EMSOptimizer: A Tool for Optimizing Shape Parameters of Electrical Devices

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last updated：Jan 16, 2026

Topology optimization × Parameter optimization

EMSOptimizer is an optimization framework that provides elements of optimization calculations (objectives, evaluation functions, constraints, and search methods) as classes, allowing for flexible combination through inheritance and implementation in Python. Designed with the premise of introducing OSS optimization libraries, it enables direct application of the latest methods for A/B comparisons and custom improvements in practical work. It integrates shape optimization using the NGnet method and parameter optimization in collaboration with eMotorSolution within the same workflow, balancing research speed and implementation speed. It offers engineers and researchers with experience in optimization and Python the freedom to "immediately apply the latest OSS methods to real-world problems."

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basic information

【Features】 ○ Configuration file (YAML) allows setting conditions and optimization algorithms → Execute optimization from the command line ○ Equipped with single-objective and multi-objective optimization algorithms. Consideration of constraints is also possible ○ Topology optimization function (based on on/off method) 　　　● Dynamic mesh regeneration function included → Achieves smooth material boundaries ○ Integration function with eMotorSolution → Supports motor dimension optimization ○ Optimization of characteristics under multiple conditions (performance maximization for each current condition, etc.) ○ Display of the optimization process via GUI and output of shape data and evaluation result data ○ Functionality can be added and customized using Python 　　　● Optimization algorithms 　　　● Evaluation functions ● For more details, please contact us. 【Contact Information】 ● E-mail: em_solution@ssil.co.jp

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Applications/Examples of results

Optimize by setting shape data (mesh file) and conditions. The execution results can be checked from the GUI and output files (CSV, etc.). Customization of the optimization process using Python is possible.