CLC number: TP181
On-line Access: 2023-12-04
Received: 2022-08-03
Revision Accepted: 2023-12-05
Crosschecked: 2023-01-08
Cited: 0
Clicked: 1831
Citations: Bibtex RefMan EndNote GB/T7714
https://orcid.org/0000-0002-8117-9764
https://orcid.org/0000-0001-5779-7135
https://orcid.org/0000-0003-2004-3289
Shaoqiang YE, Kaiqing ZHOU, Azlan Mohd ZAIN, Fangling WANG, Yusliza YUSOFF. A modified harmony search algorithm and its applications in weighted fuzzy production rule extraction[J]. Frontiers of Information Technology & Electronic Engineering, 2023, 24(11): 1574-1590.
@article{title="A modified harmony search algorithm and its applications in weighted fuzzy production rule extraction",
author="Shaoqiang YE, Kaiqing ZHOU, Azlan Mohd ZAIN, Fangling WANG, Yusliza YUSOFF",
journal="Frontiers of Information Technology & Electronic Engineering",
volume="24",
number="11",
pages="1574-1590",
year="2023",
publisher="Zhejiang University Press & Springer",
doi="10.1631/FITEE.2200334"
}
%0 Journal Article
%T A modified harmony search algorithm and its applications in weighted fuzzy production rule extraction
%A Shaoqiang YE
%A Kaiqing ZHOU
%A Azlan Mohd ZAIN
%A Fangling WANG
%A Yusliza YUSOFF
%J Frontiers of Information Technology & Electronic Engineering
%V 24
%N 11
%P 1574-1590
%@ 2095-9184
%D 2023
%I Zhejiang University Press & Springer
%DOI 10.1631/FITEE.2200334
TY - JOUR
T1 - A modified harmony search algorithm and its applications in weighted fuzzy production rule extraction
A1 - Shaoqiang YE
A1 - Kaiqing ZHOU
A1 - Azlan Mohd ZAIN
A1 - Fangling WANG
A1 - Yusliza YUSOFF
J0 - Frontiers of Information Technology & Electronic Engineering
VL - 24
IS - 11
SP - 1574
EP - 1590
%@ 2095-9184
Y1 - 2023
PB - Zhejiang University Press & Springer
ER -
DOI - 10.1631/FITEE.2200334
Abstract: Harmony search (HS) is a form of stochastic meta-heuristic inspired by the improvisation process of musicians. In this study, a modified HS with a hybrid cuckoo search (CS) operator, HS-CS, is proposed to enhance global search ability while avoiding falling into local optima. First, the randomness of the HS pitch disturbance adjusting method is analyzed to generate an adaptive inertia weight according to the quality of solutions in the harmony memory and to reconstruct the fine-tuning bandwidth optimization. This is to improve the efficiency and accuracy of HS algorithm optimization. Second, the CS operator is introduced to expand the scope of the solution space and improve the density of the population, which can quickly jump out of the local optimum in the randomly generated harmony and update stage. Finally, a dynamic parameter adjustment mechanism is set to improve the efficiency of optimization. Three theorems are proved to reveal HS-CS as a global convergence meta-heuristic algorithm. In addition, 12 benchmark functions are selected for the optimization solution to verify the performance of HS-CS. The analysis shows that HS-CS is significantly better than other algorithms in optimizing high-dimensional problems with strong robustness, high convergence speed, and high convergence accuracy. For further verification, HS-CS is used to optimize the back propagation neural network (BPNN) to extract weighted fuzzy production rules. Simulation results show that the BPNN optimized by HS-CS can obtain higher classification accuracy of weighted fuzzy production rules. Therefore, the proposed HS-CS is proved to be effective.
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