CN114757112A - Motor parameter design method and system based on Hui wolf algorithm - Google Patents
Motor parameter design method and system based on Hui wolf algorithm Download PDFInfo
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Abstract
The invention discloses a motor parameter design method and a system based on a Hui wolf algorithm, which are mainly applied to the technical field of motor optimization, wherein the method comprises the following steps: step 100, constructing a motor finite element model, and determining a parameter set to be optimized and an optimization target function set related to the parameter set to be optimized of the motor finite element model; 200, based on a motor finite element model, automatically optimizing a parameter set to be optimized by using a wolf algorithm to obtain an optimal pareto solution set library; and 300, acquiring parameter set information to be optimized, which enables the optimized target function set information to reach the minimum value, from the optimal pareto solution set library as the optimal parameters of the motor. The invention can effectively improve the optimization efficiency of the optimal parameters of the motor and shorten the iteration times by introducing the finite element model of the motor, the Husky algorithm and the pareto domination relationship for iterative calculation.
Description
Technical Field
The invention relates to the technical field of motor optimization, in particular to a motor parameter design method and system based on a grey wolf algorithm.
Background
Existing commercial motor optimization methods can be roughly classified into a direct optimization method and an indirect optimization method, in which: the direct optimization method is usually based on an equivalent magnetic circuit model, an analysis model or a finite element model of the motor, and uses a single or multi-target genetic algorithm and a particle swarm algorithm to directly optimize single or multiple parameters; the indirect optimization method obtains the sensitivity of the optimization target to each parameter by analyzing the influence of different parameter combinations to be optimized on the optimization target, and further obtains an equivalent optimization target-parameter function to be optimized so as to carry out rapid indirect optimization.
However, the above two optimization methods still have some problems in practical application: when an indirect optimization method is adopted, multiple sets of parameters are still needed for carrying out repeated iterative computation in the convergence and verification process of the response surface function, the global polynomial often cannot represent the nonlinearity of a solver model, and the optimization result also needs repeated robust verification; when the direct optimization method is adopted, the multi-target particle swarm algorithm and the genetic algorithm are low in convergence efficiency and multiple in iteration times, and a large amount of time is consumed by combining finite element calculation.
Disclosure of Invention
The invention provides a motor parameter design method and system based on a graywolf algorithm, which are used for solving one or more technical problems in the prior art and at least providing a beneficial selection or creation condition.
The embodiment of the invention provides a motor parameter design method based on a wolf algorithm, which comprises the following steps:
step 100, constructing a motor finite element model, and determining a parameter set to be optimized and an optimization target function set related to the parameter set to be optimized of the motor finite element model;
200, based on a motor finite element model, automatically optimizing a parameter set to be optimized by using a wolf algorithm to obtain an optimal pareto solution set library;
300, acquiring parameter set information to be optimized, which enables the optimized target function set information to reach the minimum value, from the optimal pareto solution set library as the optimal parameters of the motor;
the implementation process of step 200 includes:
step 210, setting the maximum iteration times and the pareto solution set library capacity, and simultaneously generating and emptying a pareto solution set library;
step 220, randomly generating a plurality of data units according to a set value range corresponding to a parameter set to be optimized, and storing the plurality of data units into a pareto solution library, wherein each data unit is recorded with different parameter set information to be optimized and local optimal parameter set information;
and 230, performing iterative updating optimization on the pareto solution set library by using the grey wolf algorithm to obtain an optimal pareto solution set library.
Further, the implementation process of step 230 includes:
231, when the kth iteration is started, deleting all data units which do not meet the pareto domination relationship between individuals in the pareto solution set library generated by the kth-1 th iteration by combining a motor finite element model to obtain a first pareto solution set library, wherein k is greater than 0 and is a positive integer;
step 232, selecting the optimal three data units from the first pareto solution set library as three guide units, and simultaneously combining all local optimal parameter set information recorded by all the remaining unselected data units in the first pareto solution set library, updating parameter set information to be optimized for all the remaining unselected data units in the first pareto solution set library to obtain a second pareto solution set library;
Step 233, judging whether k is less than the maximum iteration number; if yes, go to step 234; if not, outputting the second pareto solution set library as an optimal pareto solution set library;
234, merging the first pareto solution set library and the second pareto solution set library to obtain a third pareto solution set library;
and 235, performing parameter set information resetting and updating on all data units in the third pareto solution set library according to the set value range and the set random number corresponding to the parameter set to be optimized to obtain the pareto solution set library generated by the kth iteration, assigning k +1 to k, and returning to the execution step 231.
