JPH10209681A - Method for optimizing mounting of electronic part - Google Patents

Abstract

PROBLEM TO BE SOLVED: To provide a method for optimizing the mounting of an electronic part in which the total moving distance of a mounter head can be shortened as much as possible within a practical time and a substantially optimal arrangement of a feeder can be determined. SOLUTION: Total moving distance of a mounter head 22 is optimized in an electronic part mounting machine in which the mounter head 22 moves between an electronic part feeding section 12 arranged with a plurality of feeders 14 at specified positions and a board 16 to be mounted with a plurality of types of electronic part 24 and mounts the electronic part 24 at the part feeding section 12 on the board 16. Character data indicative of the arranging positions of the plurality of feeders 14 in the part feeding section 12 are combined to generate specified number of samples comprising a series of arranging position data. A genetic algorithm is then applied to each sample thus generated using the total moving distance as an evaluation value, thus optimizing the arranging position of the plurality of feeders 14 in the part feeding section 12.

Description

DETAILED DESCRIPTION OF THE INVENTION

[0001]

BACKGROUND OF THE INVENTION 1. Field of the Invention The present invention relates to a total moving distance or a total moving time of a mounter head in an electronic component mounting machine for supplying a plurality of types of electronic components from a plurality of feeders to a substrate such as a printed circuit board and mounting the components. And a method for optimizing electronic component mounting.

[0002]

2. Description of the Related Art With the miniaturization and high integration of electronic components, the number of electronic components mounted on a printed circuit board (hereinafter simply referred to as "substrate") has become extremely large, and the production time has been reduced. In order to reduce the cost, the time required for the electronic component mounter to mount the electronic component on the substrate is an important requirement, which affects productivity. In order to shorten the mounting time, it is effective to increase the moving speed of the mounter head of the electronic component mounting machine.However, since this method involves an increase in the cost of the electronic component mounting machine itself, it is unnecessary to use the mounter head. It is very effective to reduce the mounting time by minimizing unnecessary movements.

In order to reduce unnecessary movement of the mounter head, a feeder in an electronic component supply unit in which a plurality of feeders (one type of electronic component is supplied from the feeder) is arranged at a predetermined position. And / or at least one of the mounting order of the electronic components mounted on the board is optimized, and the total moving distance or total moving time of the mounter head until the mounting of the electronic component on the board is completed. What is necessary is to make it the least.

Therefore, conventionally, an evaluation function for calculating the total moving distance or the total moving time is determined in order to determine the optimum combination of both or one of the arrangement of the feeders and the mounting order of the electronic components. And the combination that minimizes the total moving distance or the total moving time is obtained.

[0005]

However, according to the conventional optimization method described above, the number and types of electronic components mounted on one board are very large.
There is an innumerable number of combinations, which takes a long time to calculate and is not practical. As an example, when determining the arrangement of the feeders, if the number of positions where the feeders can be arranged in the electronic component supply unit is a and the number of the feeders is b (a ≧ b), the number of combinations is , _a
C _b × b! , And 30 even when a = 10 and b = 5
There are 240 ways. Further, when the number of electronic components is c and the number of combinations including the mounting order is, for example, c = 20, _a C _b × b! × c! It becomes about 7.4 × 10 ²² ways. In reality, some parts are 1000
Since there are ~ 2000 or more, and there are also 100 or more types, it is impractical to evaluate a very large number of all combinations to find a combination that minimizes the total moving distance or total moving time.

SUMMARY OF THE INVENTION Accordingly, it is an object of the present invention to solve the above-mentioned problems, and it is an object of the present invention to provide a feeder head which is closer to the optimum one and can shorten the total moving distance or the total moving time of the mounter head within a practical time. An object of the present invention is to provide an electronic component mounting optimization method capable of determining the arrangement and / or the mounting order of electronic components.

[0007]

In order to achieve the above object, according to the first aspect of the present invention, there is provided an electronic component supply unit in which a plurality of feeders are arranged at predetermined positions and a plurality of types of electronic components. An electronic component for optimizing a total moving distance or a total moving time of the mounter head in an electronic component mounting machine in which a mounter head moves between a substrate on which components are mounted and an electronic component of an electronic component supply unit is mounted on the substrate. In the mounting optimization method, the positions of the plurality of feeders in the electronic component supply unit are connected to generate a predetermined number of individuals having an array of the positions, and the total number The present invention is characterized in that a genetic algorithm is applied using a moving distance or a total moving time as an evaluation value to optimize the arrangement positions of a plurality of feeders in an electronic component supply unit. In this way, by applying a genetic algorithm that is compatible with global search, a practically sufficient array can be obtained from the array of arrangement positions in the electronic component supply unit of a huge feeder in a realistic time. You can search by.

