Thrust fluctuations, including electromagnetic and cogging forces, significantly limit the efficiency of linear oscillating actuators (LOAs) and hinder their ability to generate reciprocating driving forces and stroke ranges, presenting a complex multi-objective optimization challenge. This study introduces an innovative optimization strategy utilizing a hybrid surrogate model (HSM) in conjunction with an improved nondominated sorting genetic algorithm II (NSGA-II). The HSM integrates four complementary data-driven surrogate models through a heuristic weighting scheme based on the coefficient of determination, effectively capturing both nonlinear behaviors and local characteristics. This integration enhances the accuracy and robustness of motor performance predictions and facilitates the rapid generation of candidate solutions for optimal exploration within a broad design space. Furthermore, a modified NSGA-II improves the crowding degree model, thereby increasing the number of viable solutions in the target region. The proposed optimization strategy results in significant enhancements in average performance and a reduction in force fluctuations in LOAs, ultimately improving system controllability. Empirical validation through static and dynamic tests of the optimized prototype, coupled with finite element analysis, substantiates the efficacy of the HSM and the proposed optimization method. This research contributes a robust, data-driven framework for high-performance LOA design. The proposed strategy may offer insights for addressing similar complex multi-objective optimization problems in engineering.
Yu et al. (Sat,) studied this question.