基于混合PAES的置信规则库推理算法

Belief Rule Base Inference Algorithm Based on Mixed Pareto Archived Evolutionary Strategy

目前对置信规则库(BRB)的研究主要是关于BRB系统的参数或结构的单目标优化。然而,BRB系统中提高推理准确性和减少系统复杂度往往是两个相互冲突的目标。因此,设计合适算法来寻找到两个目标上的最优解具有重要意义。鉴于此,该文提出基于混合Pareto存档进化策略(M-PAES)的置信规则库推理方法(M-PAES-BRB),通过最小化系统的均方根误差和系统复杂性来寻找到近似的Pareto最优前沿。该算法采用了改进型M-PAES算法来构建多目标优化模型,通过重组和变异操作生成候选解。该文选取两个标准时间序列,Mackey-Glass和Box-Jenkins作为实验数据,对M-PAES-BRB的可行性及有效性进行分析。实验结果表明,相比于模糊规则库的多目标优化方法(FRBSs),该文方法的推理准确性更高,同时系统复杂度更低。

Most of the existing methods for belief rule based(BRB) focus on single objective optimization for parameter or structure. However, according to the existing research, improving reasoning accuracy and reducing the complexity of BRB system usually conflict each other. Thus, designing a suitable algorithm to find right trade-off for the two goals is necessary. For this purpose, an algorithm named M-PAES-BRB(belief rule base inference method based on multi-objective optimization) is proposed to determine an approximation of the optimal Pareto front by concurrently minimizing the root mean squared error and the complexity. The algorithm adopts an improved mixed pareto archived evolutionary strategy(M-PAES) to build a multi-objective optimization model,M-PAES use recombination operator and mutation operator to generate candidate solutions. In the experiment, we select two standard time series, Mackey-Glass and Box-Jenkins as the experimental datasets, to test the feasibility and effectiveness of M-PAES-BRB. Compared to fuzzy rule base multi-objective evolutionary algorithms(FRBSs),the experiment results show that M-PAES-BRB’s reasoning accuracy is higher and the complexity is lower.