CN103336855A - Two-dimensional irregular layout method based on multi-subpopulation particle swarm optimization - Google Patents
Two-dimensional irregular layout method based on multi-subpopulation particle swarm optimization Download PDFInfo
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Abstract
A two-dimensional irregular layout method based on an improved particle swarm algorithm comprises the following steps of: converting geometric figures of a sample wafer and a material into a train of two-dimensional coordinate intervals, judging whether the two-dimensional intervals of the sample wafer and the material are overlapped with a heuristic bottom-left searching algorithm to move the position of the sample wafer relative to the material, and executing an improved PSO (Particle Swarm Optimization) searching process. According to the method of dividing multiple subpopulations, the influence of subpopulation optimal solutions on particles in the subpopulations is added under the condition of not changing a parameter trending to the current optimal solution, when the iteration time of a particle swarm reaches the initially set maximum iteration time, the particles stop iteration, and the current globally optimal solution is obtained as the final layout scheme. The two-dimensional irregular layout method based on the improved particle swarm algorithm has good searching capability, and is high in searching speed, good in final solution and good in layout effect.
Description
Technical field
The present invention relates to area of computer aided stock layout technology, especially a kind of stock layout method.
Background technology
At present, the stock layout method to irregular part concentrates in the didactic modern optimization method substantially both at home and abroad, mainly contains simulated annealing, genetic algorithm, particle cluster algorithm (PSO) etc.In practice, because simulated annealing is subjected to the influence of annealing speed, speed is fast, is absorbed in local extremum easily, and speed then is difficult to satisfy people's needs slowly; Genetic algorithm is then often than being easier to " precocity ", and local search ability is relatively poor; The PSO algorithm has stronger local search ability, but also is absorbed in extremal region easily and is difficult to jump out.
The range of application of stock layout problem is very extensive, as panel beating stock layout, glass tailor, cloth-cutting etc. because above-mentioned industry turnout is big, particularly some material is comparatively expensive, therefore, the raising of stock layout efficiency of algorithm can produce very big social benefit.
Summary of the invention
In order to overcome being absorbed in extremal region easily, having influenced the deficiency of stock layout effect of existing stock layout method, the invention provides a kind of when having good search capability, search speed and soon, finally separate the two-dimentional irregular stock layout method based on many subgroups particle cluster algorithm preferable, that stock layout is respond well.
The technical solution adopted for the present invention to solve the technical problems is:
A kind of two-dimentional irregular stock layout method based on many subgroups particle cluster algorithm may further comprise the steps:
The first step is converted to a series of two-dimensional coordinate interval with the geometric figure of print and material, uses the left side searching algorithm of the heuristic end to judge that whether overlapping two dimension interval of print and material come mobile print with respect to the position in the material then;
In second step, modified PSO search procedure is as follows:
1) inverse of the height after entering with print is as fitness value, and fitness value 1/H is more big, and then to enter effect more good;
2) state that enters of each print mainly contains three kinds: enter order, the anglec of rotation and mirror image, and the described span 1～n that enters order order, n is the print sum; 0 °～360 ° of the spans of anglec of rotation angle, whether mirror image mirror represents that about the y rotational symmetry, 0～1,0 expression is not in relation to the y rotational symmetry, 1 expression is about the y rotational symmetry;
3) with 2) in three parameters proposing as three elements of the elementary particle among the constituent particle group, the described elementary particle of random initializtion;
4) calculate the European geometric position of each particle, according to the order from small to large from the initial point distance all particles are divided into M subgroup, M＜n;
5) each parameter arranges as follows:
x
ij=〈order
ij，angle
ij，mirror
ij〉
J particle position vector in i the subgroup;
v
ij=〈v_ord
ij，v_ang
ij，v_mir
ij〉
J particle's velocity vector in i the subgroup;
p
ij=〈p_ord
ij，p_ang
ij，p_mir
ij〉
The historical optimum position vector of j particle in i subgroup;
psg
i=〈psg_ord
i，psg_ang
i，psg_mir
i〉
Historical optimum position, i subgroup vector;
