Abstract
A key component of particle swarm optimization algorithms is pseudorandom number generators (PRNGs) which provide random numbers to drive the stochastic search process. In this paper, we implemented ten PRNGs on CPUs and graphics processing units (GPUs). We present the effect of PRNGs on a parallel implementation of the standard particle swarm optimization on a GPU. The performance of SPSO algorithms is influenced by the quality of the PRNGs running on a GPU. By using the combined Tausworthe PRNG, the proposed parallel implementation of SPSO provides up to 307 times speedup compared to a serial CPU SPSO implementation. Speedup is greatly accelerated for high dimension, large particles and complex benchmark functions. Here, the experiments were conducted on well-known six benchmark functions. Consequently, this implementation can be widely used in real optimizing problems.
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