Speed-up of Self-Organizing Networks for Routing Problems in a Polygonal Domain

TL;DR

This paper introduces a multidimensional scaling approach to significantly accelerate self-organizing neural networks solving routing problems like the TSP in polygonal domains, demonstrating promising initial results.

Contribution

The paper proposes a novel multidimensional scaling method to speed up self-organizing networks for routing problems in polygonal spaces, applicable beyond TSP.

Findings

Significant reduction in computational time for TSP in polygonal domains.

Feasibility demonstrated with preliminary results.

Method is generalizable to other routing problem variants.

Abstract

Routing problems are optimization problems that consider a set of goals in a graph to be visited by a vehicle (or a fleet of them) in an optimal way, while numerous constraints have to be satisfied. We present a solution based on multidimensional scaling which significantly reduces computational time of a self-organizing neural network solving a typical routing problem -- the Travelling Salesman Problem (TSP) in a polygonal domain, i.e. in a space where obstacles are represented by polygons. The preliminary results show feasibility of the proposed approach and although the results are presented only for TSP, the method is general so it can be used also for other variants of routing problems.