p
g=〈pg
ord，pg
ang，pg
miri〉
Global history optimum position vector;
6) more new formula is as follows particle's velocity and position:
v
ij(d＋1)=w×v
ij(d)＋c
1×rand
1ij×[p
ij(d)-x
ij(d)]
+c
2×rand
2ij×[psg
i(d)-x
ij(d)]
+c
3×rand
3ij×[p
g(d)-x
ij(d)]
x
ij(d+1)=x
ij(d)+v
ij(d+1)
Wherein d is iterations, c
l, c
2, c
3The historical optimum solution of the particle of expression trend respectively itself, the optimum solution of subgroup, the speed controlling elements of globally optimal solution, wherein c
3＞c
2＞c
l＞0, rand
1ij, rand
2ij, rand
3ijBe the random factor between 0～1, w is inertial factor, the value w(d of w) linear decrease with the increase of iterations:
w(d)=u-v×d/D
D is maximum iteration time, u, and the value of v satisfies;
7) upgrade historical optimum solution
Each particle carries out entering in the material by the left side searching algorithm of the heuristic end after speed upgrades, and calculates the fitness value F of particle, thus the historical optimal location of new particle itself more, subgroup and overall optimal location;
During the maximum iteration time 8) set when the iterations of population has reached initial, particle stops iteration, obtains current globally optimal solution as final layout project; If do not reach maximum iteration time, continue to begin to carry out from step 6).
Further, in the described step 7), when more excellent solution is not all found in certain subgroup in continuous n iterative process, judge that this subgroup has been absorbed in locally optimal solution, to the replacement at random of described subgroup.
Further, in the described first step, it is as follows that the left side searching algorithm of the heuristic end obtains process: print enters from the lower left corner of material earlier, the print of back is turned right and is entered successively, when print exceeds the right side of material, print with respect to moving on the material, and is begun to search for again the remaining space of material from the left side, so move in circles, all enter or print stops when overflowing the material top until print.
Technical conceive of the present invention is: by dividing the method for a plurality of subgroups, under the situation of the parameter that does not change the current optimum solution of trend, added the influence of subgroup optimum solution to particle in the subgroup, strengthen local search ability with this, and when being absorbed in local extremum in the subgroup, replacement is eliminated in the subgroup, make colony have an opportunity to jump out local extremum.
Beneficial effect of the present invention mainly shows: when having good search capability, search speed soon, finally separate preferable, stock layout is respond well.
Description of drawings
Fig. 1 is the program flow diagram of two-dimentional irregular stock layout underlying algorithm.
Fig. 2 is the program flow diagram of modified PSO algorithm.
Fig. 3 overlaps clothes print based on stock layout design sketch of the present invention for certain.
Embodiment
Below in conjunction with accompanying drawing the present invention is further described.
With reference to Fig. 1～Fig. 3, a kind of two-dimentional irregular stock layout method based on many subgroups particle cluster algorithm may further comprise the steps:
The first step, the selection of two-dimentional irregular stock layout underlying algorithm
For the composing of handling two-dimentional irregular print, at first need to determine the profile of print, judge in the process whether each print can be overlapping in order to enter at print, the present invention selects the geometric figure of print and material is converted to a series of two-dimensional coordinate interval, thereby the complicacy that breaks away from irregular geometric figures is carried out stock layout, use heuristic end left side searching algorithm (HBLS) to judge that whether overlapping two dimension interval of print and material come mobile print with respect to the position in the material then, finish the underlying algorithm of two-dimentional irregular stock layout with this, its flow process can be with reference to Fig. 1.
The basic ideas of HBLS algorithm are: print enters from the lower left corner of material earlier, the print of back is turned right and is entered successively, when print exceeds the right side of material, with print with respect to moving on the material, and begin to search for again the remaining space of material from the left side, so move in circles, all enter or print stops when overflowing the material top until print.
Second step, modified PSO searching algorithm
1) print of layer with the end enters algorithm, just can pass to underlying algorithm by the state parameter that searching algorithm enters print handles, thereby calculate the inverse (1/H) of the height after each individual fitness value F(the present invention enters with print as fitness value, fitness value is more big, and then to enter effect more good).
