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

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