CN107451692A - A kind of aviation Spares method for optimizing configuration based on artificial bee colony algorithm - Google Patents
A kind of aviation Spares method for optimizing configuration based on artificial bee colony algorithm Download PDFInfo
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- CN107451692A CN107451692A CN201710653510.2A CN201710653510A CN107451692A CN 107451692 A CN107451692 A CN 107451692A CN 201710653510 A CN201710653510 A CN 201710653510A CN 107451692 A CN107451692 A CN 107451692A
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- G06N3/004—Artificial life, i.e. computing arrangements simulating life
- G06N3/006—Artificial life, i.e. computing arrangements simulating life based on simulated virtual individual or collective life forms, e.g. social simulations or particle swarm optimisation [PSO]
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Abstract
The invention discloses a kind of aviation Spares method for optimizing configuration based on artificial bee colony algorithm, belong to aviation Spares configuring technical field.Object function fit=P/C is constructed first, wherein, C represents the total cost of purchase and storage aviation Spares, P represents the spare parts support probability of equipment, and sets majorized function, sets iterations afterwards, and be iterated using artificial bee colony algorithm, try to achieve the configuration quantity of various spare parts.The present invention uses artificial bee colony algorithm not only fast convergence rate, strong robustness, it is easy to accomplish, and global and local search can be all carried out in each iterative optimization procedure, the probability for finding optimal solution is increased, also avoids the possibility for being absorbed in locally optimal solution to a certain extent.
Description
Technical field
The invention belongs to aviation Spares configuring technical field, and in particular to a kind of aviation Spares based on artificial bee colony algorithm
Method for optimizing configuration.
Background technology
Rational aviation Spares configuration is to ensure that aircraft equipment recovers equipment war after normally training war preparedness and breaking down
An important factor for power that struggles against.Current aviation Spares safeguard work carries out the pipe of extensive style by the experience of administrative staff too much
Reason, can not meet the requirement of Efficient Support.Part warehouse storage is excessive, and spare parts support probability is easily guaranteed that but wastes a large amount of ensure and provides
Gold；Part warehouse storage is very few, can significantly reduce spare parts support probability and Military Equipment Operational Readiness.Therefore how reasonably to determine to navigate
The quantity required of empty spare part and the configuration mode of variety classes spare part, will directly affect the superiority of supply guarantee system, enter
And have influence on aircraft equipment Combat readiness and support cost.It is a more complicated multiple constraint that aviation Spares, which configure, non-thread
Property combinatorial optimization problem, optimization process is sufficiently complex, and calculation scale is big, and convergence rate is slow, is easily absorbed in locally optimal solution.
The content of the invention
In order to solve the above problems, the present invention proposes a kind of aviation Spares configuration optimization side based on artificial bee colony algorithm
Method, gathering honey is carried out according to the respective division of labor by simulating bee colony, and exchange nectar source information and find the process in optimal nectar source and optimize.