Further, in the step 232, the parameter set information to be optimized is updated for all data units left in the first pareto solution set library and not selected, and an adopted calculation formula is as follows:
in the formula:
wherein the content of the first and second substances,
updating the result of the parameter set information to be optimized recorded in one of the data units left unselected in the first pareto solution set library,
and
and
refers to the three guide units which are arranged in the guide unit,
the parameter set information to be optimized recorded for the p-th leading unit,
the parameter set information to be optimized recorded in the data unit which is not selected in the first pareto solution set library is left, 
The information of the local optimal parameter set recorded for the data unit that is not selected is left in the first pareto solution set library,
and
are all three-dimensional vectors for use in aiding computation，
Is a weight coefficient, k is the current iteration number,
is the maximum number of iterations in the sequence,
in order to be a delay factor, the delay factor,
in order to be the coefficient of inertia,
and
are all in [0,1 ]]Randomly taking values in the range to generate three-dimensional vectors.
Further, in the step 235, the parameter set information to be optimized is reset and updated for all the data units in the third pareto solution set library, that is, the parameter set information to be optimized recorded in any one data unit is reset and updated for each parameter information to be optimized, and the calculation formula adopted is as follows:
wherein the content of the first and second substances,
updating the result for the ith parameter information to be optimized in the parameter set information to be optimized recorded in the data unit,
the ith parameter information to be optimized in the parameter set information to be optimized recorded for the data unit, r is the value falling within [ -0.3,0.3 [)]A given random number within the range is determined,
for a given maximum value that the ith parameter to be optimized can be assigned,
the given minimum value that can be assigned to the ith parameter to be optimized.
Further, after the step 234 is executed, the method further includes:
Solving all data units in the third pareto solution set library by using the motor finite element model to obtain optimized target function set information corresponding to parameter set information to be optimized recorded by each data unit and local optimal target function set information corresponding to local optimal parameter set information;
when the condition that the optimized target function set information recorded by any data unit forms a pareto domination relation with the local optimal target function set information recorded by the optimized target function set information is judged, the local optimal parameter set information recorded by the data unit is replaced by parameter set information to be optimized;
alternatively, when it is determined that the local optimum objective function set information described in any one data unit does not form a pareto dominant relationship with the optimized objective function set information described in the data unit, the local optimum parameter set information described in the data unit is kept unchanged.
Further, the number of data units stored in the first pareto solution set library does not exceed the pareto solution set library capacity.
Further, all the parameter set information to be optimized recorded by all the data units in the third pareto solution set library are different from each other.
In addition, an embodiment of the present invention further provides a motor parameter design system based on the grayish wolf algorithm, where the system includes:
At least one processor;
at least one memory for storing at least one program;
when executed by the at least one processor, cause the at least one processor to implement a motor parameter design method based on the graying algorithm as described in any one of the above.
The invention has at least the following beneficial effects: by using the motor finite element model with higher reliability for calculation, the nonlinear error of the solver model brought by the prior art can be effectively avoided. The guiding function of the local optimal parameter set information is additionally added in the searching process of the original wolf algorithm, and the judging application of the pareto domination relation is combined in the iteration process, so that the optimizing efficiency of the optimal parameters of the motor can be improved, and the iteration times can be shortened.
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The accompanying drawings are included to provide a further understanding of the present invention and are incorporated in and constitute a part of this specification, illustrate embodiments of the invention and together with the example serve to explain the principles of the invention and do not constitute a limitation thereof.
