GPU-Based Parallel Particle Swarm Optimization Methods for Graph Drawing

Qu, Jianhua
Liu, Xiyu
Sun, Minghe
Qi, Feng
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Particle Swarm Optimization (PSO) is a population-based stochastic search technique for solving optimization problems, which has been proven to be effective in a wide range of applications. However, the computational efficiency on large-scale problems is still unsatisfactory. A graph drawing is a pictorial representation of the vertices and edges of a graph. Two PSO heuristic procedures, one serial and the other parallel, are developed for undirected graph drawing. Each particle corresponds to a different layout of the graph. The particle fitness is defined based on the concept of the energy in the force-directed method. The serial PSO procedure is executed on a CPU and the parallel PSO procedure is executed on a GPU. Two PSO procedures have different data structures and strategies. The performance of the proposed methods is evaluated through several different graphs. The experimental results show that the two PSO procedures are both as effective as the force-directed method, and the parallel procedure is more advantageous than the serial procedure for larger graphs.

Qu, J., Liu, X., Sun, M., & Qi, F. (2017). GPU-Based Parallel Particle Swarm Optimization Methods for Graph Drawing. Discrete Dynamics in Nature and Society, 2017, 2013673. doi:10.1155/2017/2013673
Management Science and Statistics