Methods described includes：
Step 1: construction object function fit：Fit=P/C, C represent the total cost of purchase and storage aviation Spares, and P is represented
The spare parts support probability of equipment,
Wherein, m represents the species number of aviation Spares, CiThe purchase of i-th kind of spare part of expression and storage charges are used, xiRepresent i-th kind
The configuration quantity of spare part, CmaxRepresent the total cost upper limit, PiRepresent the spare parts support probability of i-th kind of spare part, PminRepresent minimum standby
Part security probability, niRepresent the installation quantity of i-th kind of spare part, λiRepresent the fault rate of i-th kind of spare part, tiI-th kind of spare part of expression
Working time；
Step 2: initialization artificial bee colony algorithm parameter, determines that each bee colony scale is M, maximum iteration
Countmax, the transformation x of i-th kind of spare partimax, the maximum exploitation number Limit in nectar source, initialization current iteration number Count
=1, M feasible solution of random initializtion is as gathering honey honeybee, and M feasible solution of random initializtion is as observation honeybee, according to step 1 institute
State the target function value that each feasible solution is calculated；
Step 3: each gathering honey honeybee needs to carry out the search in new nectar source, calculation formula is as follows：
yij(Count+1)=yij(Count)+μ(yij(Count)-ykj(Count))
Wherein yij(Count) represent the Count times iteration in i-th of gathering honey honeybee j-th of parameter, i, k ∈ 1,2 ...,
M } and i ≠ k, j ∈ { 1,2 ..., m }, μ represent the random number in [- 1,1] section, the object function in newer nectar source and green molasses source
Value, takes and is preferably used as current nectar source corresponding to gathering honey honeybee；
Step 4: observation honeybee selects corresponding nectar source according to roulette mode：
Wherein, piRepresent that observation honeybee selects the probability in i-th of nectar source, fitiThe target function value in i-th of nectar source is represented, is selected
After selecting corresponding nectar source, observation honeybee is searched for according to step 3 in formula carry out field, if newly-generated observation honeybee is than corresponding
The target function value in nectar source is more excellent, then substitutes the gathering honey honeybee；
Step 5: if the exploitation number in nectar source reaches the maximum exploitation number Limit in nectar source, the nectar source is abandoned, the nectar source
Corresponding gathering honey honeybee is changed into observing honeybee, and directly new feasible solution is generated at random in solution space；
Step 6: current iteration number Count is increased into 1, current iteration number Count is judged, return to step three, until
Reach maximum iteration Countmax。
Preferably, in the step 1, minimum spare parts support probability PminFor 0.7-0.9.
Preferably, in the step 2, bee colony scale is no less than 50.
Preferably, in the step 2, iterations is no less than 50 times.
Preferably, in the step 2, the maximum exploitation number in nectar source is no less than 50 times.
Preferably, in the step 3, take preferably includes taking object function as current nectar source corresponding to gathering honey honeybee
It is worth larger corresponding nectar source as current nectar source.
The present invention uses artificial bee colony algorithm not only fast convergence rate, strong robustness, it is easy to accomplish, and in each iteration
Global and local search can be all carried out in optimization process, the probability for finding optimal solution is increased, also avoids to a certain extent
It is absorbed in the possibility of locally optimal solution.
Brief description of the drawings
Fig. 1 is the preferred embodiment according to aviation Spares method for optimizing configuration of the present invention based on artificial bee colony algorithm
Flow chart.
Fig. 2 is the iterative process schematic diagram of embodiment illustrated in fig. 1 of the present invention.
Embodiment
To make the purpose, technical scheme and advantage that the present invention is implemented clearer, below in conjunction with the embodiment of the present invention
Accompanying drawing, the technical scheme in the embodiment of the present invention is further described in more detail.In the accompanying drawings, identical from beginning to end or class
As label represent same or similar element or the element with same or like function.Described embodiment is the present invention
Part of the embodiment, rather than whole embodiments.The embodiments described below with reference to the accompanying drawings are exemplary, it is intended to uses
It is of the invention in explaining, and be not considered as limiting the invention.Based on the embodiment in the present invention, ordinary skill people
The every other embodiment that member is obtained under the premise of creative work is not made, belongs to the scope of protection of the invention.Under
Embodiments of the invention are described in detail with reference to accompanying drawing for face.
In the description of the invention, it is to be understood that term " " center ", " longitudinal direction ", " transverse direction ", "front", "rear",
The orientation or position relationship of the instruction such as "left", "right", " vertical ", " level ", " top ", " bottom " " interior ", " outer " is based on accompanying drawing institutes
The orientation or position relationship shown, it is for only for ease of the description present invention and simplifies description, rather than instruction or the dress for implying meaning
Put or element there must be specific orientation, with specific azimuth configuration and operation, therefore it is not intended that the present invention is protected
The limitation of scope.
