Comparison of three space mapping techniques on electromagnetic design optimization

Abstract

PurposeThis paper aims to investigate three low-evaluation-budget optimization techniques: output space mapping (OSM), manifold mapping (MM) and Kriging-OSM. Kriging-OSM is an original approach having high-order mapping. Design/methodology/approachThe electromagnetic device to be optimally sized is a five-phase linear induction motor, represented through two levels of modeling: coarse (Kriging model) and fine.The optimization comparison of the three techniques on the five-phase linear induction motor is discussed. FindingsThe optimization results show that the OSM takes more time and iteration to converge the optimal solution compared to MM and Kriging-OSM. This is mainly because of the poor quality of the initial Kriging model. In the case of a high-quality coarse model, the OSM technique would show its domination over the other two techniques. In the case of poor quality of coarse model, MM and Kriging-OSM techniques are more efficient to converge to the accurate optimum. Originality/valueKriging-OSM is an original approach having high-order mapping. An advantage of this new technique consists in its capability of providing a sufficiently accurate model for each objective and constraint function and makes the coarse model converge toward the fine model more effectively.

Comparison between Space Mapping and Direct FEA Optimizations for the Design of Halbach Array PM Motor

“…To solve the problem using SM, it is possible to use different techniques reported in the literature, including among them Output Space Mapping (OSM), Manifold Mapping (MM) and Kriging-OSM [27]. The first one is the easiest to implement compared to the others because it avoids the process of extracting parameters.…”

“…MM is an improvement of OSM and allows for the finding of a solution to those where the OSM does not find a correct optimal solution. Finally, Kriging-OSM makes it possible to provide a sufficiently accurate modified coarse model through adaptive corrective mapping [27].…”