The correct identification of partial discharge (PD) as an early indication of many insulation problems in electrical equipment is crucial for formulating maintenance plans which is an effective way to avoid catastrophic failure as the associated defects are treated at an early stage. The memory-based Multimodal multivariant optimization algorithm(MOA) is applied for PD fault identification based on an iterative global and local search to further improve the accuracy of partial discharge (PD) fault identification. However, the construction of the algorithm's search element has randomness and the setting of related parameters has high pertinence to the identification of complex PD faults in the actual environment. So This article proposes a novel adaptive multivariant optimization algorithm for PD recognition(AMOA).The first step is concerned with the PD data projection into different grids, in which the data may be removed from the data set if it has sparse local density and number of data peak density points are explored as potential PD fault categories if it has high density. After that, the memory-based MOA is applied to identify the PD fault based on an iterative global and local search. With a view to examining the validity of the proposed method, it is applied to the PD datasets of corona discharge, suspension discharge, air gap discharge, discharge along the surface in high-voltage equipment，as well as to the PD datasets of Insulator Surface discharge in GIS under actual operating conditions. The results show that it’s average recognition accuracy is 19.53%、13.04%、19.46%、37.18%、7.79%、8.13%and 4.19% higher than that obtained by the RDB, KPP, SVM-KNN, DPC-DLP, GWOKM, PSO, and MOA algorithms, respectively. It could be concluded that the proposed approach offers the advantages of high PD fault recognition for the electrical equipment.