Aviation Spares method for optimizing configuration of the invention based on artificial bee colony algorithm, as shown in figure 1, mainly including following step
Suddenly：
Step 1: construction object function fit：Fit=P/C, C represent the total cost of purchase and storage aviation Spares, and P is represented
The spare parts support probability of equipment,
Wherein, m represents the species number of aviation Spares, CiThe purchase of i-th kind of spare part of expression and storage charges are used, xiRepresent i-th kind
The configuration quantity of spare part, CmaxRepresent the total cost upper limit, PiRepresent the spare parts support probability of i-th kind of spare part, PminRepresent minimum standby
Part security probability, niRepresent the installation quantity of i-th kind of spare part, λiRepresent the fault rate of i-th kind of spare part, tiI-th kind of spare part of expression
Working time；
Step 2: initialization artificial bee colony algorithm parameter, determines that each bee colony scale is M, maximum iteration
Countmax, the transformation x of i-th kind of spare partimax, the maximum exploitation number Limit in nectar source, initialization current iteration number Count
=1, M feasible solution of random initializtion is as gathering honey honeybee, and M feasible solution of random initializtion is as observation honeybee, according to step 1 institute
State the target function value that each feasible solution is calculated；
Step 3: each gathering honey honeybee needs to carry out the search in new nectar source, calculation formula is as follows：
yij(Count+1)=yij(Count)+μ(uij(Count)-ykj(Count))
Wherein yij(Count) represent the Count times iteration in i-th of gathering honey honeybee j-th of parameter, i, k ∈ 1,2 ...,
M } and i ≠ k, j ∈ { 1,2 ..., m }, μ represent the random number in [- 1,1] section, the object function in newer nectar source and green molasses source
Value, takes and is preferably used as current nectar source corresponding to gathering honey honeybee；
Step 4: observation honeybee selects corresponding nectar source according to roulette mode：
Wherein, piRepresent that observation honeybee selects the probability in i-th of nectar source, fitiThe target function value in i-th of nectar source is represented, is selected
After selecting corresponding nectar source, observation honeybee is searched for according to step 3 in formula carry out field, if newly-generated observation honeybee is than corresponding
The target function value in nectar source is more excellent, then substitutes the gathering honey honeybee；
Step 5: if the exploitation number in nectar source reaches the maximum exploitation number Limit in nectar source, the nectar source is abandoned, the nectar source
Corresponding gathering honey honeybee is changed into observing honeybee, and directly new feasible solution is generated at random in solution space；
Step 6: current iteration number Count is increased into 1, current iteration number Count is judged, return to step three, until
Reach maximum iteration Countmax。
It is understood that step 1 gives majorized function fit, given restrictive condition-expense no more than setting
Value, and spare parts support probability cannot be below setting value.Under normal circumstances, the setting value of spare parts support probability-minimum spare part is protected
The setting of barrier probability is to fit to air standard, and generally value is 0.7-0.9, and requires relatively low to expense standard,
X in the present embodimentiTo be to be evaluated.
In the present embodiment, by taking m=5 as an example, illustrate.
The parameters such as the installation quantity of i-th kind of spare part, fault rate, working time are as shown in the table.
Each bee colony scale M is arranged to 100, maximum iteration Countmax100 are arranged to, the transformation of each spare part is equal
100 are arranged to, the maximum exploitation number Limit in nectar source is arranged to 50.
Each spare part quantity is respectively in the optimal case for optimizing to obtain according to this method：26、92、72、72、78.Iteration mistake
Journey is as shown in Figure 2.
The present invention uses artificial bee colony algorithm not only fast convergence rate, strong robustness, it is easy to accomplish, and in each iteration
Global and local search can be all carried out in optimization process, the probability for finding optimal solution is increased, also avoids to a certain extent
It is absorbed in the possibility of locally optimal solution.
It is last it is to be noted that：The above embodiments are merely illustrative of the technical solutions of the present invention, rather than its limitations.To the greatest extent
The present invention is described in detail with reference to the foregoing embodiments for pipe, it will be understood by those within the art that：It is still
Technical scheme described in foregoing embodiments can be modified, or which part technical characteristic is equally replaced
Change；And these modifications or replacement, the essence of appropriate technical solution is departed from the essence of various embodiments of the present invention technical scheme
God and scope.