[0008] Further, the individual is configured by connecting the arrangement of the arrangement positions and the arrangement of the mounting order of the electronic components on the board, and the generated individuals are subjected to a genetic algorithm in a genetic algorithm. When generating a next-generation individual by performing an operation, the genetic operation is performed independently on one or both of the array of the arrangement position and the array of the mounting order of each individual. In addition to the arrangement position in the electronic component supply section, the mounting order of the electronic components can be optimized, and the total moving distance or the total moving time of the mounter head can be further optimized. .

Further, when performing a genetic operation in a genetic algorithm on each of the generated individuals to generate a next-generation individual, the sequence is determined for one or more predetermined individuals. If a new individual is generated by a method of rotating a predetermined number in the direction,
In addition to selection, crossover, and mutation, which are the genetic operations in a general genetic algorithm, a new genetic operation called rotation as described above is added, so that a more global search can be performed. This rotation operation has the effect that important parts of the individual are preserved,
The possibility of falling into a local solution can be prevented, and the possibility of shortening the time required to converge to the optimal solution increases.

[0010] The rotation may be performed on individuals having evaluation values from the highest evaluation value to the nth (n is a natural number) among the evaluation values. This further increases the possibility that the time required to converge to the optimal solution is shortened because the important part of the sequence of the individual having a high evaluation value is not significantly changed.

[0011]

DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS Preferred embodiments of the present invention will be described in detail with reference to the accompanying drawings. (First Embodiment) A schematic structure of an electronic component mounter (mounter) 10 using an electronic component mounting optimization method according to the present invention will be described with reference to FIGS. The electronic component supply unit 12 is formed by arranging a plurality of feeders 14 at predetermined positions. Specifically, a plurality of feeder mounting portions 18 to which the feeder 14 can be mounted with the substrate 16 interposed therebetween are arranged in parallel in two rows. The feeder 14 can be mounted on an arbitrary one of the plurality of feeder mounting portions 18. The board 16 is fed into the electronic component mounter 10 by fixed-size feed while moving on the rail 20, and after a predetermined electronic component is mounted, the board 2
It moves on 0 and is sent to the subsequent process.

The mounter head 22 includes the electronic component supply unit 1
The electronic component 24 of the electronic component supply unit 12 is mounted on the substrate 16 by moving between the feeder 14 of the electronic component 2 and a substrate 16 on which a plurality of types of electronic components 24 are mounted. Specifically, the mounter head 22 is provided with one or more suction units 26 for sucking the electronic components 24. The suction unit 26 has a nozzle 28 for sucking electronic components. The nozzle 28 is moved in a Z direction (up and down direction) by a nozzle driving unit (not shown) provided in the suction unit 26.
And it is rotated about the rotation axis in the Z direction. Suction and suction release operations are performed by suction and discharge of air from the nozzle 28. When a plurality of suction units 26 are provided, a plurality of types of electronic components 24 can be suctioned and moved onto the substrate 16 at a time, provided that the feeder 14 is disposed in the adjacent feeder mounting portion 18. Becomes

The mounter head 22 is mounted on an XY unit 32 disposed on the upper surface of the base 30, and
0, it can be moved horizontally in the X and Y directions in the space above. More specifically, the XY unit 32 includes the X axis 32a and the Y axis 32a.
And an X axis 32 on the Y axis 32b as an example.
a in the Y direction and the mounter head 22 on the X axis 32a.
Are configured to be movable in the X direction, and can be moved to an arbitrary position by being driven by a drive mechanism (not shown) provided in the XY unit 32. The drive mechanism also has a function of moving and rotating the suction unit 26 in the Z direction.

The suction unit 26 and the XY unit 32
A control unit 40 including a microcomputer (microcomputer) 34, a memory 36, an input interface (input I / F) 38, and the like is controlled to operate in a predetermined pattern. More specifically, various data (type and number of feeders 14, mounting position and type of electronic components 24 on substrate 16, number of suction units 26, number of suction units 26, substrate 16 and electronic component supply unit 12 The basic data including the positional relationship with the feeder mounting unit 18 is input. The microcomputer 34 stores the input data in a basic data area 36 a in the memory 36, and then arranges the feeder 14 in the electronic component supply unit 12 and / or stores the data in the program area 36 c of the memory 36 in advance. An arithmetic program based on a genetic algorithm for determining the order of mounting the electronic components 24 on the substrate 16 is executed. The determined arrangement position and mounting order are stored in the optimum value area 36b in the memory 36, and the electronic component 24 is mounted on the board by controlling the suction unit 26 and the XY unit 32 based on the data of the arrangement position and the mounting order. I do.

