[1]LIN Yanqing,FU Yanggeng.NSGA-II-based EBRB rules activation multi-objective optimization[J].CAAI Transactions on Intelligent Systems,2018,13(3):422-430.[doi:10.11992/tis.201710012]
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NSGA-II-based EBRB rules activation multi-objective optimization

CAAI Transactions on Intelligent Systems[ISSN 1673-4785/CN 23-1538/TP] Volume: 13 Number of periods: 2018 3 Page number: 422-430 Column: 学术论文—智能系统 Public date: 2018-05-05
Title:
NSGA-II-based EBRB rules activation multi-objective optimization
Author(s):
LIN Yanqing;  FU Yanggeng
College of Mathematics and Computer Science, Fuzhou University, Fuzhou 350116, China
Keywords:
extended belief rule base (EBRB); inconsistency; activation rules; multi-objective optimization; NSGA-II algorithm
CLC:
TP18
DOI:
10.11992/tis.201710012
Abstract:
To address the low reasoning accuracy of extended belief rule-base (EBRB) systems with too many inconsistent activated rules, this paper introduces a fast elitist non-dominated sorting genetic algorithm (NSGA-Ⅱ) and proposes a rule activation multi-objective optimization approach based on the NSGA-Ⅱ algorithm. In this approach, binary coding is carried out for the activated rules whose activation weights are greater than zero. The inconsistent set of activated rules following synthetic reasoning and the sum of activation weights are taken as the objective function of the multi-objective optimization problem. Using the fast elitist non-dominated sorting genetic algorithm, the problem of a set of activation rules with a small inconsistency is solved, reducing the effect of the inconsistent activated rules on the reasoning accuracy of EBER systems. To validate the efficiency and feasibility of the proposed method, this paper introduces a nonlinear function and the proposed method was tested against the leak detection of an oil pipeline. The experimental results show that the rule activation multi-objective optimization approach based on NSGA-Ⅱ can effectively improve the reasoning performance of EBRB systems.
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