JOURNAL OF SHANDONG UNIVERSITY (ENGINEERING SCIENCE) ›› 2015, Vol. 45 ›› Issue (1): 30-36.doi: 10.6040/j.issn.1672-3961.1.2014.212

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Parallel differential evolution algorithm for parameter learning of belief rule base

YANG Longhao1, FU Yanggeng1, GONG Xiaoting2   

  1. 1. College of Mathematics and Computer Science, Fuzhou University, Fuzhou 350116, Fujian, China;
    2. College of Economics and Management, Fuzhou University, Fuzhou 350116, Fujian, China
  • Received:2014-03-26 Revised:2014-10-15 Published:2014-03-26
  • Contact: 巩晓婷(1982-),女,河南漯河人,讲师,硕士,主要研究方向为不确定多准则决策,信息隐藏技术等

Abstract: To solve the problem of the existing parameter learning approaches for Belief Rule Base (BRB) were mainly serial algorithms, and those approaches were unsuitable for handling parameter optimization model under the big data. The differential evolution algorithm of swarm intelligence algorithms and the distributed method of cluster systems were introduced to the BRB, and then a parallel parameter learning approach using message passing interface was proposed. A numeric example of the pipeline leak detection problem was given. The new approach was compared with the existing parameter approaches in terms of the convergence error, the speedup ratio and the efficiency of parallel algorithm with different nodes of the cluster system. The experimental results showed that the approach was feasibilitiness and effectiveness.

Key words: message passing interface, pipeline leak detection, belief rule base, differential evolution algorithm, parallel algorithm, parameter learning, cluster system

CLC Number: 

  • TP18
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