In this paper, a model for Graphics Processing Unit (GPU) implementation of Particle Swarm Optimization (PSO) using digital pheromones to coordinate swarms within ndimensional design spaces is presented. Previous work by the authors demonstrated the capability of digital pheromones within PSO for searching n-dimensional design spaces with improved accuracy, efficiency and reliability in both serial and parallel computing environments using traditional CPUs. Modern GPUs have proven to outperform the number of floating point operations when compared to CPUs through inherent data parallel architecture and higher bandwidth capabilities. The advent of programmable graphics hardware in the recent times further provided a suitable platform for scientific computing particularly in the field of design optimization. However, the data parallel architecture of GPUs requires a specialized formulation for leveraging its computational capabilities. When the objective function computations are appropriately formulated for GPUs, it is theorized that the solution efficiency (speed) can be significantly increased while maintaining solution accuracy. The development of this method together with a number of multi-modal unconstrained test problems are tested and presented in this paper
