A Component Allocation Method for Multifunctional Mounter Based on Iterative Binary Genetic Algorithm
A technology of genetic algorithm and allocation method, which is applied in the field of component allocation of multi-function placement machines based on iterative binary genetic algorithm, can solve the problems of insufficient search randomness and failure to output the optimal solution, etc., to increase diversity and improve production efficiency Effect
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specific Embodiment approach 1
[0035] Specific implementation mode one: combine Figure 6 This embodiment will be described. A method for allocating components of a multifunctional placement machine based on an iterative binary genetic algorithm described in this embodiment, the method is specifically implemented through the following steps:
[0036] Step 1: Import PCB component information;
[0037] Step 2: Group all placement points according to the component types in the imported PCB component information to obtain the initial component group, and perform step 3 with the initial component group as the current component group;
[0038] Step 3: use the current element group to initialize the chromosome population array, and then decode the coding information of each chromosome in the chromosome population array to obtain the fitness function value of each chromosome;
[0039] Step 4: Set the termination condition for iterative optimization of the chromosome population array;
[0040] Step 5: According t...
specific Embodiment approach 2
[0049] Specific embodiment two: the difference between this embodiment and specific embodiment one is that: in the step 1, the PCB component information is imported, and the imported PCB component information specifically includes:
[0050] Component type number information: "Cpc" indicates the c-type component, c=1,2,...,C, C represents the total number of component types;
[0051] Information on the number of mounting points corresponding to various components: Φ(c) indicates the number of mounting points corresponding to the c-type component;
[0052] Nozzle type information: "Nzn" indicates the nth nozzle type, n∈{1,2,...,N}, N represents the total number of nozzle types, η(c) indicates the nozzle corresponding to the c-th type component type index;
[0053] Feeder serial number information: "Fdf" means the f-th feeder, f=1, 2,..., F, F means the total number of feeders, ξ(c) means the corresponding component of the c-th type The serial number index of the feeder, ξ(c)∈{...
specific Embodiment approach 3
[0056] Specific implementation mode three: the difference between this implementation mode and specific implementation modes one to two is: the specific process of the step 2 is:
[0057] Group components of the same component type into one group to obtain the initial component group, and perform step 3 with the initial component group as the current component group;
[0058] Use CC{ψ} to represent component groups, the total number of component groups is Ψ, the index of component groups is ψ=1,2,...,Ψ, the total number of component groups Ψ is equal to the number of component types C;
[0059] Each component group contains two component information, where the first component information is the component type number, expressed as CC{ψ}(1)=c, and the second component information is the mounting point corresponding to the component type number Number, expressed as CC{ψ}(2)=Φ(c).
[0060] In the initial component group obtained from the component information in Table 1, CC{1}=[1...
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