## 5 Conclusions
(2021) [PERSON]; [PERSON]; [PERSON] Optimized exploitation of ancillary services: Compensation of reactive, unbalance and harmonic currents based on particle swarm optimization. _IEEE Lat. Am. Trans._**2021**, _19_, 314-325. [CrossRef]
* [PERSON] et al. (2021) [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON] Optimization of three-dimensional magnetic field in vacuum Interrupter using particle swarm optimization algorithm. _IEEE Trans. Appl. Supercond._**2021**, _31_, 5000804. [CrossRef]
* [PERSON] et al. (2020) [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON] PV maximum power-point tracking using modified particle swarm optimization under partial shading conditions. _Chin. J. Electr. Eng._**2020**, \(6\), 106-121. [CrossRef]* [PERSON] and [PERSON] (2019) [PERSON]; [PERSON] A hybrid taguchi binary particle swarm optimization for antenna designs. _IEEE Antennas Wirel. Propag. Lett._**2019**, _18_, 1581-1585. [CrossRef]
* [PERSON] et al. (2020) [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON] Fuzzy-based concept learning method: Exploiting data with fuzzy conceptual clustering. _IEEE Trans. Cybern._**2020**, _52_, 582-593. [CrossRef] [PubMed]
* [PERSON] (1979) [PERSON]; [PERSON] Fuzzy clustering with a fuzzy covariance matrix. In Proceedings of the 1978 IEEE Conference on Decision and Control Inclusing the 17 th Symposium on Adaptive Precesses, San Diego, CA, USA, 10-12 January 1979. * [PERSON] (2009) [PERSON] _Statistical Models: Theory and Practice_; Cambrige University: Cambridge, UK, 2009. * [PERSON] (1991) [PERSON]; [PERSON] Turing computability with neural nets. _Appl. Math. Lett._**1991**, \(4\), 77-80. [CrossRef]
* [PERSON] (1988) [PERSON]; [PERSON] Multivariable functional interpolation and adaptive networks. _Complex Syst._**1988**, \(2\), 321-355. * [PERSON] et al. (2020) [PERSON]; [PERSON]; [PERSON]; [PERSON] A granular computing-based hybrid hierarchical method for construction of long-term prediction intervals for gaseous system of steel industry. _IEEE Access_**2020**, \(8\), 63538-63550. [CrossRef]
* [PERSON] et al. (2015) [PERSON]; [PERSON]; [PERSON]; [PERSON] Hierarchical granular clustering an emergence of information granules of higher type and higher order. _IEEE Trans. Fuzzy Syst._**2015**, _23_, 2270-2283. [CrossRef]
* [PERSON] and [PERSON] (2022) [PERSON]; [PERSON] A design of CGK-based granular model using hierarchical structure. _Appl. Sci._**2022**, _12_, 3154. [CrossRef]
* [PERSON] et al. (2018) [PERSON]; [PERSON]; [PERSON] A design of granular Takagi-Sugeno fuzzy model through the synergy of fuzzy subspace clustering and optimal allocation of information granularity. _IEEE Trans. Fuzzy Syst._**2018**, _26_, 2499-2509. [CrossRef]
* [PERSON] et al. (2019) [PERSON]; [PERSON]; [PERSON]; [PERSON] Granular fuzzy modeling guided through the synergy of granulating output space and clustering input subspaces. _IEEE Trans. Cybern._**2019**, _51_, 2625-2638. [CrossRef]
* [PERSON] et al. (2020) [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON] Optimization and modeling of process parameters in multi-hole simultaneous drilling using taguchi method and fuzzy logic approach. _Materials_**2020**, _13_, 680. [CrossRef]
* [PERSON] et al. (2021) [PERSON]; [PERSON]; [PERSON] A review on genetic algorithm: Past present, and future. _Multimed. Tools Appl._**2021**, _80_, 8091-8126. [CrossRef] [PubMed]
* (47) Available online: [[http://lib.stat.cmu.edu/datasets/boston](http://lib.stat.cmu.edu/datasets/boston)]([http://lib.stat.cmu.edu/datasets/boston](http://lib.stat.cmu.edu/datasets/boston)) (accessed on 13 July 2022). * (48) UCI Machine Learning Repository, Concrete Compressive Strength Data Set. Available online: [[https://archive.ics.uci.edu/ml/datasets/Concrete+Compressive+Strength](https://archive.ics.uci.edu/ml/datasets/Concrete+Compressive+Strength)]([https://archive.ics.uci.edu/ml/datasets/Concrete+Compressive+Strength](https://archive.ics.uci.edu/ml/datasets/Concrete+Compressive+Strength)) (accessed on 13 July 2022).