Double‐sided tubular machine (DSTM) is very suitable for wave energy conversion but easily suffers from high thrust ripple. In order to get the minimum cogging force with the maximum thrust force, a new DSTM with hybrid segmented permanent magnet array is proposed and optimised by a novel iterative few‐sample multi‐objective optimisation method. The novel optimisation method is based on an iterative Taguchi method framework to obtain optimal design with only few samples. To solve the low precision problem of the iterative Taguchi method, a surrogate‐model based multi‐objective optimisation algorithm that uses a general regression neural network, a speed‐constrained multi‐objective particle swarm optimisation and an exponentially weighted moving average are embedded into this framework. The optimisation result is compared with other alternative topologies and methods, and a prototype is manufactured for testing experiment.

Double-sided tubular machines (DSTM) have greatly improved space utilization and power density, and are widely used in tidal power generation and high-power electric drives, while the thrust force ripple is also significant. To get the minimum cogging force with the maximum thrust force, hybrid segmented PM array topology is introduced to the DSTM and used as research object for multi-objective optimization. Due to the constraints among the parameters to be optimized, this paper proposes a new constrained multi-parameter multi-objective optimization method. In this method, a new optimal Latin hypercube(OLH) is put forward to solve the constrained parameter sampling problem. RReliefF is used to distinguish the importance of multi-parameter and divide them into three layers: strong, medium and weak, and surrogate models are established hierarchically to simplify the optimization complexity. In addition, an improved Gaussian process regression(GPR) algorithm is proposed to improve the optimization accuracy of strong significance parameters. The optimization results show that the average thrust force is increased by 4.7%, the cogging force is reduced by 28%, and total harmonic distortion rate of the voltage is reduced by 30.5% without loss of efficiency, which significantly improves the performance of the machine, and verifies the effectiveness of the proposed method, which is suitable for constrained multi-parameter multi-objective optimization.INDEX TERMS Double-sided tubular machine, segmented permanent magnet, constrained, multi-objective optimization.