关键词: 软件体系结构, 性能优化, 差分演化, 代理模型

Abstract: A few of evolutionary algorithms for performance optimization at software architecture (SA) level have been proposed to obtain the near optimal performance improvement solutions in large search space, and the NSGA-II is the typical representative of these algorithms. However, most of these algorithms do not fully consider two factors: the performance improvement space with discrete feature and the performance evaluation with the high computational effort. As a result, the solutions obtained by these algorithms are not good and the corresponding processes of performance optimization are also considerably time-consuming. Aiming at this problem, this paper proposes a surrogate model assisted differential evolution algorithm for performance optimization at SA level, which is named by SMDE4PO. In the SMDE4PO, many strategies of crossover and mutation are adopted to enlarge the search space and accelerate the convergence. Furthermore, the random forest is regarded as surrogate model to substantially reduce the number of performance evaluation. The experimental results from four different size of cases show that (1) The SMDE4PO is significantly better than the NSGA-II in three indicators of contribution, generation distance and hyper volume; (2) By means of the random forest to predict performance indices, the run time of the SMDE4PO is reduced by 48% than the NSGA-II in the best results in the experiments.

Key words: software architecture, performance optimization, differential evolution, surrogate model