CN110298468A - A kind of single dimension chain optimization matching method based on ant group algorithm - Google Patents

A kind of single dimension chain optimization matching method based on ant group algorithm Download PDF

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CN110298468A
CN110298468A CN201810246257.3A CN201810246257A CN110298468A CN 110298468 A CN110298468 A CN 110298468A CN 201810246257 A CN201810246257 A CN 201810246257A CN 110298468 A CN110298468 A CN 110298468A
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pheromones
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杨余旺
陈响洲
徐雷
夏吉安
徐益民
徐辉
郑天镱
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Nanjing University of Science and Technology
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Abstract

The invention discloses a kind of, and single dimension chain based on ant group algorithm optimizes matching method.This method is as follows: reading part size information from database and dimension chain forms information；Transition probability of each part relative to current part in next column part class is calculated, chooses the part of maximum probability as part to be matched；Random number is generated, using the part in previous step as matching part if the random number is less than probability threshold value, is otherwise rounded by the product of probability and part remainder to select next part；Taboo list is written in currently selected part；Be repeated up to all part classes have a part with match, form a complete dimension chain；Local updating pheromones record the routing information and fitted position value of present combination；All parts are repeated up to all to complete to match；The overall situation updates pheromones, finds the apolegamy sequence of maximum assembling quality, and database is written in apolegamy result.The method of the present invention has many advantages, such as that apolegamy speed is fast, ability of searching optimum is strong in assembly Combinatorial Optimization.

