Improvements for mlrose applied to the Traveling Salesperson Problem

TL;DR

This paper enhances the mlrose library's heuristic algorithms, specifically Genetic Algorithm and Hill Climbing, to produce shorter solutions for the Traveling Salesperson Problem by exploiting problem structure.

Contribution

It introduces generic improvements to mlrose's TSP optimization methods, resulting in more efficient solutions applicable beyond TSP.

Findings

Shorter tour lengths achieved with proposed improvements

Improvements are generic and not limited to TSP

Enhanced algorithms outperform original versions

Abstract

In this paper we discuss the application of Artificial Intelligence (AI) to the exemplary industrial use case of the two-dimensional commissioning problem in a high-bay storage, which essentially can be phrased as an instance of Traveling Salesperson Problem (TSP). We investigate the mlrose library that provides an TSP optimizer based on various heuristic optimization techniques. Our focus is on two methods, namely Genetic Algorithm (GA) and Hill Climbing (HC), which are provided by mlrose. We present improvements for both methods that yield shorter tour lengths, by moderately exploiting the problem structure of TSP. That is, the proposed improvements have a generic character and are not limited to TSP only.