Computing shortest paths in 2D and 3D memristive networks

TL;DR

This paper demonstrates how memristive networks can be used to efficiently compute multiple shortest paths in 2D and 3D networks, offering a novel approach to solving complex maze problems.

Contribution

It introduces a method for utilizing memristive networks to solve multiple shortest path problems simultaneously in 2D and 3D configurations.

Findings

Memristive networks can compute shortest paths in maze-like structures.

Simulations show effective shortest path solutions in 2D and 3D networks.

Potential applications in fields requiring efficient pathfinding.

Abstract

Global optimisation problems in networks often require shortest path length computations to determine the most efficient route. The simplest and most common problem with a shortest path solution is perhaps that of a traditional labyrinth or maze with a single entrance and exit. Many techniques and algorithms have been derived to solve mazes, which often tend to be computationally demanding, especially as the size of maze and number of paths increase. In addition, they are not suitable for performing multiple shortest path computations in mazes with multiple entrance and exit points. Mazes have been proposed to be solved using memristive networks and in this paper we extend the idea to show how networks of memristive elements can be utilised to solve multiple shortest paths in a single network. We also show simulations using memristive circuit elements that demonstrate shortest path computations in both 2D and 3D networks, which could have potential applications in various fields.