[Retracted] Taxonomy of Adaptive Neuro-Fuzzy Inference System in Modern Engineering Sciences

Table 3

Optimization algorithm hybrids with their ANFIS counterparts.

Model	Variants
ANFIS-ABC [10]	ANFIS-aABC (adaptive ABC) [28, 29], ANFIS-Scoutless ABC [35]
ANFIS-ACO [11]	ANFIS-ACOr (ACO for continuous domains) [36, 37], ANFIS-ACS (ant colony system) [38]
ANFIS-BA [12]	HBA (hybrid bat algorithm)-ANFIS [39]
ANFIS-DE [20]	ANFIS-DEACS (differential evolution with ant colony search) [40]
ANFIS-FFA [21]	WT (wavelet transform)-ANFIS-HFPSO (hybrid FF and PSO) [41], MFA (modified FFA)-ANFIS-P&O (perturbation and observation) [42]
ANFIS-GA [22]	ANFIS-NSGAII (nondominated sorting GA-II) [35, 43]
ANFIS-HS [24]	ANFIS-GHS (global-best HS) [44]
ANFIS-PSO [28]	ANFIS-QPSO (quantum PSO) [45], ANFIS-QPSO-ADCEC (adaptive dynamical CE coefficient) [46], PSO-ANFIS-FFRLS (forgetting factor recursive least square) [47], ANFIS-adaptive weighted PSO [48], DyHAP (dynamic hybrid ANFIS-PSO) [49], wavelet-PSO-ANFIS [50], ANFIS-APAPSO (adaptive population activity PSO) [51]
ANFIS-SA [29]	ANFIS-RCSA (real-coded SA) [52]
ANFIS-SC [31]	TS (tabu search)-SC-ANFIS [53], ANFIS-FCM (fuzzy C-means) [54]

ANFIS, when hybridized with optimization algorithms, extends its error-handling capability, for accurate weight updating.