Claims (6)
- A kind of 1. aviation Spares method for optimizing configuration based on artificial bee colony algorithm, it is characterised in that including：Step 1: construction object function fit：Fit=P/C, C represent the total cost of purchase and storage aviation Spares, and P represents equipment Spare parts support probability,<mrow> <mi>C</mi> <mo>=</mo> <munderover> <mo>&Sigma;</mo> <mrow> <mi>i</mi> <mo>=</mo> <mn>1</mn> </mrow> <mi>m</mi> </munderover> <msub> <mi>C</mi> <mi>i</mi> </msub> <msub> <mi>x</mi> <mi>i</mi> </msub> <mo>&le;</mo> <msub> <mi>C</mi> <mi>max</mi> </msub> </mrow><mrow> <msub> <mi>P</mi> <mi>i</mi> </msub> <mo>=</mo> <munderover> <mo>&Sigma;</mo> <mrow> <mi>k</mi> <mo>=</mo> <mn>0</mn> </mrow> <msub> <mi>x</mi> <mi>i</mi> </msub> </munderover> <mfrac> <mrow> <msup> <mrow> <mo>(</mo> <msub> <mi>n</mi> <mi>i</mi> </msub> <msub> <mi>&lambda;</mi> <mi>i</mi> </msub> <msub> <mi>t</mi> <mi>i</mi> </msub> <mo>)</mo> </mrow> <mi>k</mi> </msup> <msup> <mi>e</mi> <mrow> <mo>-</mo> <msub> <mi>n</mi> <mi>i</mi> </msub> <msub> <mi>&lambda;</mi> <mi>i</mi> </msub> <msub> <mi>t</mi> <mi>i</mi> </msub> </mrow> </msup> </mrow> <mrow> <mi>k</mi> <mo>!</mo> </mrow> </mfrac> </mrow><mrow> <mi>P</mi> <mo>=</mo> <munderover> <mo>&Pi;</mo> <mrow> <mi>i</mi> <mo>=</mo> <mn>1</mn> </mrow> <mi>m</mi> </munderover> <msub> <mi>P</mi> <mi>i</mi> </msub> <mo>&GreaterEqual;</mo> <msub> <mi>P</mi> <mi>min</mi> </msub> </mrow>Wherein, m represents the species number of aviation Spares, CiThe purchase of i-th kind of spare part of expression and storage charges are used, xiRepresent i-th kind of spare part Configuration quantity, CmaxRepresent the total cost upper limit, PiRepresent the spare parts support probability of i-th kind of spare part, PminRepresent that minimum spare part is protected Hinder probability, niRepresent the installation quantity of i-th kind of spare part, λiRepresent the fault rate of i-th kind of spare part, tiRepresent the work of i-th kind of spare part Time；Step 2: initialization artificial bee colony algorithm parameter, it is M, maximum iteration Count to determine each bee colony scalemax, i-th The transformation x of kind spare partimax, the maximum exploitation number Limit in nectar source, current iteration number Count=1 is initialized, it is random first M feasible solution of beginningization is calculated as gathering honey honeybee, M feasible solution of random initializtion as observation honeybee according to described in step 1 The target function value of each feasible solution；Step 3: each gathering honey honeybee needs to carry out the search in new nectar source, calculation formula is as follows：yij(Count+1)=yij(Count)+μ(yij(Count)-ykj(Count))Wherein yij(Count) j-th of parameter of i-th of gathering honey honeybee in the Count times iteration, i, k ∈ { 1,2 ..., M } and i are represented ≠ k, j ∈ { 1,2 ..., m }, μ represent the target function value of the random number in [- 1,1] section, newer nectar source and green molasses source, take Preferably it is used as current nectar source corresponding to gathering honey honeybee；Step 4: observation honeybee selects corresponding nectar source according to roulette mode：<mrow> <msub> <mi>p</mi> <mi>i</mi> </msub> <mo>=</mo> <mfrac> <mrow> <msub> <mi>fit</mi> <mi>i</mi> </msub> </mrow> <mrow> <msubsup> <mo>&Sigma;</mo> <mrow> <mi>k</mi> <mo>=</mo> <mn>1</mn> </mrow> <mi>M</mi> </msubsup> <msub> <mi>fit</mi> <mi>k</mi> </msub> </mrow> </mfrac> </mrow>Wherein, piRepresent that observation honeybee selects the probability in i-th of nectar source, fitiThe target function value in i-th of nectar source is represented, is chosen Behind