Optimization of Interval Type-2 Fuzzy Logic System Using Grasshopper Optimization Algorithm

The estimation of the fuzzy membership function parameters for interval type 2 logic system (IT2-FLS) is a challenging task in presence uncertainty and imprecision. Grasshopper optimization algorithm (GOA) fresh population based meta-heuristic that mimics swarming behavior grasshoppers nature, which has good convergence ability towards optima. main objective this paper to apply GOA estimate optimal Gaussian an IT2-FLS. antecedent part (Gaussian parameters) are encoded as artificial swarm optimized using its algorithm. Tuning consequent accomplished extreme learning machine. IT2-FLS (GOAIT2FELM) obtained premise on tuned then applied Australian national electricity market data forecasting loads prices. performance proposed model compared with other population-based including genetic bee colony Analysis performance, same data-sets, reveals GOAIT2FELM could be better approach improving accuracy variants