Each feeder 14 has the same type of electronic component 2
2 are housed in recesses provided at predetermined intervals in the tape 42 as shown in the enlarged view of FIG. 2, and the electronic component 24 is moved to the suction position of the suction unit 26 by feeding the tape 42 at a fixed size. It is configured to be supplied sequentially. The feeding operation of the tape 42 of the feeder 14 is controlled by the control unit 40 and interlocks with the movement of the suction unit 26. With the above configuration, the mounter head 22 moves to the feeder 14 and the electronic component 24 supplied to the suction position is moved.
The suction unit 26 is moved downward to be sucked and lifted upward, and the electronic component is conveyed upward while rotating the electronic component in consideration of the mounting angle on the board 16. Thereafter, the suction unit 26 is moved downward to mount the electronic component 24 at a predetermined position on the substrate 16, and then, after releasing the suction, is moved upward, and the next feeder 1 is sucked to suck the next electronic component 24.
Move in four directions. By repeating this operation,
The electronic component mounter 10 can sequentially mount predetermined electronic components 24 on the electronic component mounting site on the board 16 that has been transported.

Here, in order to efficiently mount the electronic components 24 on the board 16 in a short time, the mounting order of the electronic components 24 on the board 16 and the electronic component supply unit 12 of the feeder 14 are required.
Needs to be considered. By optimizing at least one of these, the mounter head 2
2, the total moving distance or the total moving time can be further reduced, and the electronic component 24 can be mounted efficiently. If both are optimized, the total moving distance or the total moving time can be further reduced.

Next, the operation of the electronic component mounting machine 10 will be described with reference to the flowcharts of FIGS. First, the overall operation of the electronic component mounter 10 will be described with reference to the flowchart of FIG. In step 101, basic data is input to the electronic component mounting machine 10 from the input I / F 38 as described above. The input basic data (the type and number of the feeders 14, the mounting position and the type of the electronic component 24 on the substrate 16,
Number of suction units 26, substrate 16 and electronic component supply unit 12
) Is stored in a memory 36 by the microcomputer 34.
Is stored in the basic data area 36a.

In step 102, the microcomputer 34 executes an arithmetic program stored in the memory 36 and, based on the basic data, arranges the feeder 14 which is closer to the optimum one in the electronic component supply section 12 and / or
Alternatively, the order in which the electronic components 24 are mounted on the substrate 16 is obtained,
It is stored in the optimum value area 36b in the memory 36. In step 103, the microcomputer 34
The XY unit 32 is moved based on the arrangement position of the feeder 14 in the electronic component supply unit 12 and / or the mounting order of the electronic component 24 on the substrate 16 stored in the optimum value area 36b in the 6 to move the mounter head 22. The electronic component 24 is moved between the electronic component supply unit 12 and the board 16, and the suction unit 26 is operated to suck the electronic component 24 by the feeder 14 and mount the electronic component 24 on the board 16. When all the predetermined electronic components 24 are mounted on the substrate 16, the mounting operation of the substrate 16 ends, and the electronic components 24 are mounted on the new substrate 16 conveyed on the base 30 in the same order. This mounting operation is repeated.

Subsequently, step 1 which is a feature of the present invention is described.
An arithmetic program to which the genetic algorithm in 02 is applied will be described with reference to the flowchart in FIG.
The feeder mounting section 18 in the electronic component mounting machine 10
A description will be given using a model in which the planar positional relationship between (B00 to B11) and the mounting position (J00 to J23) of the electronic component 24 in the substrate 16 is as shown in FIG. 6 as an example. Further, it is assumed that the types of the electronic components 24 mounted on the substrate 16 are five types (P, Q, R, S, T) as an example.