Fig. 1 is a schematic flow chart of a motor parameter design method based on a grayish wolf algorithm in an embodiment of the present invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention more apparent, the present invention is further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the invention and do not limit the invention.
It is noted that while a division of functional blocks is depicted in the system diagram, and logical order is depicted in the flowchart, in some cases the steps depicted and described may be performed in a different order than the division of blocks in the system or the flowchart. The terms first, second and the like in the description and in the claims, as well as in the drawings described above, are used for distinguishing between similar elements and not necessarily for describing a particular sequential or chronological order.
Referring to fig. 1, fig. 1 is a schematic flow chart of a motor parameter design method based on a grayish wolf algorithm according to an embodiment of the present invention, where the method includes the following steps:
step 100, constructing a motor finite element model, and determining a parameter set to be optimized and an optimization target function set related to the parameter set to be optimized of the motor finite element model;
200, based on a motor finite element model, automatically optimizing a parameter set to be optimized by using a wolf algorithm to obtain an optimal pareto solution set library;
And 300, acquiring parameter set information to be optimized, which enables the optimized target function set information to reach the minimum value, from the optimal pareto solution set library as the optimal parameters of the motor.
In the embodiment of the present invention, for the step 100, the process of constructing the finite element model of the motor depends on existing electromagnetic field finite element simulation software, including ANSYS Electronics Desktop simulation software, JMag simulation software, and the like.
In the embodiment of the present invention, for step 100, it is first determined that the parameter set to be optimized includes N parameters to be optimized, and each parameter to be optimized must be constrained within the robust calculation range of the finite element model of the motor; and then obtaining M optimization objective functions according to different implicit relations among the N parameters to be optimized to form the optimization objective function set, wherein any one of the M optimization objective functions is at least related to two parameters to be optimized in the N parameters to be optimized, and the M optimization objective functions must cover the N parameters to be optimized.
In the embodiment of the present invention, the implementation process of step 200 includes the following steps:
step 210, setting the maximum iteration times and the pareto solution set library capacity, and simultaneously generating and emptying a pareto solution set library;
Step 220, randomly generating a plurality of data units according to a set value range corresponding to a parameter set to be optimized, and storing the plurality of data units into a pareto solution library, wherein each data unit records different parameter set information to be optimized and local optimal parameter set information, and the local optimal parameter set information recorded by any data unit is the parameter set information to be optimized recorded by the data unit;
and 230, performing iterative updating optimization on the pareto solution set library by using a grey wolf algorithm to obtain an optimal pareto solution set library.
In the embodiment of the present invention, the implementation process of step 230 includes the following steps:
231, when the kth iteration is started, deleting all data units which do not meet the pareto domination relation between individuals in the pareto solution set library generated by the kth-1 iteration by combining a finite element model of the motor to obtain a first pareto solution set library, wherein k is more than 0 and is a positive integer;
232, selecting three optimal data units from the first pareto solution collection library as three guide units, and meanwhile, combining all local optimal parameter set information recorded by all the remaining unselected data units in the first pareto solution collection library, updating parameter set information to be optimized for all the remaining unselected data units in the first pareto solution collection library to obtain a second pareto solution collection library;
Step 233, judging whether k is less than the maximum iteration number; if yes, go to step 234; if not, outputting the second pareto solution set library as an optimal pareto solution set library;
step 234, merging the first pareto solution set library and the second pareto solution set library to obtain a third pareto solution set library, wherein information of all parameter sets to be optimized, which are recorded by all data units in the third pareto solution set library, are different from each other, that is, the deduplication processing is simultaneously executed in the merging processing process;
and 235, performing parameter set information resetting and updating on all data units in the third pareto solution set library according to the set value range and the set random number corresponding to the parameter set to be optimized to obtain the pareto solution set library generated by the kth iteration, assigning k +1 to k, and returning to the execution step 231.
It should be noted that, the step 230 should perform an iterative operation starting from k =1, where the pareto solution set library generated in the k-1 st iteration is actually the pareto solution set library obtained after the initialization task of the step 220 is performed.
