GPU based parallel cooperative Particle Swarm Optimization using C-CUDA: A case study

The applications requiring massive computations may get benefit from the Graphics Processing Units (GPUs) with Compute Unified Device Architecture (CUDA) by reducing the execution time. Since the introduction of CUDA, applications from different areas have been benefited. Evolutionary algorithms are one such potential area where CUDA implementation proves to be beneficial not only in terms of the speedups obtained but also the improvement in convergence time. In this paper we present a detailed study of parallel implementation of one of the existing variants of Particle Swarm Optimization which is Cooperative Particle Swarm Optimization (CPSO). We also present a comparative study on CPSO implemented in C and C-CUDA. The algorithm was tested on a set of standard benchmark optimization functions. In this process, some interesting results related to the speedup and improvements in the time in convergence were obtained. The differences in randomizing procedures used in CUDA seem to contribute towards the diversity in population leading to better solution in contrast with the serial implementation. It also provides motivation for further research on neural network architecture and weight optimization using CUDA implementation. The results obtained in this paper therefore re-emphasize the utility of CUDA based implementation for complex and computationally intensive applications.