Weighted Fuzzy Production Rule Extraction Utilizing an Improved Grey Wolf Optimizer
Xue-Wei Liu1, Shao-Qiang Ye2, Feng Qin3, Kai-Qing Zhou1,*
1 College of Computer Science and Engineering, Jishou University, Jishou, China
2 Faculty of Computing, Universiti Teknologi Malaysia, Skudai, Johor Bahru, Malaysia
3 School of Computer and Artificial Intelligence, Huaihua University, Huaihua, China
* Corresponding Author: Kai-Qing Zhou. Email: 
Computers, Materials & Continua https://doi.org/10.32604/cmc.2026.085316
Received 08 May 2026; Accepted 14 July 2026; Published online 05 August 2026
Abstract
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.
Keywords
Grey wolf optimizer; function optimization; back propagation neural network; weighted fuzzy production rule extraction
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