Description

A kind of single dimension chain optimization matching method based on ant group algorithm
Technical field
The present invention relates to computer aided optimums to match technical field, especially a kind of single dimension chain based on ant group algorithm Optimize matching method.
Background technique
Computer Aided Selection assembly refers to that each group is manufactured by economic accuracy of machining at ring size in dimensional chain for assembly, is added It measures, and is put into corresponding parts library one by one after work, it is using computer that part to be assembled is quasi- by defined technology It then makes overall planning with economic criteria, the components volume residual after making apolegamy is minimum, and the quality of product is stablized.
There is common traditional optimization in engineering: genetic algorithm, simulated annealing, ant group algorithm etc..Genetic algorithm, The objective function and constraint condition of the non-numeric optimizations algorithm such as simulated annealing can be linear and nonlinear, can solve to mix Close continuously and discrete variable optimization problem, implementation procedure is relatively easy, but using genetic algorithm solved when be easy production Raw precocity phenomenon, local optimal searching ability are poor, and simulated annealing is poor to the control ability of search space, operation efficiency not yet It is high.
Known dimensional chain for assembly composition number of rings is n, and equal each group cyclization number of components is N, then whole assembled schemes have Nn, Wherein there is one group of group to be combined into optimum combination, is matched by the program, meet objective function.In order to obtain this assembled scheme, if Using the method for exhaustion, with the increase of n, N, assembled scheme is exponentially increased, and operation time will tend to the limit.
Summary of the invention
Single ruler based on ant group algorithm that speed is fast, ability of searching optimum is strong is matched the purpose of the present invention is to provide a kind of Very little chain optimizes matching method.
The technical solution for realizing the aim of the invention is as follows: a kind of single dimension chain optimization apolegamy side based on ant group algorithm Method, comprising the following steps:
Step 1: reading part size information from database and dimension chain forms information；
Step 2: initialization, including ant colony, pheromone concentration, the table of random numbers and taboo list；
Step 3: cycle rate counter NC adds one, records cycle-index；
Step 4: calculating transition probability of each part relative to current part in next column part class, choose maximum probability Part is as part to be matched；
Step 5: generate random number, compared with probability threshold value, if be less than probability threshold value if using the part in step 4 as It replaces the spare parts, if more than probability threshold value, is then rounded by the product of probability and part remainder to select next part；
Step 6: currently selected part is written taboo list, avoids selecting again by modification taboo list；
Step 7: repeating step 4~6, until all part classes have a part to participate in matching, composition one complete Dimension chain；
Step 8: calculating the fitted position value of present combination, local updating pheromones record the routing information of present combination With fitted position value；
Step 9: step 4~8 are repeated, until all parts are all completed to match；
Step 10: calculating assembly rate, assembly precision and assembling quality, the overall situation updates pheromones；
Step 11: judging whether that meeting apolegamy requires, if it is not, then repeating step 2~10；If so, finding maximum assembly The apolegamy sequence of quality, output apolegamy result；
Step 12: database is written in apolegamy result.
Further, transition probability of each part relative to current part in calculating next column part class described in step 4, It is specific as follows:
Ant ant determines the position to be shifted in next step during the motion, according to the information content on each paths, P(i+1)l(t) indicate that the probability for being transferred to position (i+1, l) by position (i, j) in t moment ant ant, l are just less than or equal to K Integer, expression formula are as follows:
In formula, α indicates the relative importance of pheromone concentration；β indicates the relative importance of expectation pheromones； τ(i+1)l(t) corresponding node p is indicated(i+1)lPheromone concentration；η(i+1)l(t) corresponding node p is indicated(i+1)lHeuristic information Element；K indicates next column part category number；
The next column node of ant is determined according to pseudo-randomness proportionality principle, ant selects next column node P' such as following formula institute Show:
Wherein, q indicates the equally distributed random number of obedience being randomly generated in [0,1] section；q0It indicates under ant selection The probability metrics threshold value of one node, n indicate the column part category number.
Further, local updating pheromones, formula described in step 8 are as follows:
τil=(1- ρ) τil+Δτil
Wherein, τilIndicate that the pheromone concentration of position (i, l), ρ indicate the dissipation speed of pheromones；Q indicates ant circulation The pheromones total amount discharged for one week；ΔτilIndicate that ant stays in the pheromones on position (i, l) in the circulating cycle；T0For according to zero The constant of part dimension information setting, N indicate part category sum；diFor the size of current location (i, l), d0For optimization path When the size of front ring.
Further, the overall situation described in step 10 updates pheromones, and formula is as follows:
Wherein, τilIndicate that the pheromone concentration of position (i, l), ρ indicate the dissipation speed of pheromones；Q indicates ant circulation The pheromones total amount discharged for one week；ΔτilIndicate that ant stays in the pheromones on position (i, l) in the circulating cycle；T0For according to zero The constant of part dimension information setting；diFor the size of current location (i, l), d0Work as the size of front ring for optimization path, m is indicated When the part category number of front ring.
Compared with prior art, the present invention its remarkable advantage are as follows: (1) ant group algorithm has apolegamy in assembly Combinatorial Optimization The advantages that speed is fast, ability of searching optimum is strong；(2) single dimension chain optimization apolegamy can be efficiently and accurately completed, after making apolegamy Components volume residual is minimum, and the quality of product is stablized.
Detailed description of the invention
Fig. 1 is that the present invention is based on the overview flow charts of single dimension chain of ant group algorithm optimization matching method.
