Tuning fuzzy logic controller with SGWO for river water quality modelling

Abstract

Particle Swarm Optimization (PSO) algorithm is amongst the prominent population dependent search procedure and was originally established on the communal conduct and intelligence of birds inside a swarm. Further, PSO algorithm is used as a prominent tuning tool for fuzzy logic controllers. Nevertheless, it still experiences abundant difficulties while dealing with Mamdani’s fuzzy logic controller. In the present work, we are analyzing the water quality of river Yamuna for the years 2018–2019 using Mamdani’s fuzzy logic controller and an adjustable hybrid variant, Swarmed Grey Wolf Optimizer has been employed to tune membership function’s parameters of the fuzzy logic controller. We validate our proposed approach by comparing similar fuzzy systems tuned with PSO and Grey Wolf Optimizer in terms of Root Mean Square Error. The outcomes of the investigation revealed that the proposed model is effective and promising in the field of water quality modelling.