Multi-swarm PSO algorithm for the Quadratic Assignment Problem: a massive parallel implementation on the OpenCL platform

TL;DR

This paper introduces a multi-swarm PSO algorithm optimized for parallel execution on OpenCL, demonstrating improved efficiency and solution quality for large populations tackling the Quadratic Assignment Problem.

Contribution

The paper presents a novel multi-swarm PSO implementation on OpenCL with migration options, optimized for large populations and parallel processing, enhancing solution quality for QAP.

Findings

Parallel implementation significantly reduces computation time.

Large populations improve the probability of near-optimal solutions.

Migration between swarms enhances optimization performance.

Abstract

This paper presents a multi-swarm PSO algorithm for the Quadratic Assignment Problem (QAP) implemented on OpenCL platform. Our work was motivated by results of time efficiency tests performed for single-swarm algorithm implementation that showed clearly that the benefits of a parallel execution platform can be fully exploited, if the processed population is large. The described algorithm can be executed in two modes: with independent swarms or with migration. We discuss the algorithm construction, as well as we report results of tests performed on several problem instances from the QAPLIB library. During the experiments the algorithm was configured to process large populations. This allowed us to collect statistical data related to values of goal function reached by individual particles. We use them to demonstrate on two test cases that although single particles seem to behave chaotically during the optimization process, when the whole population is analyzed, the probability that a particle will select a near-optimal solution grows.