The case where Fig. 2 is first ant of the invention rough schematic.
The case where Fig. 3 is second ant of the invention rough schematic.
Fig. 4 is single dimension chain burdening mode schematic diagram.
Specific embodiment
In conjunction with Fig. 1, the present invention is based on single dimension chains of ant group algorithm to optimize matching method, comprising the following steps:
Step 1: reading part size information from database and dimension chain forms information；
Step 2: initialization, including ant colony, pheromone concentration, the table of random numbers and taboo list；
Step 3: cycle rate counter NC adds one, records cycle-index；
Step 4: calculating transition probability of each part relative to current part in next column part class, choose maximum probability Part is as part to be matched；
Step 5: generate random number, compared with probability threshold value, if be less than probability threshold value if using the part in step 4 as It replaces the spare parts, if more than probability threshold value, is then rounded by the product of probability and part remainder to select next part；
Step 6: currently selected part is written taboo list, avoids selecting again by modification taboo list；
Step 7: repeating step 4~6, until all part classes have a part to participate in matching, composition one complete Dimension chain；
Step 8: calculating the fitted position value of present combination, local updating pheromones record the routing information of present combination With fitted position value；
Step 9: step 4~8 are repeated, until all parts are all completed to match；
Step 10: calculating assembly rate, assembly precision and assembling quality, the overall situation updates pheromones；
Step 11: judging whether that meeting apolegamy requires, if it is not, then repeating step 2~10；If so, finding maximum assembly The apolegamy sequence of quality, output apolegamy result；
Step 12: database is written in apolegamy result.
Further, transition probability of each part relative to current part in calculating next column part class described in step 4, It is specific as follows:
Ant ant determines the position to be shifted in next step during the motion, according to the information content on each paths, P(i+1)l(t) indicate that the probability for being transferred to position (i+1, l) by position (i, j) in t moment ant ant, l are just less than or equal to K Integer, expression formula are as follows:
In formula, α indicates the relative importance of pheromone concentration；β indicates the relative importance of expectation pheromones； τ(i+1)l(t) corresponding node p is indicated(i+1)lPheromone concentration；η(i+1)l(t) corresponding node p is indicated(i+1)lHeuristic information Element；K indicates next column part category number；
The next column node of ant is determined according to pseudo-randomness proportionality principle, ant selects next column node P' such as following formula institute Show:
Wherein, q indicates the equally distributed random number of obedience being randomly generated in [0,1] section；q0It indicates under ant selection The probability metrics threshold value of one node, n indicate the column part category number.
Further, local updating pheromones, formula described in step 8 are as follows:
τil=(1- ρ) τil+Δτil
Wherein, τilIndicate that the pheromone concentration of position (i, l), ρ indicate the dissipation speed of pheromones；Q indicates ant circulation The pheromones total amount discharged for one week；ΔτilIndicate that ant stays in the pheromones on position (i, l) in the circulating cycle；T0For according to zero The constant of part dimension information setting, N indicate part category sum；diFor the size of current location (i, l), d0For optimization path When the size of front ring.
Further, the overall situation described in step 10 updates pheromones, and formula is as follows:
Wherein, τilIndicate that the pheromone concentration of position (i, l), ρ indicate the dissipation speed of pheromones；Q indicates ant circulation The pheromones total amount discharged for one week；ΔτilIndicate that ant stays in the pheromones on position (i, l) in the circulating cycle；T0For according to zero The constant of part dimension information setting；diFor the size of current location (i, l), d0Work as the size of front ring for optimization path, m is indicated When the part category number of front ring.
Embodiment 1
Single dimension chain optimization matching method based on ant group algorithm is done combined with specific embodiments below and is further situated between in detail It continues.
1, architectural framework
It is simple to introduce:
Input: part size information (the dimension information P of each partij) and dimension chain composition information (dimension chain group cyclization Number n and each group cyclization number of components N)
Condition setting: ant colony, pheromone concentration, the table of random numbers and taboo list etc.；
Output:
Ant: the routing information and fitted position value of present combination judge whether equipment size value meets assembly and want It asks.
All ants: assembly rate, assembly precision and assembling quality are calculated.The apolegamy sequence of maximum assembling quality is found, if Meet, output apolegamy result.Then iteration again is not met.
In conjunction with Fig. 2, the case where first ant: wherein n=3；N=3；Q0=0.2；α=1；β=0, route completely random (according to the pseudorandom of the table of random numbers), it is assumed that P0-P11-P21-P31；Local updating pheromones P11, P21, P31 1+1, and root Mass parameter is calculated according to formula.
In conjunction with Fig. 3, the case where second ant: in P1 state, pheromones P11=1, P12=0, P13=0,
From formula: selecting the probability of P11 for 0.8* (1/3)+0.2, select P12, the probability of P13 is 0.8* (1/3). Explain: the 0.2 maximum part of probability selection pheromones, 0.8 probability randomly choose part.P2 state and P3 state are similarly. Assuming that P0-P11-P22-P32；Local updating pheromones P11 is 2+1, P22, P32 1+1, and calculates quality ginseng according to formula Number.
Remaining ant is similarly；
It takes the maximum dimension chain of mass parameter to judge whether after one wheel iteration eligible, meets, terminate, do not meet complete Office updates pheromones.Iteration again.
In above process, due to α=1；β=0, heuristic information element are not added, and convenient for explanation, heuristic letter is being added Breath element, i.e. when β is not 0, heuristic information element can be calculated by following formula, and the heuristic information element of each part is constant.
2, functional module forms
(1) problem describes
In conjunction with Fig. 4, dimension chain C has Pi type component composition, and each Pi type component is corresponding with n sample components and waits for Choosing, then all apolegamy assembled scheme has nm, wherein there is one group of group to be combined into optimum combination.In order to obtain this assembled scheme, herein Seek to obtain optimal or preferably assembly sequence using ant group algorithm.