2) state that enters of each print mainly contains three kinds: enter order (order, 1～n, n are the print sum), the anglec of rotation (angle, 0 °～360 °), mirror image (mirror is about y rotational symmetry, 0～1)
3) with 2) in three parameters proposing as three elements of the elementary particle among the constituent particle group, the sum with particle is set at 30 here, and these 30 particles of random initializtion.
4) calculate the European geometric position of each particle, according to the order from small to large from the initial point distance 30 particles are divided into 5 parts, every part of 6 particles namely have been divided into 5 subgroups.The purpose of dividing according to European geometric distance is in order to allow each subgroup be distributed in zones of different in original state, to strengthen the ability of searching optimum of algorithm as far as possible.
5) each parameter arranges as follows:
x
ij=(order
ij,angle
ij,mirror
ij〉
J particle position vector in i the subgroup
v
ij=〈v_ord
ij，v_ang
ij，v_mir
ij〉
J particle's velocity vector in i the subgroup
p
ij=〈p_ord
ij，p_ang
ij,p_mir
ij〉
The historical optimum position vector of j particle in i subgroup
psg
i=〈psg_ord
i，psg_ang
i，psg_mir
i〉
Historical optimum position, i subgroup vector
p
g=<pg
ord，pg
ang，pg
miri>
Global history optimum position vector
6) more new formula is as follows particle's velocity and position:
v
ij(d+1)=w×v
ij(d)+c
1×rand
1ij,×[p
ij(d)-x
ij,(d)]
+c
2×rand
2ij×[psg
i(d)-x
ij(d)]
+c
3×rand
3ij×[p
g(d)-x
ij(d)]
x
ij(d+1)=x
ij(d)+v
ij(d+1)
Wherein d is iterations, and hence one can see that, and c is determined in the renewal in particle each generation by the data of previous generation
l, c
2, c
3The historical optimum solution of the particle of expression trend respectively itself, the optimum solution of subgroup, the speed controlling elements of globally optimal solution, wherein c
3＞c
2＞c
l＞0, rand
1ij, rand
2ij, rand
3ijBe the random factor between 0～1, w is inertial factor, and when w was big, algorithm was to the solution space extensive search; Otherwise algorithm is searched among a small circle, and the value of w is linear decrease with the increase of iterations:
w(d)=u-v×d/D
D is maximum iteration time, u, and the value of v satisfies O＜v＜u＜1.
In classical PSO algorithm, particle's velocity more new formula is:
v
ij(d+1)=w×v
ij(d)+c′
1×rand
1ij×[p
ij(d)-x
ij(d)]
+c′
3×rand
3ij×[p
g(d)-x
ij(d)]
With improved PSO algorithm parameter contrast, following relation is arranged:
c′
l=c
l+c
2
c′
3=c
3
Modified PSO algorithm has added the concept of subgroup, and has more added the influence of subgroup optimum solution in the new formula in speed, strengthens Local Search, but c
3Do not change, thereby guaranteed that algorithm convergence is not weakened in the trend of more excellent solution.
7) upgrade historical optimum solution
Each particle carries out entering in the material by bottom stock layout algorithm after speed upgrades, and calculates the fitness value F of particle, thus the more historical optimal location of new particle itself, subgroup and overall optimal location.
8) subgroup eliminates and reset mechanism
Be absorbed in the problem of locally optimal solution easily at the PSO algorithm, superseded and the reset mechanism that has added the subgroup of subgroup among the present invention, when more excellent solution is not all found in certain subgroup in continuous n iterative process, so, think that this subgroup has been absorbed in locally optimal solution, thereby can make this subgroup have an opportunity jump out locally optimal solution, strengthen the ability of searching optimum of algorithm by the replacement at random to the subgroup this moment.
During the maximum iteration time 9) set when the iterations of population has reached initial, particle stops iteration, obtains current globally optimal solution as final layout project.If do not reach maximum iteration time, continue to begin to carry out from step 6).
