TY  - EJOU
AU  - Liu, Xue-Wei 
AU  - Ye, Shao-Qiang 
AU  - Qin, Feng 
AU  - Zhou, Kai-Qing 

TI  - Weighted Fuzzy Production Rule Extraction Utilizing an Improved Grey Wolf Optimizer
T2  - Computers, Materials \& Continua

PY  - 
VL  - 
IS  - 
SN  - 1546-2226

AB  - Weighted fuzzy production rules (WFPRs) provide superior expressiveness and interpretability in knowledge engineering area. However, manual construction of WFPRs is labor-intensive, time-consuming, and inherently subjective, which greatly restricts their practical application. The back propagation neural network (BPNN) has been widely adopted for automatic WFPR extraction. Nevertheless, its high sensitivity to initial weight configurations frequently results in premature convergence to local optima, generating redundant, poorly interpretable rule sets that compromise the inherent interpretability advantage of WFPRs. This paper proposes an elite dynamic scout-guided grey wolf optimizer (EDSG-GWO) and integrates it into a BPNN-based WFPR extraction framework to optimize network initial weights. The EDSG-GWO incorporates a nonlinear convergence factor, dynamic weighted position updating, an elite opposition-based learning mechanism, and an adaptive scout bee perturbation strategy to effectively balance global exploration and local exploitation. Unlike existing GWO variants, the EDSG-GWO achieves the synergistic integration of the four above strategies, collectively enhancing convergence accuracy and exploration capability. Numerical experiments are conducted on twelve benchmark functions. The results demonstrate that the EDSG-GWO delivers competitive optimization accuracy and convergence speed. Validated on the PIMA Indians Diabetes Database, the optimized BPNN attains a test accuracy of 72.92%, which is comparable to other metaheuristic-based approaches. More notably, the extracted WFPRs reach an accuracy of 77.08%, outperforming the baseline method by a notable margin. The four extracted rules involve merely six core diagnostic features, whose weight distributions are highly consistent with established medical knowledge. This contributes to a concise, clinically plausible, and highly interpretable rule set for auxiliary diabetes diagnosis. Further validation on the Breast Cancer Wisconsin dataset yields a WFPRs testing accuracy of 94.15%, confirming the framework’s generalizability.
KW  - Grey wolf optimizer; function optimization; back propagation neural network; weighted fuzzy production rule extraction

DO  - 10.32604/cmc.2026.085316