Tuning an evolutionary algorithm with taguchi methods and application to the dimensioning of an electrical motor

Authors:
J.-L. Hippolyte;C. Bloch;P. Chatonnay;C. Espanet;D. Chamagne;G. Wimmer
Affiliations:
Computer Science Laboratory of the University of Franche-Comté, Montbéliard, France;Computer Science Laboratory of the University of Franche-Comté, Montbéliard, France;Computer Science Laboratory of the University of Franche-Comté, Montbéliard, France;University of Franche-Comté, Belfort, France;University of Franche-Comté, Belfort, France;University of Franche-Comté
Venue:
CSTST '08 Proceedings of the 5th international conference on Soft computing as transdisciplinary science and technology
Year:
2008

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Abstract

This paper presents an original method of permanent magnet motor optimal design developped by both Electrical Engineering and Computer Science laboratories. An Evolutionary Algorithm combining Genetic Algorithms and Multiagent Systems is used. This Genetic Multiagent System parameters are determined using a robust design method based on the Taguchi approach. The quality of the algorithm is evaluated considering the multiobjective quality of the solutions it delivers on a permanent magnet machine constrained optimization. Contradictory objectives as efficiency and weight have a large influence on the design of electrical machines. Performances of the resulting tuned up algorithm are compared with previous results from the authors.