With reference to Fig. 3, stock layout design sketch of the present invention, (material width: 1200mmm, the print number: 18, iterations: 600, stock layout height H: 901mm, stock utilization: 78.1%).
Claims (3)
1. two-dimentional irregular stock layout method based on many subgroups particle cluster algorithm is characterized in that: may further comprise the steps:
The first step is converted to a series of two-dimensional coordinate interval with the geometric figure of print and material, uses the left side searching algorithm of the heuristic end to judge that whether overlapping two dimension interval of print and material come mobile print with respect to the position in the material then;
In second step, modified PSO search procedure is as follows:
1) inverse of the height after entering with print is as fitness value, and fitness value 1/H is more big, and then to enter effect more good;
2) state that enters of each print mainly contains three kinds: enter order, the anglec of rotation and mirror image, and the described span 1～n that enters order order, n is the print sum; 0 °～360 ° of the spans of anglec of rotation angle, whether mirror image mirror represents that about the y rotational symmetry, 0～1,0 expression is not in relation to the y rotational symmetry, 1 expression is about the y rotational symmetry;
3) with 2) in three parameters proposing as three elements of the elementary particle among the constituent particle group, the described elementary particle of random initializtion;
4) calculate the European geometric position of each particle, according to the order from small to large from the initial point distance all particles are divided into M subgroup, M＜n;
5) each parameter arranges as follows:
x
ij=〈order
ij,angle
ij,mirror
ij〉
J particle position vector in i the subgroup;
v
ij=〈v_ord
ij,v_ang
ij,v_mir
ij〉
J particle's velocity vector in i the subgroup;
p
ij=〈p_ord
ij，p_ang
ij，p_mir
ij)
The historical optimum position vector of j particle in i subgroup;
psg
i=〈Psg_ord
i，Psg_ang
i，psg_mir
i〉
Historical optimum position, i subgroup vector;
p
g=〈pg
ord，pg
ang，pg
miri〉
Global history optimum position vector;
6) more new formula is as follows particle's velocity and position:
v
ij(d+1)=w×v
ij(d)+c
1×rand
1ij×[p
ij(d)-x
ij(d)]
+c
2×rand
2ij×[psg
i(d)-x
ij(d)]
+c
3×rand
3ij×[p
g(d)-x
ij(d)]
x
ij(d+1)=x
ij(d)+v
ij(d+1)
Wherein d is iterations, c
1, c
2, c
3The historical optimum solution of the particle of expression trend respectively itself, the optimum solution of subgroup, the speed controlling elements of globally optimal solution, wherein c
3＞c
2＞c
l＞0, rand
1ij, rand
2ij, rand
3ijBe the random factor between 0～1, w is inertial factor, the value w(d of w) linear decrease with the increase of iterations:
w(d)=u-v×d/D
D is maximum iteration time, u, and the value of v satisfies;
7) upgrade historical optimum solution
Each particle carries out entering in the material by the left side searching algorithm of the heuristic end after speed upgrades, and calculates the fitness value F of particle, thus the historical optimal location of new particle itself more, subgroup and overall optimal location;
During the maximum iteration time 8) set when the iterations of population has reached initial, particle stops iteration, obtains current globally optimal solution as final layout project; If do not reach maximum iteration time, continue to begin to carry out from step 6).
2. a kind of two-dimentional irregular stock layout method based on many subgroups particle cluster algorithm as claimed in claim 1, it is characterized in that: in the described step 7), when more excellent solution is not all found in certain subgroup in continuous n iterative process, judge that this subgroup has been absorbed in locally optimal solution, to the replacement at random of described subgroup.
3. a kind of two-dimentional irregular stock layout method based on many subgroups particle cluster algorithm as claimed in claim 1 or 2, it is characterized in that: in the described first step, it is as follows that the left side searching algorithm of the heuristic end obtains process: print enters from the lower left corner of material earlier, the print of back is turned right and is entered successively, when print exceeds the right side of material, with print with respect to moving on the material, and begin to search for again the remaining space of material from the left side, so move in circles, all enter or print stops when overflowing the material top until print.
