A GPU-based genetic algorithm for the p-median problem

Abstract

The p-median problem is a well-known NP-hard problem. Many heuristics have been proposed in the literature for this problem. In this paper, we exploit a GPGPU parallel computing platform to present a new genetic algorithm implemented in Cuda and based on a Pseudo Boolean formulation of the p-median problem. We have tested the effectiveness of our algorithm using a Tesla K40 (2880 Cuda cores) on 290 different benchmark instances obtained from OR-Library, discrete location problems benchmark library, and benchmarks introduced in recent publications. The algorithm succeeded in finding optimal solutions for all instances except for two OR-library instances, namely pmed30 and pmed40, where better than 99.9% approximations were obtained.

A GPU-Based Genetic Algorithm for the Multiple Allocation P-Hub Median Problem

“…Moreover, it solved instances of problems so far unsolved (URAND instances of size up to 6000 nodes). Obviously, this program executed on a more powerful GPU such as the Nvidia-Kepler will give better results in time and quality and especially, it will allow to solve large hard instances which require the exhaustion of the entire search space AlBdaiwi and AboElFotoh (2017). Future studies might consider studying other versions of the hub location problem including robust and dynamic problems.…”

Customized Genetic Algorithm for Facility Allocation using p-median

“…To enable comparative studies of these methods, benchmarking data bases are used. The Operational Research (OR) library [8] and the Traveling Salesman Problem (TSP) [9] are the most used ones [6,7,10,11,12,13].…”