Abstract
Purpose This paper aims to propose a reliable local search algorithm having steepest descent pivot rule for computationally expensive optimization problems. In particular, an application to the design of Permanent Magnet Synchronous Motor PMSM drives is shown. Designmethodologyapproach A surrogate assisted HookeJeeves algorithm SAHJA is proposed. The SAHJA is a local search algorithm with the structure of the HookeJeeves algorithm, which employs a local surrogate model dynamically constructed during the exploratory move at each step of the optimization process. Findings Several numerical experiments have been designed. These experiments are carried out both on the simulation model offline and at the actual plant online. Moreover, the offline experiments have been considered in nonnoisy and noisy cases. The numerical results show that use of the SAHJA leads to a saving in terms of computational cost without requiring any extra hardware components. Originalityvalue The surrogate approach in the design of electric drives is novel. In addition, implementation of the proposed surrogate model allows the algorithm not only to reduce computational cost but also to filter noise caused by the sensors and measurement devices.