A Parallel Strategy for a Genetic Algorithm in Routing Wavelength Assignment Problem Using GPU with CUDA


This paper presents a parallel strategy with a heuristic approach to reduce the execution time bottleneck of a routing and wavelength assignment problem in wavelength-division multiplexing networks of a previous work that uses a sequential genetic algorithm. As the parallelization solution, the GPU hardware processing on CUDA architecture and CUDA C programming language were adopted. The results achieved were between 35 and 40 times faster than the sequential version of the genetic algorithm.

In Proceedings of 16th Encontro Nacional de Inteligência Artificial e Computacional 2020