improving performance on constrained problems.

Contribution

The paper proposes the space net mechanism for metaheuristics, allowing better use of searched solutions and enhancing optimization effectiveness.

Findings

SNO outperforms existing metaheuristics on benchmark problems.

The space net provides a 'vision' of the solution landscape.

SNO shows consistent improvements in convergence speed.

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.