Modified Adaptive Neuro-Fuzzy Inference System Trained by Scoutless Artificial Bee Colony

Mohd Najib Mohd Salleh, Norlida Hassan, Kashif Hussain, Noreen Talpur, Shi Cheng

Research output: Chapter in Book/Report/Published conference proceedingConference contributionpeer-review


Neuro-fuzzy systems have produced high accuracy in modeling numerous real-world applications. However, the in-built computational complexity and curse of dimensionality often cease opportunities of implementations in applications with large input size. This is also true with adaptive neuro-fuzzy inference system (ANFIS) as mostly the applications in literature are with small input size. The five-layer architecture of ANFIS is modified in this paper to reduce computational cost. For effective parameters training, the popular swarm-based metaheuristic algorithm Artificial Bee Colony (ABC) algorithm is employed after modification for enhanced convergence ability. The proposed ABC variant eliminates scout bees, hence called ABC-Scoutless, outperforms standard ABC and particle swarm optimization (PSO) on benchmark test functions. The modified ANFIS trained by ABC-Scoutless performs equally better as standard ANFIS on benchmark classification problems with different input range, but with less computational cost due to reduced number of trainable parameters.
Original languageEnglish
Title of host publicationAdvances in Information and Communication Networks
Subtitle of host publicationProceedings of the 2018 Future of Information and Communication Conference (FICC), Vol. 2
EditorsKohei Arai, Supriya Kapoor, Rahul Bhatia
Number of pages12
ISBN (Electronic)978-3-030-03405-4
ISBN (Print)978-3-030-03404-7
Publication statusPublished - 27 Dec 2018

Publication series

NameAdvances in Intelligent Systems and Computing

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