Single dimension chain optimization mineral processing model can be described as:
There are m group cyclization for (1) dimension chain；
(2) each group of cyclization has n parts to be selected；
(3) one of them can only be chosen on demand from n parts to be selected every time；
(4) each part to be selected can only match once, i.e., can only occur in an apolegamy example；
(5) each apolegamy example have and only m group it is cyclic in a part；
(6) how to acquire it is all it is mating in the quality and quantity of qualified example all reach best.
(2) algorithm designs
A) state transition probability P(i+1)l(t): ant ant during the motion, is determined according to the information content on each paths The position to be shifted in next step, P(i+1)l(t) it indicates to be transferred to the general of position (i+1, l) by position (i, j) in t moment ant ant Rate, l are the positive integer less than or equal to K, and expression formula is as follows:
In formula, α indicates the relative importance of pheromone concentration；β indicates the relative importance of expectation pheromones； τ(i+1)l(t) corresponding node p is indicated(i+1)lPheromone concentration；η(i+1)l(t) corresponding node p is indicated(i+1)lHeuristic information Element；K indicates next column part category number；
A possibility that selection next column node of ant, need to determine according to pseudo-randomness proportionality principle, increase algorithm whereby Randomness is avoided converging on local solution, is shown below:
In formula, q indicates the equally distributed random number of obedience being randomly generated in [0,1] section；q0It indicates under ant selection The probability metrics threshold value of one node, n indicate the column part category number.
If q≤q0, algorithm determines the searching route of next step using the information in existing path, that is, selects the zero of maximum probability Part is as the next node of access, and otherwise algorithm starts to explore new path, after being rounded by random number and the product of parts count Numerical value select next node to be accessed.
B) the update rule of pheromones: different from practical ant colony, people's formula Ant ColonySystem has memory function, and set Cant is used Record the node that ant ant has currently passed by, set Cant will make dynamic with the searching process of ant and adjust.With when Between passage, the pheromones on path can die away in the past, the disappearance degree of pheromones be indicated with parameter 1- ρ, by one Section time ant completes a bit of path, and the information content on each paths does local updating, such as following formula (4-7) and formula (4-8) institute Show.Every completion one cycle carries out global update, as shown in following formula (4-9).Optimization apolegamy purpose be for global optimum, So the renewal amount of global information element is larger.By the update of the two pheromones, can be mentioned for the Path selection of next ant For foundation.
The local updating pheromones, formula are as follows:
τil=(1- ρ) τil+Δτil
Wherein, τilIndicate that the pheromone concentration of position (i, l), ρ indicate the dissipation speed of pheromones；Q indicates ant circulation The pheromones total amount discharged for one week；ΔτilIndicate that ant stays in the pheromones on position (i, l) in the circulating cycle；T0For according to zero The constant of part dimension information setting, N indicate part category sum；diFor the size of current location (i, l), d0For optimization path When the size of front ring.
The global update pheromones, formula are as follows:
Wherein, τilIndicate that the pheromone concentration of position (i, l), ρ indicate the dissipation speed of pheromones；Q indicates ant circulation The pheromones total amount discharged for one week；ΔτilIndicate that ant stays in the pheromones on position (i, l) in the circulating cycle；T0For according to zero The constant of part dimension information setting；diFor the size of current location (i, l), d0Work as the size of front ring for optimization path, m is indicated When the part category number of front ring.
C) calculating of heuristic information element: heuristic information element nijAlso known as it is expected pheromones, nijIt indicates to reach node pij's Expected degree, part solution corresponding to the path currently passed by based on specific optimization problem and ant, using constructing, this is excellent The experience (the greedy rule in such as critical path problem) for changing solution, judges the superiority and inferiority for selecting the connection, does not consider subsequent road The experience of diameter and other ants.Therefore, heuristic information element n is definedijIt is as follows:
In formula, p is the average value of all parts in same class；Pi is part actual size value.
(1) algorithm is realized
Human oasis exploited is different from practical ant, it needs certain memory, can remember the node accessed；And Artificial Ant Colony is not path that is stone-blind, but finding suitable consciously when selecting next paths yet, therefore, The structure of human oasis exploited and Artificial Ant Colony construction can indicate are as follows:
Error process based on ant group algorithm, flow chart is as shown in Figure 1:
Step 1: reading part size information from database and dimension chain forms information；
Step 2: initialization, including ant colony, pheromone concentration, the table of random numbers and taboo list etc.；
Step 3: cycle rate counter NC adds one, records cycle-index；
Step 4: calculating transition probability of each part relative to current part in next column part class；Choose maximum probability Part is as part to be matched；
Step 5: generate random number, compared with probability threshold value, if be less than probability threshold value if using the part in step 4 as It replaces the spare parts, if more than probability threshold value, then selects next part with being rounded with the product of part remainder by probability；
Step 6: currently selected part is written taboo list, avoids selecting again by modification taboo list；
Step 7: repeating step 4-5, until all part classes have a part to participate in matching, form a complete ruler Very little chain；
Step 8: calculating the fitted position value of present combination, local updating pheromones record the routing information of present combination With fitted position value；
Step 9: step 4-8 is repeated, until all parts are all completed to match；
Step 10: calculating assembly rate, assembly precision and assembling quality, the overall situation updates pheromones；
Step 11: judging whether that meeting apolegamy requires, if it is not, then repeating 2~10；If so, finding maximum assembling quality Apolegamy sequence, output apolegamy result；Step 12: database is written in apolegamy result.

