  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.