Specifically, the implementation process of step 231 includes: firstly, solving all data units in a pareto solution set library generated by the (k-1) th iteration by using a motor finite element model to obtain optimized target function set information corresponding to parameter set information to be optimized recorded by each data unit; secondly, carrying out pareto domination relation judgment on the optimized target function set information recorded by each data unit in the pareto solution set library generated by the k-1 iteration and the optimized target function set information recorded by other data units one by one, if the pareto domination relation is met, retaining the data unit, and if the pareto domination relation is not met, deleting the data unit, thereby obtaining a first pareto solution set library after preliminary processing; and finally, judging whether the quantity of the data units stored in the first pareto solution library after the primary processing exceeds the capacity of the initially set pareto solution library, if so, deleting the first pareto solution library after the primary processing according to the excess quantity, and if not, directly outputting the first pareto solution library as a final first pareto solution library.
Wherein, the pareto dominance relation is defined as follows:
in the formula
Referring to the ith optimization objective function information recorded in the data unit, if the ith optimization objective function information is recorded in the same pareto solution set library, the parameter set information to be optimized
Data unit and parameter set information to be optimized
When the data unit satisfies the above formula, it is determined that the parameter set information to be optimized is recorded
May dominate the recording of parameter set information to be optimized
Data units of (2), i.e. describing parameter set information to be optimized
Satisfies the pareto dominance relationship.
Wherein, the deleting process is performed on the first pareto solution set library after the preliminary treatment according to the excess number, and the specific implementation process comprises the following steps:
step a, assuming that Y1 data units are stored in the first pareto solution set library after the primary processing, constructing a Y1 row × M column selection matrix according to Y1 optimized objective function set information recorded by Y1 data units, and extracting the maximum optimized objective function set information as
And minimum optimization objective function set information of
Wherein
Refers to the maximum value in the first column of data of the selection matrix,
refers to the minimum value in the first column of data of the selection matrix;
B, determining boundary conditions by using the maximum optimization target function set information and the minimum optimization target function set information, and constructing an M-dimensional network structure, wherein the boundary of the first-dimensional network is
So as to analogize the boundaries of other dimension networks;
step c, setting the number of grids as j, averagely dividing each dimension network in the M-dimension network structure into j grids, and marking each optimized target function set information in Y1 optimized target function set information in the M-dimension network structure respectively;
d, supposing that the excess quantity is n, selecting a first grid with the most marked information from the M-dimensional network structure, supposing that the quantity of all data units associated with the first grid is d, if d is larger than or equal to n, randomly selecting n data units from the d data units, and deleting the n data units from the first pareto solution set library after preliminary treatment; if d is less than n, firstly deleting all data units associated with the first grid from the first pareto solution set library after the preliminary processing, then continuously selecting a second grid with second most marking information from the M-dimensional network structure, then randomly selecting n-d data units from all data units associated with the second grid, and then deleting the n-d data units from the first pareto solution set library after the preliminary processing; thereby the number of data units stored in the final first pareto solution pool does not exceed the pareto solution pool capacity.
Specifically, in the step 232, based on the M-dimensional network structure, the selection probability P of any data unit is inversely proportional to the number N of all data units in the grid where the data unit is located, that is, the data unit is located
And C is a set selection pressure coefficient, at the moment, the selection probability of all the data units in the first pareto solution set library is calculated, and then three data units with the selection probability ranked at the top are used as three guide units.
Specifically, in the step 232, the parameter set information to be optimized is updated for all data units left in the first pareto solution set library and not selected, and an adopted calculation formula is as follows:
in the formula:
wherein, the first and the second end of the pipe are connected with each other,
updating the result of the parameter set information to be optimized recorded in one of the data units left unselected in the first pareto solution set library,
and
and
refers to the three guide units which are arranged in the guide unit,
the parameter set information to be optimized recorded for the p-th leading unit,
the parameter set information to be optimized recorded in the data unit which is not selected in the first pareto solution set library is left,
the information of the local optimal parameter set recorded in the data unit which is not selected is left in the first pareto solution set library, 
And
are all three-dimensional vectors used for aiding in computation,
is a weight coefficient, k is the current iteration number,
is the maximum number of iterations in the sequence,
in order to be a delay factor, the delay factor,
in order to be the coefficient of inertia,
and
are all in [0,1 ]]Randomly taking values in the range to generate three-dimensional vectors.