Claims (4)

1. a kind of single dimension chain based on ant group algorithm optimizes matching method, which comprises the following steps:
Step 1: reading part size information from database and dimension chain forms information；
Step 2: initialization, including ant colony, pheromone concentration, the table of random numbers and taboo list；
Step 3: cycle rate counter NC adds one, records cycle-index；
Step 4: calculating transition probability of each part relative to current part in next column part class, choose the part of maximum probability As part to be matched；
Step 5: random number is generated, compared with probability threshold value, using the part in step 4 as matching zero if being less than probability threshold value Part is then rounded by the product of probability and part remainder if more than probability threshold value to select next part；
Step 6: currently selected part is written taboo list, avoids selecting again by modification taboo list；
Step 7: repeating step 4~6, until all part classes have a part to participate in matching, form a complete size Chain；
Step 8: calculating the fitted position value of present combination, local updating pheromones record the routing information and dress of present combination With size value；
Step 9: step 4~8 are repeated, until all parts are all completed to match；
Step 10: calculating assembly rate, assembly precision and assembling quality, the overall situation updates pheromones；
Step 11: judging whether that meeting apolegamy requires, if it is not, then repeating step 2~10；If so, finding maximum assembling quality Apolegamy sequence, output apolegamy result；
Step 12: database is written in apolegamy result.
2. single dimension chain according to claim 1 based on ant group algorithm optimizes matching method, which is characterized in that step 4 Transition probability of each part relative to current part in the calculating next column part class, specific as follows:
Ant ant determines the position to be shifted in next step, P during the motion, according to the information content on each paths(i+1)l(t) Indicate that the probability for being transferred to position (i+1, l) by position (i, j) in t moment ant ant, l are the positive integer less than or equal to K, table It is as follows up to formula:
In formula, α indicates the relative importance of pheromone concentration；β indicates the relative importance of expectation pheromones；τ(i+1)l (t) corresponding node p is indicated(i+1)lPheromone concentration；η(i+1)l(t) corresponding node p is indicated(i+1)lHeuristic information element；K table Show next column part category number；
Determine that the next column node of ant, ant selection next column node P' are shown below according to pseudo-randomness proportionality principle:
Wherein, q indicates the equally distributed random number of obedience being randomly generated in [0,1] section；q0Indicate that ant selects next node Probability metrics threshold value, n indicates the column part category number.
3. single dimension chain according to claim 1 based on ant group algorithm optimizes matching method, which is characterized in that step 8 The local updating pheromones, formula are as follows:
τil=(1- ρ) τil+Δτil
Wherein, τilIndicate that the pheromone concentration of position (i, l), ρ indicate the dissipation speed of pheromones；Q indicates that ant recycles one week The pheromones total amount discharged；ΔτilIndicate that ant stays in the pheromones on position (i, l) in the circulating cycle；T0For according to part ruler The constant of very little information setting, N indicate part category sum；diFor the size of current location (i, l), d0It is current for optimization path The size of ring.
4. single dimension chain according to claim 1 based on ant group algorithm optimizes matching method, which is characterized in that step 10 The global update pheromones, formula are as follows:
Wherein, τilIndicate that the pheromone concentration of position (i, l), ρ indicate the dissipation speed of pheromones；Q indicates that ant recycles one week The pheromones total amount discharged；ΔτilIndicate that ant stays in the pheromones on position (i, l) in the circulating cycle；T0For according to part ruler The constant of very little information setting；diFor the size of current location (i, l), d0Work as the size of front ring for optimization path, m indicates current The part category number of ring.
CN201810246257.3A 2018-03-23 2018-03-23 A kind of single dimension chain optimization matching method based on ant group algorithm Withdrawn CN110298468A (en)

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Cited By (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN113052494A (en) * 2021-04-20 2021-06-29 南京理工大学 Group string matching technology based on ant colony optimization algorithm
CN113357976A (en) * 2021-06-01 2021-09-07 南京海道普数据技术有限公司 Automatic matching system for workpiece grouping production line

Cited By (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN113052494A (en) * 2021-04-20 2021-06-29 南京理工大学 Group string matching technology based on ant colony optimization algorithm
CN113357976A (en) * 2021-06-01 2021-09-07 南京海道普数据技术有限公司 Automatic matching system for workpiece grouping production line

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