Improved Tasmanian devil optimization algorithm for parameter identification of electric transformers

Abstract

Tasmanian devil optimization (TDO) algorithm represents one of the most recent optimization algorithms that were introduced based on the nature behavior of Tasmanian devil behavior. However, as a recent optimizer, its performance may provide inadequate balance among the exploitation and exploration abilities, especially when dealing with the multimodal and high-dimensional natures of optimization tasks. To overcome this shortage, a novel variant of the TDO, called improved Tasmanian devil optimization (ITDO), is introduced in this paper. In ITDO, two competitive strategies are embedded into TDO to enrich the scope of the searching capability with the aim of improving the diversification and identification of the algorithm. The effectiveness of the ITDO algorithm is examined by validating its performance on CEC 2020 benchmark functions with different landscape natures. The recorded results proved that the ITDO is very competitive with other counterparts. After ITDO exhibited a sufficient performance, then, it was applied to estimate the parameters of the 1 kVA, 230/230 V, single-phase transformer. Some assessment metrics along with convergence analysis are conducted to affirm the performance of the proposed algorithm. The recorded results confirm the competitive performance of the proposed method in comparison with the other optimization methods for the benchmark functions and can identify the accurate parameters for the single-phase transformer as the estimated parameters by ITDO are highly coincident with the experimental parameters.