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Cited By (16)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN103678800A (en) * | 2013-12-09 | 2014-03-26 | 华南理工大学 | Method for stock layout of irregular parts on rectangular board |
| CN105488562A (en) * | 2015-11-27 | 2016-04-13 | 浙江工业大学义乌科学技术研究院有限公司 | Irregular part stock layout method based on multi-factor particle swarm algorithm |
| CN105740953A (en) * | 2016-01-29 | 2016-07-06 | 温州大学 | Irregular layout method based on real-coded quantum evolutionary algorithm |
| CN105845427A (en) * | 2016-04-08 | 2016-08-10 | 辽宁石油化工大学 | Particle swarm optimization-based core section design method for transformer |
| CN105956663A (en) * | 2016-03-04 | 2016-09-21 | 安徽工程大学 | Parallel stock layout optimization method of special shaped part blanking |
| CN105989605A (en) * | 2016-03-04 | 2016-10-05 | 安徽工程大学 | Irregular part blanking layout positioning method |
| CN106156534A (en) * | 2016-09-10 | 2016-11-23 | 天津大学 | A kind of special container panel beating based on Creo batch Nesting |
| CN106571610A (en) * | 2016-10-29 | 2017-04-19 | 南京理工大学 | Power transmission line mechanical deicing order optimization method based on particle swarm algorithm |
| CN106959608A (en) * | 2017-02-27 | 2017-07-18 | 同济大学 | A kind of water supply network seepage optimal control method based on cluster particle cluster algorithm |
| CN109214557A (en) * | 2018-08-15 | 2019-01-15 | 浙江优尼家装饰科技有限公司 | A kind of irregular plate Nesting of two dimension |
| CN111275243A (en) * | 2020-01-08 | 2020-06-12 | 广东工业大学 | Two-dimensional irregular layout method for single-specification plate |
| CN111488686A (en) * | 2020-04-14 | 2020-08-04 | 常州纳捷机电科技有限公司 | Horizontal stripe distribution position matching layout method based on genetic algorithm |
| CN113032921A (en) * | 2021-03-16 | 2021-06-25 | 山东科技大学 | Layout algorithm based on parallel adaptive parameter cuckoo search and lowest horizontal line |
| CN113189553A (en) * | 2021-01-28 | 2021-07-30 | 北京理工大学 | Multi-modal optimization-based micro-maneuvering multi-target rapid detection method |
| CN113762607A (en) * | 2021-08-26 | 2021-12-07 | 甘肃同兴智能科技发展有限责任公司 | Prediction method for carbon emission of power grid enterprise |
| CN115629537A (en) * | 2022-12-22 | 2023-01-20 | 南京净环热冶金工程有限公司 | Heating furnace combustion control method and system based on subgroup improved particle swarm optimization PID |
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| CN103678800A (en) * | 2013-12-09 | 2014-03-26 | 华南理工大学 | Method for stock layout of irregular parts on rectangular board |
| CN105488562A (en) * | 2015-11-27 | 2016-04-13 | 浙江工业大学义乌科学技术研究院有限公司 | Irregular part stock layout method based on multi-factor particle swarm algorithm |
| CN105488562B (en) * | 2015-11-27 | 2017-11-10 | 浙江工业大学义乌科学技术研究院有限公司 | A kind of layout of polygon method based on multiple-factor particle cluster algorithm |
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| CN106571610A (en) * | 2016-10-29 | 2017-04-19 | 南京理工大学 | Power transmission line mechanical deicing order optimization method based on particle swarm algorithm |
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| CN109214557A (en) * | 2018-08-15 | 2019-01-15 | 浙江优尼家装饰科技有限公司 | A kind of irregular plate Nesting of two dimension |
| CN111275243A (en) * | 2020-01-08 | 2020-06-12 | 广东工业大学 | Two-dimensional irregular layout method for single-specification plate |
| CN111488686A (en) * | 2020-04-14 | 2020-08-04 | 常州纳捷机电科技有限公司 | Horizontal stripe distribution position matching layout method based on genetic algorithm |
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