The weight coefficient is
Guidance for adjusting the local optimal parameter set information, and
when the value is larger, the search scope of the wolf algorithm is improved, and the convergence precision is reduced; delay factor
For adjusting the effect of the guide delay, when
When the value is larger, the guiding delay effect is weakened, the search scope of the wolf algorithm is improved, and the convergence precision is reduced; coefficient of inertia
For adjusting the effect of guiding inertia
When the value is increased, the inertia effect is guided to be increased along with the exponential curve with the iteration times presenting higher times, the search strength of the wolf algorithm is improved when the wolf algorithm is applied in the early middle period, and the wolf algorithm is applied in the later middle periodThe convergence speed in application will be increased.
Specifically, after the step 234 is executed, the method further includes: solving all data units in the third pareto solution set library by using the motor finite element model to obtain optimized target function set information corresponding to parameter set information to be optimized recorded by each data unit and local optimal target function set information corresponding to local optimal parameter set information; when the condition that the optimized target function set information recorded by any data unit forms a pareto domination relation with the local optimal target function set information recorded by the optimized target function set information is judged, the local optimal parameter set information recorded by the data unit is replaced by parameter set information to be optimized; on the contrary, when it is judged that the locally optimal objective function set information described in any data unit does not form the pareto dominant relationship with the locally optimal objective function set information described in the data unit, the locally optimal parameter set information described in the data unit is kept unchanged.
Specifically, in step 235, because the precision of the parameter set to be optimized is affected by manufacturing errors and other noise factors in the actual engineering, the parameter set information to be optimized is more likely to be repeated due to data units with excessively concentrated parameter set information to be optimized, and an iteration process falls into an extremum region to affect convergence efficiency and convergence effect, so that the embodiment of the present invention proposes to perform parameter set information to be optimized reset update on all data units in the third pareto solution library, that is, to perform reset update on each parameter information to be optimized in the parameter set information to be optimized recorded in any one data unit, and an adopted calculation formula is as follows:
wherein, the first and the second end of the pipe are connected with each other,
updating the result for the ith parameter information to be optimized in the parameter set information to be optimized recorded in the data unit,
the ith parameter information to be optimized in the parameter set information to be optimized recorded for the data unit is r falling in [ -0.3,0.3 [)]A given random number within the range is determined,
for a given maximum value that the ith parameter to be optimized can be assigned,
the given minimum value that can be assigned to the ith parameter to be optimized.
In the embodiment of the present invention, for the step 300, the finally selected parameter set information to be optimized should also meet the manufacturing accuracy requirement and the error tolerance range requirement of the actual engineering.
In the embodiment of the invention, the finite element model of the motor with higher reliability is used for calculation, so that the nonlinear error of the solver model brought by the prior art can be effectively avoided. The guiding function of the local optimal parameter set information is additionally added in the searching process of the original wolf algorithm, and the judging application of the pareto domination relation is combined in the iteration process, so that the optimizing efficiency of the optimal parameters of the motor can be improved, and the iteration times can be shortened.
In addition, an embodiment of the present invention further provides a motor parameter design system based on the grayish wolf algorithm, where the system includes:
at least one processor;
at least one memory for storing at least one program;
when executed by the at least one processor, the at least one program causes the at least one processor to implement the motor parameter design method based on the graying algorithm as described in any of the above embodiments.
The contents in the method embodiments are all applicable to the system embodiments, the functions realized by the system embodiments are the same as the method embodiments, and the beneficial effects achieved by the system embodiments are the same as the method embodiments.