corresponding nectar source, observation honeybee is searched for according to step 3 in formula carry out field, if newly-generated observation honeybee is than corresponding nectar source Target function value it is more excellent, then substitute the gathering honey honeybee；Step 5: if the exploitation number in nectar source reaches the maximum exploitation number Limit in nectar source, the nectar source is abandoned, the nectar source is corresponding Gathering honey honeybee be changed into observe honeybee, directly generate new feasible solution at random in solution space；Step 6: current iteration number Count is increased into 1, current iteration number Count, return to step three are judged, until reaching Maximum iteration Countmax。
- 2. the aviation Spares method for optimizing configuration based on artificial bee colony algorithm as claimed in claim 1, it is characterised in that described In step 1, minimum spare parts support probability PminFor 0.7-0.9.
- 3. the aviation Spares method for optimizing configuration based on artificial bee colony algorithm as claimed in claim 1, it is characterised in that described In step 2, bee colony scale is no less than 50.
- 4. the aviation Spares method for optimizing configuration based on artificial bee colony algorithm as claimed in claim 1, it is characterised in that described In step 2, iterations is no less than 50 times.
- 5. the aviation Spares method for optimizing configuration based on artificial bee colony algorithm as claimed in claim 1, it is characterised in that described In step 2, the maximum exploitation number in nectar source is no less than 50 times.
- 6. the aviation Spares method for optimizing configuration based on artificial bee colony algorithm as claimed in claim 1, it is characterised in that described In step 3, take preferably includes taking the larger corresponding nectar source conduct of target function value as current nectar source corresponding to gathering honey honeybee Current nectar source.
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| CN109508847A (en) * | 2018-04-10 | 2019-03-22 | 国网浙江省电力有限公司嘉兴供电公司 | A kind of electric spare unit method for early warning of guarantor |
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Cited By (9)
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| CN109472386A (en) * | 2018-04-10 | 2019-03-15 | 国网浙江省电力有限公司嘉兴供电公司 | A kind of determining method for protecting electric spare unit amount of purchase |
| CN109472387A (en) * | 2018-04-10 | 2019-03-15 | 国网浙江省电力有限公司嘉兴供电公司 | A kind of electric spare unit demand predictor method of guarantor |
| CN109473971A (en) * | 2018-04-10 | 2019-03-15 | 国网浙江省电力有限公司嘉兴供电公司 | Guarantor's electricity spare unit dispatching method based on GIS |
| CN109508847A (en) * | 2018-04-10 | 2019-03-22 | 国网浙江省电力有限公司嘉兴供电公司 | A kind of electric spare unit method for early warning of guarantor |
| CN109508847B (en) * | 2018-04-10 | 2022-07-01 | 国网浙江省电力有限公司嘉兴供电公司 | Electricity-protecting spare part early warning method |
| CN109473971B (en) * | 2018-04-10 | 2022-07-22 | 国网浙江省电力有限公司嘉兴供电公司 | GIS-based power protection spare part scheduling method |
| CN110287523A (en) * | 2019-05-16 | 2019-09-27 | 中国人民解放军海军工程大学 | The spare part scheme optimization method and device of multiple batches of component under modularization storage mode |
| CN111177642A (en) * | 2019-12-24 | 2020-05-19 | 中国航空工业集团公司西安飞机设计研究所 | Method for predicting requirement of spare parts of aviation materials |
| CN111178620A (en) * | 2019-12-24 | 2020-05-19 | 中国航空工业集团公司西安飞机设计研究所 | Maintenance support point site selection optimization method |
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