Hybrid Optimization-Based GRU Neural Network for Software Reliability Prediction

• 作者: Junyu Lin Long Ren Maochuan Wu Shouchuang Shi Zhiwen Wang
• 来源期刊: 国际计算机前沿大会会议论文集
• 学科: 工学
• 关键词: Software reliability PSO GRU Prediction accuracy Generalization performance
• 年，卷（期）: 2020,（2）
• 页码范围: 369-383
• 页数: 15页
• 分类号: TP1

摘要

Aiming at the problems of low prediction accuracy and weak generalization ability of current reliability prediction models,this paper proposes a hybrid multi-layer heterogeneous particle swarm optimization algorithm(HMHPSO)that can simultaneously optimize the structure and parameters of the GRU neural network.It first introduced a multi-layer heteromass particle swarm optimization(MHPSO)algorithm,which sets the population topology as a hierarchical structure and introduces the concept of attractors,so as to improve the update formula of particle speed,and enhance the information interaction ability between particles,increase the diversity of the groups,thereby improving the optimization ability of the algorithm.Then the HMHPSO used the quantum particle swarm optimization(QPSO)algorithm to determine the structure of the GRU,that is,the number of hidden nodes.Experimental results show that the algorithm can generate GRU neural networks with high generalization performance and low architecture complexity,and has better prediction accuracy in software reliability prediction.