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In this paper, two pairs of embedded Runge-Kutta (RK) type techniques for straightforwardly tackling third-order ordinary differential equations (ODEs) of the form v″′ = f(x, v, v′) signified as RKTGD strategies were proposed and explored. Relying on the order conditions, the primary pair with mathematical order 4 and 3 was called RKTGD4(3), while different has order 5 and 4, and was named RKTGD5(4). The new strategies were determined so that the higher-order techniques were exact and the lower order techniques would bring about the best error estimates. At that point, variables step-size codes were created to support the methods and utilized in solving a lot of third-order problems. Comparisons were made between mathematical results and existing embedded RK pairs within the scientific literature, that require the problems to be reduced into a system of first-order ODEs, and the effectiveness of the new RKTGD pairs have appeared.

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