A Diversified Multi-Start Algorithm for Unconstrained Binary Quadratic Problems Leveraging the Graphics Processor Unit

TL;DR

This paper presents a GPU-accelerated diversified multi-start algorithm for unconstrained binary quadratic problems, significantly improving solution quality and speed by leveraging parallel processing for initial solution generation and screening.

Contribution

It introduces a novel GPU-based multi-start approach with screening for binary quadratic problems, enhancing efficiency and solution diversity over traditional methods.

Findings

GPU-enabled multi-start yields faster results

The method produces higher quality solutions

Demonstrates superior performance on benchmarks

Abstract

Multi-start algorithms are a common and effective tool for metaheuristic searches. In this paper we amplify multi-start capabilities by employing the parallel processing power of the graphics processer unit (GPU) to quickly generate a diverse starting set of solutions for the Unconstrained Binary Quadratic Optimization Problem which are evaluated and used to implement screening methods to select solutions for further optimization. This method is implemented as an initial high quality solution generation phase prior to a secondary steepest ascent search and a comparison of results to best known approaches on benchmark unconstrained binary quadratic problems demonstrates that GPU-enabled diversified multi-start with screening quickly yields very good results.