The Processor may be a Central-Processing Unit (CPU), other general-purpose Processor, a Digital Signal Processor (DSP), an Application-Specific-Integrated-Circuit (ASIC), a Field-Programmable Gate Array (FPGA) or other Programmable logic device, a discrete Gate or transistor logic device, a discrete hardware component, or the like. The general purpose processor may be a microprocessor or the processor may be any conventional processor or the like, the processor being the control center of the grey wolf algorithm based motor parameter design system, various interfaces and lines connecting the various parts of the overall grey wolf algorithm based motor parameter design system operational apparatus.
The memory may be used to store the computer programs and/or modules, and the processor may implement the various functions of the grey wolf algorithm based motor parameter design system by running or executing the computer programs and/or modules stored in the memory and calling the data stored in the memory. The memory may mainly include a program storage area and a data storage area, wherein: the storage program area is used for storing an operating system, application programs (such as a sound playing function and an image playing function) required by at least one function and the like; the storage data area is used for storing data (such as audio data, a phone book and the like) and the like created according to the use of the mobile phone. Further, the memory may include high-speed random access memory, and may also include non-volatile memory, such as a hard disk, a memory, a plug-in hard disk, a Smart-Media-Card (SMC), a Secure-Digital (SD) Card, a Flash-memory Card (Flash-Card), at least one magnetic disk storage device, a Flash memory device, or other volatile solid state storage device.
While the description of the present application has been made in considerable detail and with particular reference to a few illustrated embodiments, it is not intended to be limited to any such details or embodiments or any particular embodiments, but it is to be construed that the present application effectively covers the intended scope of the application by reference to the appended claims, which are interpreted in view of the broad potential of the prior art. Moreover, the foregoing describes the present application in terms of embodiments foreseen by the inventor for which an enabling description was available, notwithstanding that insubstantial modifications of the present application, not presently foreseen, may nonetheless represent equivalents thereto.
Claims (8)
1. A motor parameter design method based on a Grey wolf algorithm is characterized by comprising the following steps:
step 100, constructing a motor finite element model, and determining a parameter set to be optimized and an optimization target function set related to the parameter set to be optimized of the motor finite element model;
200, automatically optimizing a parameter set to be optimized by utilizing a wolf algorithm based on a motor finite element model to obtain an optimal pareto solution set library;
300, acquiring information of a parameter set to be optimized, which enables information of an optimized target function set to reach a minimum value, from an optimal pareto solution set library as an optimal parameter of the motor;
the implementation process of step 200 includes:
step 210, setting the maximum iteration times and the pareto solution set library capacity, and simultaneously generating and emptying a pareto solution set library;
step 220, randomly generating a plurality of data units according to a set value range corresponding to a parameter set to be optimized, and storing the plurality of data units into a pareto solution library, wherein each data unit is recorded with different parameter set information to be optimized and local optimal parameter set information;
and 230, performing iterative updating optimization on the pareto solution set library by using the grey wolf algorithm to obtain an optimal pareto solution set library.
2. The grayish wolf algorithm-based motor parameter design method according to claim 1, wherein the implementation process of the step 230 comprises:
231, when the kth iteration is started, deleting all data units which do not meet the pareto domination relation between individuals in the pareto solution set library generated by the kth-1 iteration by combining a finite element model of the motor to obtain a first pareto solution set library, wherein k is more than 0 and is a positive integer;
step 232, selecting the optimal three data units from the first pareto solution set library as three guide units, and simultaneously combining all local optimal parameter set information recorded by all the remaining unselected data units in the first pareto solution set library, updating parameter set information to be optimized for all the remaining unselected data units in the first pareto solution set library to obtain a second pareto solution set library;
step 233, judging whether k is less than the maximum iteration number; if yes, go to step 234; if not, outputting the second pareto solution set library as an optimal pareto solution set library;
234, merging the first pareto solution set library and the second pareto solution set library to obtain a third pareto solution set library;
And 235, performing parameter set information resetting and updating on all data units in the third pareto solution set library according to the set value range and the set random number corresponding to the parameter set to be optimized to obtain the pareto solution set library generated by the kth iteration, assigning k +1 to k, and returning to the execution step 231.
