Multi-objective multi-constraint optimization is required for the design of high-performance electrical appliances because of their need to satisfy a multitude of requirements. Optimization issues such as these perform searches within small and complex regions (design spaces that satisfy all constraints). Multipoint searches such as genetic algorithms (GA) are useful as search methods, but there exists the issue of calculation amounts increasing significantly in size. Pre-processing occurs in the form of narrowing down the design space as so to reduce these calculation amounts. However, depending on the way in which this narrowing down takes place, there exists the importance of factors such as experience, making personal judgements, or the risk of these narrowing-down processes not being carried out correctly.
This document shows a reduction in calculation costs by narrowing down the design space as an optimization calculation pre-process using PM motor optimization issues as an example. Furthermore, and in terms of narrowing-down methods, comparisons are performed between GA which uses an equivalent circuit model, an experimental design method, and GA using an FEA model. It is shown that despite the low calculation cost, the equivalent circuit model and experimental design method cannot perform narrowing-down processes correctly.