First, in step 201, an initial population is generated. Here, the individual is the substrate 16 of the electronic component 24.
The character data (elements) indicating the mounting order of the feeder 14 and the arrangement position of the feeder 14 in the electronic component supply unit 12 are concatenated, and are represented by an arrangement order of the mounting order and an arrangement position. That is,
The mounting order is expressed by arranging the mounting positions (J00 to J23) in the mounting order. The disposition positions are five types of electronic components 24.
Feeder mounting part 1 to which (P, Q, R, S, T) is mounted
8 (B00 to B11). One specific example of an individual is shown below. The array in the first half with J is the array indicating the mounting order (first array, consisting of 24 elements), and the array in the second half with B is the array of arrangement positions (second array, 5 elements). Is composed). (J01, J08, J15, ...
.., J02, J03: B01, B04, B09, B05, B07) Such individual units are individually combined with the mounting position (J00 to J23) and the feeder mounting portion 18 (B00 to B11) to have different first arrangements. , And second arrays (m is a natural number of 2 or more) are created and connected to generate m arrays. Each generated individual is stored as character string data in the calculation area 36d of the memory 36.

Next, in step 202, the total moving distance or total moving time is calculated and obtained as an evaluation value for each of the m individuals based on the elements indicating the mounting order and the arrangement position. In this case, an individual having a smaller evaluation value, that is, an individual having a shorter moving distance or a shorter moving time has a higher evaluation, and an individual having the highest evaluation, that is, an individual having the smallest evaluation value is the optimum individual at that time. In step 203, it is determined whether or not the evaluation value of the optimal individual has become smaller than a predetermined moving distance or moving time, or whether or not a predetermined number of generations has been repeated. The calculation program ends. Also,
If the conditions are not satisfied, the process proceeds to step 204. For example, individuals with low evaluations, that is, individuals with high evaluation values are selected, individuals are crossed over, or a mutation method is applied to elements constituting one individual. The next individual group is generated by changing the element by applying, and the process proceeds to step 202.

Thereafter, steps 202 to 204 are repeated until the condition of step 203 is satisfied. In the genetic algorithm, this repeating unit is called a generation.
The one with the smallest evaluation value in the final evaluation is the final optimal individual. When the next group of individuals is generated in step 204, for example, at least a individuals (a is a natural number) are left in the descending order of the evaluation,
Selection, crossover, and mutation are applied to the remaining ma individuals to generate ma individuals constituting the next population, and the remaining m individuals are added to generate the next m individuals. The number of individuals is appropriate because the evaluation value of the best individual is the same or improved with each generation.

Here, crossover and mutation, which are genetic operations for changing individuals in the genetic algorithm, will be described. First, in the crossover, for example, in each of the first array and the second array of the two individuals A and B selected by the uniform random number, a common position for each array is determined by the uniform random number. Then, for each of the first array and the second array, for example, an operation of exchanging an element sequence on the right side of the determined position to generate two next individuals is referred to. FIG. 7 shows an example of the crossover operation. In the two individuals A and B selected by the uniform random number, a common position is determined for each of the first array and the second array by the uniform random number, and each array is located on the right side of the position (broken line). By exchanging the element sequence, the next individual is generated. In order to prevent the generation of individuals (individuals containing the same element in one array) that cannot be solved due to crossover and mutation, the array example of the individuals in FIG. It is an example. Next, mutation refers to an operation of converting a part or all of elements for each individual into other elements and generating the next individual according to a preset probability (mutation rate).

As described above, when optimizing a system represented by variables having so-called discrete values for which an objective function cannot be defined, such as a problem of optimizing the total moving distance or the total moving time of the mounter head 22, In order to determine the optimal value of the arrangement position of the feeder 14 and the mounting order of the electronic components 24 on the board 16 by using the genetic algorithm as described above, a huge number of feeders can be arranged in the electronic component supply unit 12. A practically sufficient array can be searched from the array of positions in a realistic time.

In the above-described embodiment, the individuals are represented by connecting the arrangement of the mounting order and the arrangement of the feeder arrangement positions. For example, the mounting order of the electronic components 24 on the substrate 16 is predetermined. In this case, the individual may be represented only by the arrangement of the feeder arrangement positions, or when the arrangement position of the feeder is predetermined, the individual may be represented only by the arrangement of the mounting order of the electronic components 24. .

