# Optimization of a Multi-Type PMSM Based on Pyramid Neural Network
## 1 Introduction  
The development of material technology has promoted the analysis and synthesis of modern structures of permanent magnet excited machines. Researchers have worked on improving machine parameters affecting magnetic, thermal and mechanical properties to allow for a greater density of energy. Compared with ordinary motors, high-speed permanent magnet synchronous motors (HPMSMs) [1] have obvious advantages such as a high speed, small geometric size, large power density and small moment of inertia [2]. HPMSMs can be directly connected with elevated-speed loads, which can eliminate traditional mechanical transmission devices, and they not only reduce mechanical noise but also improve the efficiency of the transmission system [3]. HPMSMs have broad application prospects in centrifugal compressors, flywheel energy storage, high speed grinders, hybrid electric vehicles, aviation, marine systems, distributed power generation systems and other fields [4]. However, under the collective influence of temperature, armature reaction, mechanical vibration and other factors, a serious temperature rise in the inner components of the motor will cause demagnetization of the permanent magnet [5]. This will lead to the degradation of the motor's performance and insufficient output. Heavy loss in an HPMSM will make it unable to drive the load and will threaten the safety of the system [6]. To obtain better performance from an HPMSM, developing its optimized construction has become a research hotspot [7]. Compared with the traditional algorithm, the concept of the meta-heuristic optimization algorithm is simple, easy to implement and can optimize the problem without requiring a lot of gradient information. Therefore, in recent years, meta-heuristic optimization algorithms have gradually replaced traditional algorithms and have been applied to practical problems. Currently, algorithms can be divided into five categories according to their source of inspiration: swarm intelligence algorithm (SI), evolutionary algorithm (EA), memetic algorithm (MA), human-based algorithm, physics-based algorithm (PhA) [8] and hybrid algorithm. The improved symbiotic organisms search (ISOS) [9] is an evolutionary algorithm with a good global search ability and convergence. The modified version of the salp swarm algorithm (mSSA) [10] is a swarm intelligence algorithm which has higher precision and a better convergence effect. The information-exchanged Gaussian arithmetic optimization algorithm with quasi-opposition learning (ieggos-AOA) [11] based on quasi-opposite learning is a kind of PhA. It has the characteristics of a simple algorithm structure and fast convergence. The GASA algorithm [12], based on the genetic algorithm and simulated annealing algorithm, is a hybrid algorithm, which has the characteristics of a simple structure and low calculation cost. Quantum evolutionary computation [13] has become an important research hotspot in the field of intelligent computing in recent years. It makes full use of the theory of quantum bits and superposition states in quantum computation, uses qubit coding to represent individuals and employs quantum gate updating to perform evolutionary operations. At present, the combination of quantum theory and intelligent algorithms has become a popular research method [14]. The quantum bat algorithm (QBA) [15] incorporates the concept of quantum evolutionary computation into the bat algorithm, allowing it to fully utilize the advantages of the algorithm. Compared with classical algorithms, the QBA has the advantages of fast convergence, a simple structure, a strong search ability and a robust local jump mechanism. However, due to the large amount of finite element analysis required in the optimization algorithm, the cost of online calculation is very high. Therefore, it is necessary to introduce an effective model that can replace the finite element simulation in structural optimization design for permanent magnet synchronous motors.