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A Software Reliability Prediction Algorithm Based on MHPSO - BP Neural Network

机译：一种基于MHPSO - BP神经网络的软件可靠性预测算法

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摘要

Because the weights and thresholds of BP neural network usually adopt random assignment, there is a problem of low accuracy in software reliability prediction. In order to solve this problem, a software reliability prediction algorithm (MHPSO-BP) based on multi-layer heterogeneous PSO optimized BP neural network is proposed in this paper. In this algorithm, the population structure of the particle swarm is set to the hierarchical structure, and the velocity updating equation of the particle is improved by using the attractor. The information interaction between the particles is enhanced, and the optimization performance of the particle swarm optimization algorithm is improved. And then use the improved PSO to optimize the weight and threshold of the BP neural network. The software reliability prediction experiment was performed using the JM1 software defect data set of the NASA-MDP project during the experiment. The results show that the proposed method has better predictive performance than the traditional BP neural network.
机译：因为BP神经网络的权重和阈值通常采用随机分配，所以在软件可靠性预测中存在低精度的问题。为了解决这个问题，本文提出了一种基于多层异构PSO优化的BP神经网络的软件可靠性预测算法（MHPSO-BP）。在该算法中，将粒子群的群体结构设定为分层结构，通过使用吸引物改善粒子的速度更新等式。增强了粒子之间的信息相互作用，提高了粒子群优化算法的优化性能。然后使用改进的PSO来优化BP神经网络的权重和阈值。在实验期间使用NASA-MDP项目的JM1软件缺陷数据集进行软件可靠性预测实验。结果表明，该方法具有比传统的BP神经网络更好的预测性能。

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