A brief survey of advances in Particle Swarm Optimization on Graphic Processing Units

In the last few years, the Graphic Processing Units (GPUs) emerged as an exciting new hardware environment available for a truly parallel implementation and execution of Nature and Bio-inspired Algorithms. In contrast to common multicore CPUs that contain up to tens of independent cores, the GPUs represent a massively parallel single-instruction multiple-data (SIMD) devices that can nowadays reach peak performance of hundreds and thousands of giga FLOPS (floating-point operations per second). Nature and Bio-inspired Algorithms often adopt populational problem solving approaches and implement parallel optimization strategies in which group or groups of candidate solutions search for optimal solutions. Swarm Intelligence and Particle Swarm Optimization (PSO) in particular can be seen as multiagent methods in which the interaction of simple independent agents yields intelligent collective behavior. Such algorithms especially fit to the architecture of the GPUs. This survey provides a brief overview of the latest state-of-the-art research on the design, implementation, and applications of PSO-based methods on the GPUs.