Evolution strategies and multi-objective optimization of permanent magnet motor

Publication: Research - peer-reviewJournal article – Annual report year: 2012

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When designing a permanent magnet motor, several geometry and material parameters are to be defined. This is not an easy task, as material properties and magnetic fields are highly non-linear and the design of a motor is therefore often an iterative process. From an engineering point of view, we usually want to maximize the efficiency of the motor and from an economic point of view we want to minimize the cost of the motor. As these two things seldom go hand in hand, the goal is to find the best efficiency per cost. The scope of this paper is therefore to investigate the applicability of evolution strategies, ES to effectively design and optimize parameters of permanent magnet motors. Single as well as multi-objective optimization procedures are carried out. A modified way of creating the strategy parameters for the ES algorithm is also proposed and has together with the standard ES algorithm undergone a comprehensive parameter study for the parameters ρ and λ. The results of this parameter study show a significant improvement in stability and speed with the use of the modified ES version. To find the most effective selector for a multi-objective optimization, MOO, of the motor a performance examination of 4 different selectors from the group of programs called PISA has been made and compared for MOO of the efficiency and cost of the motor. This performance examination showed that the indicator based evolutionary algorithm, IBEA, and hypervolume estimation algorithm, HypE, selectors performed almost equally good on this MOO problem where the HypE selector only had a slightly better performance indicator.
Original languageEnglish
JournalApplied Soft Computing
Publication date2012
Volume12
Journal number2
Pages778-792
ISSN1568-4946
DOIs
StatePublished
CitationsWeb of Science® Times Cited: 3

Keywords

  • Metaheuristic, Permanent magnet motor, Multi objective optimization, Optimization, Evolution strategies
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