Optimization design of PMECC based on improved OBL and analytical decision-making multi-objective grey wolf optimization algorithm

In order to increase output torque while reducing eddy current loss as much as possible, this paper proposes a multi-objective grey wolf algorithm based on improved opposition-based learning (OBL) and analytical decisionmaking (IOAD-MOGWO), and applies this algorithm to the multi-objective optimal design of permanent magnet eddy current coupler (PMECC). Firstly, the Box-Behnken design method (BBD) is used to obtain the sample data required to construct the PMECC model, based on which the response surface methodology (RSM) is used to establish an approximate mathematical model about the structural parameters and performance indicators. Secondly, the IOAD-MOGWO algorithm is proposed, which solves the problems of local convergence and low search efficiency of the original multiobjective grey wolf algorithm by introducing the improved OBL strategy and the individual search strategy based on analysis and decision-making. Finally, a comparison of the optimization results with those obtained by the other two algorithms demonstrates the superior performance of the IOAD-MOGWO algorithm. In addition, the optimization results before and after the improvement are compared by means of COMSOL finite element simulation experiments. The simulation results show that compared with the original PMECC, the output torque of the optimized PMECC is greatly improved and the eddy current loss is significantly reduced, which proves the reliability of the optimization method.