en Research Paper Research Paper <div style="text-align: justify;"><span style="font-family:times new roman;"><span style="font-size:14px;"><span style="color:#000000;">A methodology is proposed for optimal shaping of permanent magnets with non-conventional and complex geometries, used in synchronous motors. The algorithm includes artificial neural network-based surrogate model and multi-objective search based optimization method that will lead to Pareto front solutions. An interior permanent magnet topology with crescent-shaped magnets is also introduced as the case study, on which the proposed optimal shaping methodology is applied. Produced torque per magnets mass and percentage torque ripple are considered as the objectives, in order to take both performance and cost into account. Multi-layer perceptron architecture used to create the approximated model is trained to fit the samples collected via time-stepping finite element simulations. The methodology can be easily generalized to offer a fast and accurate method to optimally define arbitrary permanent magnet shape parameters in various synchronous motors.</span></span></span></div> Permanent Magnet Shaping,Surrogate Model,Artificial Neural Network,Multi-Objective Optimization, 114 121 http://ijeee.iust.ac.ir/browse.php?a_code=A-10-78-26&slc_lang=en&sid=1 A. Nobahari amin.nobahari@gmail.com 180031947532846009280 180031947532846009280 No School of Electrical Engineering, Iran University of Science and Technology, Narmak, Tehran 13114-16846, Iran. M. R. Mosavi M_Mosavi@iust.ac.ir 180031947532846009281 180031947532846009281 Yes School of Electrical Engineering, Iran University of Science and Technology, Narmak, Tehran 13114-16846, Iran. A. Vahedi avahedi@iust.ac.ir 180031947532846009282 180031947532846009282 No School of Electrical Engineering, Iran University of Science and Technology, Narmak, Tehran 13114-16846, Iran.