FIG. 8 shows the results of a search performed over 30 generations under the following conditions. The average value of the evaluation values of the population (in this example, the total moving distance, that is, the path length) is shown together with the average. The evaluation value of the individual also decreases gradually and converges to the optimal solution. Here, the number of electronic components is 24 and the electronic component supply unit 12
The number of the feeder mounting portions 18 of the feeder 14 in the
The number of feeders 14 and the number of feeders 14 were five, and the number of suction heads 26 attached to the mounter head 22 was one. The mounting order of the electronic components was determined in advance, and the optimum combination of the arrangement positions of the feeders 14 was searched. . First, 58 individual groups are generated with uniform random numbers, and the processing of the genetic algorithm shown in FIG. 5 is performed using the path length as an evaluation value. When calculating the path length, the positional relationship between the substrate 16 and the feeder mounting unit 18 in the electronic component supply unit 12, the types of the electronic components on the substrate 16 and the positions of the electronic components are as shown in FIG. When the mounter head 22 starts from the start position 44 and repeats the movement between the position of the feeder 14 and the position of the electronic component on the substrate 16 in the determined mounting order, the total path length is determined by the position scale 46. We calculate based on. In this case, the mounting order of the electronic components was determined to be from J00 to J23 in ascending numerical order.

Although some variation is found in the search results depending on the state of generation of the uniform random numbers, in the search results of 30 times in which the random number sequence is changed, the generation converging to the optimal solution is the shortest second generation and the longest generation. It converges on the twelfth generation and converges to the optimal solution on average on the 7.7th generation. FIG. 8 shows the search result converged to the eighth generation closest to the average. All combinations of the arrangement positions of the feeders 14 are smaller than the number of the feeder mounting portions 18 and the number of the feeders by ₁₂
C ₅ × 5! The number of combinations evaluated before converging to the optimal solution in the search result is calculated as 58 × 7.7 from the number of individuals per generation and the average number of convergent generations, and is calculated as about 447 times It can be seen that it is sufficiently smaller than the case of calculating for all combinations.

(Second Embodiment) The configuration and operation of the electronic component mounter according to the present embodiment are substantially the same as the configuration and operation of the electronic component mounter according to the first embodiment described above. The position of the feeder 14 and the electronic components 2 on the substrate 16
A part of the operation program using the genetic algorithm for finding the optimum value of the mounting order of No. 4 is different, and this difference is a feature of the present embodiment.

This characteristic point is, as apparent from the flowchart shown in FIG. 9, a step 20 for generating the next individual group.
In 4, the operations (selection, crossover,
Mutation), a new operation called rotation is added. First, this rotation will be described with reference to FIG. First, the number of elements to be shifted by a uniform random number in an element sequence constituting an individual is determined.
In FIG. 10, the number of elements to be shifted is four as an example. Then, each element constituting the individual is set to 4 in a predetermined direction (in FIG.
Shift one by one. The element that has reached the right end turns to the left end. That is, the elements constituting the individual are rotated and moved by a predetermined number in a predetermined direction. By this operation, a new individual can be generated.

The advantage of this operation of rotation is that a new individual can be generated without changing the elements constituting the individual and maintaining the adjacent relation between the elements constituting the individual as much as possible. In particular, in a state where a certain number of best individuals have been obtained, the first practice is to leave the best individuals as they are and apply a new crossover or mutation to the remaining individuals to generate the next population. Although the form method is good, there is a high possibility that a better optimal solution exists near the best individual. However, even if crossover or mutation is applied to this best individual, the constituent elements of the best individual will always change, and it is impossible to preserve the elements. The element position cannot be changed while holding. On the other hand, in the rotation, the arrangement of the best individual can be changed without changing the element, and the position of the element can be changed while maintaining the adjacent relation between the elements as much as possible. This has the effect of preserving important parts of the individual, making it possible to search for other local solutions in the vicinity of the local solution, preventing falling into a specific local solution, and converging to the optimal solution. The possibility of the time to shorten is increased.

For example, when there are a plurality of suction units 26, there are many partial element arrangement patterns capable of simultaneously adsorbing plural kinds of electronic components in the combined arrangement of the arrangement positions of the feeders 14; Individuals that have a significant effect on shortening the moving distance or the total moving time, and are composed of a combination array including such a partial element array pattern are the best individuals. However, even if the individual has the partial element array pattern as described above, the individual having the partial element array pattern arranged at a position close to the substrate 16 and the individual arranged at a position distant from the substrate 16 are different. Naturally, the evaluation of the individual arranged at a position close to the substrate 16 is higher, but when the conventional genetic algorithm is used, the partial element array pattern as described above is In some cases, even a placed individual becomes a local solution. Therefore, by using rotation for such an individual, it is possible to move the partial element array pattern throughout the entire combination array of the arrangement positions of the feeders 14 while maintaining the adjacent relation between the components. Thus, the position can be changed to a position closer to the substrate 16. From this, prevention of falling into a local solution,
The possibility of converging to the optimal solution earlier is increased.

