Multi-Objective Design for Switched Reluctance Machines Using Genetic and Fuzzy Algorithms
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Abstract
This paper generalizes an automatic design process for dimensioning switched reluctance machines using a combination between genetic and fuzzy algorithms. The process considers all desired performance, dimensions and design guidelines as optimization objectives for determining tness score of genetic algorithms whereas most automatic processes neither consider the restricted dimensions as objectives nor use them during adjusting machine dimensions. The processes are immediately interrupted and reject adjusted models in case that any dimension of these models does not comply with the restrictions regardless of their performances. In this paper, all interrupts are eliminated from design process. Besides, optimization objectives from design guidelines based on empirical experience improve the calculation accuracy of analytical analysis while still satisfying the requirements. Despite the increased number of objectives, the tness of each objective is still normally determined since fuzzy algorithms connect all objectives together to convert model goodness into a scale factor used for scaling the objectives before summing into the tness score. This formulates imprecise decision of designers into fuzzy rules and compensates the lacks of design experiences. The proposed method demonstrates the promising results veried by statistical records of model optimization from a mass simulation.
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