Solving the Uncapacitated Single Allocation p-Hub Median Problem on GPU

TL;DR

This paper presents a GPU-based parallel genetic algorithm for the uncapacitated single allocation p-hub median problem, achieving superior solution quality and speed, and solving previously unsolved large instances.

Contribution

It introduces a GPU-accelerated genetic algorithm with tailored encoding and initialization techniques for this specific problem.

Findings

Outperforms existing heuristics in solution quality and runtime.

Successfully solves large instances up to 6000 nodes.

Provides high-quality solutions for previously unsolved problems.

Abstract

A parallel genetic algorithm (GA) implemented on GPU clusters is proposed to solve the Uncapacitated Single Allocation p-Hub Median problem. The GA uses binary and integer encoding and genetic operators adapted to this problem. Our GA is improved by generated initial solution with hubs located at middle nodes. The obtained experimental results are compared with the best known solutions on all benchmarks on instances up to 1000 nodes. Furthermore, we solve our own randomly generated instances up to 6000 nodes. Our approach outperforms most well-known heuristics in terms of solution quality and time execution and it allows hitherto unsolved problems to be solved.