The rotation is performed not only on the individual with the highest evaluation value (that is, the one with a small evaluation value) but also on the nth evaluation value from the highest evaluation value among the evaluation values because the individual elements do not change. (N is a natural number)
You may make it perform to several individuals with high evaluation.
This further increases the possibility that the time required to converge to the optimal solution is shortened because the important part of the sequence of the individual having a high evaluation value is not significantly changed.

As described above, various preferred embodiments of the present invention have been described. However, the present invention is not limited to the above-described embodiments, and many modifications can be made without departing from the spirit of the invention. Of course.

[0034]

According to the electronic component mounting optimization method according to the present invention, a huge number of feeders can be arranged in the electronic component supply unit by applying a genetic algorithm which is compatible with global search. A practically sufficient arrangement can be searched in a realistic time from the arrangement of the positions and the arrangement of the mounting order of the electronic components on the board. Also, if a new individual is generated by rotating the sequence of the individual by a predetermined number in a predetermined direction, in addition to the genetic operations in a general genetic algorithm, selection, crossover, mutation, Since a new genetic operation such as rotation is added, a more global search becomes possible. This operation of rotation has the effect of preserving important parts of the individual, preventing it from falling into a local solution, and increasing the possibility of shortening the time required to converge to the optimal solution. .

[Brief description of the drawings]

FIG. 1 is a perspective view showing a schematic structure of a general electronic component mounting machine.

FIG. 2 is a plan view of FIG.

FIG. 3 is a block diagram of an electronic component mounter that executes an electronic component mounting optimization method according to the present invention.

FIG. 4 is a flowchart showing the entire operation of FIG. 3;

FIG. 5 is a flowchart showing an operation of a first embodiment of an arithmetic program based on a genetic algorithm for determining a feeder arrangement position and / or an electronic component mounting order.

FIG. 6 is an explanatory diagram showing a feeder mounting portion and a mounting position of an electronic component on a board in one model of the electronic component mounter.

FIG. 7 is an explanatory diagram showing an operation of generating an individual by crossover.

8 is a graph showing a transition of an evaluation value (total moving distance of a mounter head, that is, a path length) when the electronic component mounting optimization method according to the present invention is executed on the model of FIG. 6;

FIG. 9 is a flowchart showing an operation of a second embodiment of an arithmetic program based on a genetic algorithm for obtaining a feeder arrangement position and / or an electronic component mounting order.

FIG. 10 is an explanatory diagram for explaining a rotation operation used in the calculation program of FIG. 9;

[Explanation of symbols]

 DESCRIPTION OF SYMBOLS 10 Electronic component mounting machine 12 Electronic component supply part 14 Feeder 16 Substrate 18 Feeder mounting part 22 Mounter head 24 Electronic component

 ──────────────────────────────────────────────────の Continued on the front page (72) Inventor Tatsuo Koitabashi 188 Wakasato, Nagano City, Nagano Prefecture Inside the Nagano Prefectural Industrial Experimental Station

Claims (4)

[Claims] A mounter head moves between an electronic component supply unit in which a plurality of feeders are arranged at predetermined positions and a substrate on which a plurality of types of electronic components are mounted, and removes the electronic components in the electronic component supply unit. In an electronic component mounting optimization method for optimizing a total moving distance or a total moving time of the mounter head in an electronic component mounting machine mounted on a board, connecting arrangement positions of the plurality of feeders in the electronic component supply unit. A predetermined number of individuals having the arrangement of the arrangement positions are generated, and a genetic algorithm is applied to each of the generated individuals using the total moving distance or the total moving time as an evaluation value to supply electronic components to a plurality of feeders. An electronic component mounting optimization method characterized by optimizing an arrangement position in a unit. 2. The method according to claim 1, wherein the individual is configured by connecting the arrangement of the arrangement positions and the arrangement of the mounting order of the electronic components on the board. When performing the operation to generate the next generation of individuals, the genetic operation is performed independently on one or both of the arrangement of the arrangement position and the arrangement of the mounting order of each individual, and mounting of electronic components. 2. The method for optimizing the mounting of electronic components according to claim 1, wherein the order is also optimized. 3. When generating a next-generation individual by performing a genetic operation in a genetic algorithm on each of the generated individuals, the array is oriented in a predetermined direction for one or more predetermined individuals. 2. A new individual is generated by a method of rotating the object by a predetermined number.
Or the electronic component mounting optimization method according to 2. 4. The electronic component mounting according to claim 3, wherein the rotation is performed on individuals having evaluation values from the highest evaluation value to the n-th evaluation value (n is a natural number) among the evaluation values. Optimization method.