Parallel Particle Swarm Optimization Algorithm Based On GPU-Accelerated With Its Applications

Particle Swarm Optimization (PSO) is an evolutionary computation technique inspired by social behavior of bird flocking. It has proven to be a powerful global optimization method and shows great potential in practice. However, it still needs plenty of computing time when it processes much data and when large-scale complicated work is involved in which math modeling and optimization are highly demanded, whereas parallel PSO comes into being and becomes a hit since it can reduce working-out time dramatically. Parallel PSO algorithm is mostly run on parallel machine and simulated by multi-thread technology, however, exists the following drawbacks: The consumption on communication between processes confines the particle-scales; Most researcher don't have the parallel machine equipments, therefore couldn't make use of parallel PSO algorism to solve problems; Multi-thread technology couldn't raise running performance because it runs on common pc with serial-parallel simulation.Graphics Processing Unit (GPU) has been developing rapidly in recent years, and as a result, various applications associated with computer graphics advance greatly. At the same time, the highly processing power, parallelism and programmability available nowadays on the contemporary GPU provide an ideal platform on which the general-purpose such as digital image processing computation could be made. Aiming at those problems in Parallel PSO algorithm, we raised a fine-grained PSO algorism based on GPU acceleration, which converts the process of working-out into the process of texture-rendering based on GPU, making PSO greatly accelerated in it. As achieving a good optimization effect, it also increases the particle population in the fine-grained parallelism, speeds up its running and provides ordinary user with a feasible PSO solution.Collision detection of deformable objects is one of the most important problems in the fields of robotics, computer animation, and virtual reality, etc. we proposed a collision detection algorithm based particle swarm optimization (PSO) and GPU for solving the problems as large updating data per frame and low efficiency in collision detection for deformable objects. The PSO algorithm is used for computing the distance between two nearest of all the two-dimensional discrete triangular patches instead of two objects, and minimizing the searching space and increasing the efficiency. The parallel particle swarm algorithm based GPU is advance at the same time, and is mapped the process of the texture rendering in GPU, and is used for the results in collision detection. It is shown clearly in experiment that the algorithm obtains righter results in computing the minimum distance and getting the collision position. It is feasible.