3. The grayish wolf algorithm-based motor parameter design method of claim 2, wherein in the step 232, the parameter set information to be optimized is updated for all data units left unselected in the first pareto solution set library by using the following calculation formula:
in the formula:
wherein the content of the first and second substances,
updating the result of the parameter set information to be optimized recorded in one of the data units left unselected in the first pareto solution set library,
and
and
refers to the three guide units which are arranged in the guide unit,
the parameter set information to be optimized recorded for the p-th leading unit,
the parameter set information to be optimized recorded in the data unit which is not selected in the first pareto solution set library is left,
the information of the local optimal parameter set recorded in the data unit which is not selected is left in the first pareto solution set library,
and
are all three-dimensional vectors used for aiding in the computation, 
Is a weight coefficient, k is the current iteration number,
is the maximum number of iterations in the sequence,
in order to be a delay factor, the delay factor,
in order to be the coefficient of inertia,
and
are all in [0,1 ]]Randomly taking values in the range to generate three-dimensional vectors.
4. The motor parameter design method based on the graying algorithm of claim 2, wherein in the step 235, the parameter set information to be optimized is reset and updated for all the data units in the third pareto solution set library, that is, the parameter set information to be optimized in each of the parameter set information to be optimized recorded in any one data unit is reset and updated by using the following calculation formula:
wherein the content of the first and second substances,
updating the result for the ith parameter information to be optimized in the parameter set information to be optimized recorded in the data unit,
the ith parameter information to be optimized in the parameter set information to be optimized recorded for the data unit, r is the value falling within [ -0.3,0.3 [)]A given random number within the range is determined,
is the ithThe given maximum value that the parameter to be optimized can be assigned,
the given minimum value that can be assigned to the ith parameter to be optimized.
5. The grayish wolf algorithm-based motor parameter design method according to claim 2, further comprising, after performing the step 234:
Solving all data units in the third pareto solution set library by using the motor finite element model to obtain optimized target function set information corresponding to the parameter set information to be optimized recorded by each data unit and local optimal target function set information corresponding to the local optimal parameter set information;
when judging that the optimization target function set information recorded by any data unit forms a pareto domination relation with the local optimal target function set information recorded by the data unit, replacing the local optimal parameter set information recorded by the data unit with parameter set information to be optimized;
alternatively, when it is determined that the local optimum objective function set information described in any data unit does not form a pareto dominance relationship with the optimized objective function set information described in the data unit, the local optimum parameter set information described in the data unit is kept unchanged.
6. The grayish wolf algorithm-based motor parameter design method according to claim 2, characterized in that the number of data units stored in the first pareto solution set library does not exceed a pareto solution set library capacity.
7. The grayish wolf algorithm-based motor parameter design method according to claim 2, characterized in that all the parameter set information to be optimized recorded by all the data units in the third pareto solution set library are different from each other.
8. A motor parameter design system based on a graywolve algorithm, the system comprising:
at least one processor;
at least one memory for storing at least one program;
when executed by the at least one processor, cause the at least one processor to implement the graying algorithm-based motor parameter design method of any one of claims 1 to 7.
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Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN117094064A (en) * | 2023-10-19 | 2023-11-21 | 西南交通大学 | Method, device, equipment and storage medium for calculating layout parameters of components |
| CN117634397A (en) * | 2023-12-01 | 2024-03-01 | 安徽工程大学 | Multi-objective optimization method and system based on two-dimensional equivalent model of axial flux permanent magnet motor |
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Cited By (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN117094064A (en) * | 2023-10-19 | 2023-11-21 | 西南交通大学 | Method, device, equipment and storage medium for calculating layout parameters of components |
| CN117094064B (en) * | 2023-10-19 | 2024-03-01 | 西南交通大学 | Method, device, equipment and storage medium for calculating layout parameters of components |
| CN117634397A (en) * | 2023-12-01 | 2024-03-01 | 安徽工程大学 | Multi-objective optimization method and system based on two-dimensional equivalent model of axial flux permanent magnet motor |
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