Find in Library

Evolutionary algorithm is an important method for solving large-scale optimization problems, which is widely applied to machine learning, process control, engineering optimization, management science, and social sciences. However, when the traditional evolutionary algorithms are used to high-dimensional and computing-density task, the performance of corresponding applications is difficult to be satisfactory. Parallelization on supercomputer is a popular solution to solve this problem. This paper proposes a two-level parallel self-adaptive differential evolution algorithm with neighborhood search (SaNSDE) on the Sunway TaihuLight, which implements process-level and thread-level parallelism. In the process-level parallelism, the cooperative co-evolution model and pool model are implemented, which divide large-scale problems into multiple low-dimensional problems and distribute them in different processes. In the thread-level parallelism, fitness calculation is accelerated. Experimental results show that the algorithm using the cooperative co-evolution model and the pool model, compared with the traditional parallel algorithm, improves the convergence effect more obviously after multi-core expansion. Compared with the serial algorithm, the two-level parallel SaNSDE algorithm achieves the maximum speedup of 134.29, 186.05, 239.01 and 189.80 in the four benchmark functions, respectively.