CN110059405A - High-quality Steiner minimum tree construction method with differential evolution under X structure - Google Patents
High-quality Steiner minimum tree construction method with differential evolution under X structure Download PDFInfo
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Abstract
The invention relates to a high-quality Steiner minimum tree construction method with differential evolution under an X structure. The method can effectively optimize the wire length of the wiring tree and realize the high-quality Steiner minimum tree construction method with differential evolution under the X structure.
Description
Technical field
Belong to Computer-aided Design Technology field the present invention relates to bright, and in particular to band differential evolution under a kind of X architecture
High quality Steiner minimum tree constructing method.
Background technique
Loose routing is an important link of VLSI designs, in loose routing, multiterminal gauze
Loose routing problem is to find the wiring tree problem of a given pin set, and Steiner minimum tree is as ultra-large collection
It is current super large-scale integration system to how to construct a Steiner minimum tree at the best model of circuit loose routing
Process requirement is made to solve the problems, such as.
Currently, most of algorithm for solving wiring problem is all to carry out related work by model basis of Manhattan structure,
But based on Manhattan structure carry out wire length optimization, since its wiring moves towards limited, can only horizontal cable run and vertical cabling, can not
Wiring area is fully utilized, the excessive redundancy of interconnection line resource is caused.Therefore the optimisation strategy based on Manhattan structure is carrying out
When interconnection line wire length optimizes, optimization ability is limited.
Summary of the invention
In view of this, the purpose of the present invention is to provide the high quality Steiner under a kind of X architecture with differential evolution is minimum
The X architecture Steiner minimum tree cabling scenario of high quality finally can be obtained using optimization wiring wire length as target in tree constructing method.
To achieve the above object, the present invention adopts the following technical scheme:
High quality Steiner minimum tree constructing method with differential evolution under a kind of X architecture, comprising the following steps:
Step S1: initializing population using Prim algorithm, obtains the population of initial solution；
Step S2: improved differential evolution algorithm and traditional differential evolution algorithm are constructed；
Step S3: judging whether algorithm iteration number reaches threshold value threshold, according to improved if not up to threshold value
Differential evolution algorithm carries out mutation operation to the population of initial solution；According to traditional differential evolution algorithm to first if reaching threshold value
The population for the solution that begins carries out mutation operation；
Step S4: based on greedy selection strategy, the optimum individual in selected population is entered in the next generation of population；
Step S4: circulation step S3-S4, until population iteration terminates, obtain globally optimal solution, as final wiring side
Case.
Further, the mutation operation formula of the improved differential evolution algorithm is as follows:
Wherein Xp1(g), Xp2(g), Xp3It (g) is g for three random individuals in population, and p1≠p2≠p3≠ i, Vi
It (g) is g for the variation individual that i-th of individual generates in population.
The formula of crossover operation is as follows:
Wherein Vi(g), Xi(g) difference g is for i-th of individual and i-th of variation individual, XM in populationiIt (g) is g generation kind
The intersection individual that i-th of individual and i-th of variation individual generate in group.
Wherein:The difference set result of A and B is sought in expression；If expression B is empty set, two o'clock is taken to become A
It is different；If B is empty, in conjunction with Union-find Sets strategy, it is added to element in A as side to be selected in B, if the side of B can be added in A
Adding rear B also is an illegal tree, then cabling mode when not connected point being linked to be while and initialized at random is added to B
In until B be a legal tree terminate；Indicate that A and B makees crossover operation according to probability cr.
Further, the mutation operation formula of traditional differential evolution algorithm is as follows:
Vi(g)=Xp1(g)+F*(Xp2(g)-Xp3(g))
Wherein Xp1(g), Xp2(g), Xp3It (g) is g for three random individuals in population, and p1≠p2≠p3≠ i, Vi
It (g) is g for the variation individual that i-th of individual generates in population.
Wherein, F uses adaptive strategy, and specific rules are as follows:
F in formulal=0.1, Fu=0.9, fb, fm, fwThree individual fitnesses respectively selected in mutation process from
Result after arriving small sequence greatly；
Crossover operation formula is as follows:
WhereinIndicate that j-th of bit of i-th of individual, cr use adaptive strategy, specific rules are as follows:
Wherein crl=0.1, cru=0.6, fi, fmin, fmax,The respectively adaptive value of current individual, it is minimum suitable in population
Individual should be worth, in population maximum adaptation value individual and population in average individual adaptive value.
Wherein it is as follows to calculate function for adaptive value:
Wherein TxIndicate the side collection set of wiring tree, l (ei) indicate side collection element eiLength.
Further, the greedy selection strategy specifically:
Wherein: f (XMi(g)) it indicates to intersect individual XMi(g) adaptive value, f (Xi(g)) current individual X is indicatedi(g) suitable
It should be worth, Xi(g+1) individual of next-generation population is indicated entry into.
Further, the threshold value threshold is set as 0.25 times that algorithm iteration number reaches maximum number of iterations.
Compared with the prior art, the invention has the following beneficial effects:
The present invention is based on minimum tree generation strategy, edge points to strategy, greedy selection strategy and New discrete differential evolution algorithm,
It can overcome the problems, such as limited because being routed wire length optimization ability under the structure of Manhattan.
Detailed description of the invention
Fig. 1 is the method for the present invention flow chart；
Fig. 2 is that X architecture Steiner minimum tree constructs example in one embodiment of the invention；
Fig. 3 is the cabling mode situation in one embodiment of the invention using edge point to the two pins under strategy；
Fig. 4 is the coding situation of examples of particles in population in one embodiment of the invention；
Fig. 5 is improved mutation operation one in operation operator design process in one embodiment of the invention；
Fig. 6 is improved mutation operation two in operation operator design process in one embodiment of the invention；
Fig. 7 is improved crossover operation in operation operator design process in one embodiment of the invention.
Specific embodiment
The present invention will be further described with reference to the accompanying drawings and embodiments.
Fig. 1 is please referred to, the present invention provides the high quality Steiner minimum tree constructing method with differential evolution under X architecture, packet
Include following steps:
Step S1: initializing population using Prim algorithm, obtains the population of initial solution；Using most in the present embodiment
The mode of small spanning tree method of formation initializes given pin point set, using the manhatton distance between pin point as
Weight carries out the construction of minimum spanning tree to the pin point set using Prim algorithm
Step S2: improved differential evolution algorithm and traditional differential evolution algorithm are constructed；
Step S3: judge whether algorithm iteration number reaches threshold value threshold i.e. algorithm iteration number and reach maximum and change
0.25 times of generation number carries out variation behaviour according to population of the improved differential evolution algorithm to initial solution if not up to threshold value
Make；Mutation operation is carried out according to population of traditional differential evolution algorithm to initial solution if reaching threshold value；
Step S4: based on greedy selection strategy, the optimum individual in selected population is entered in the next generation of population；
Step S4: circulation step S3-S4, until population iteration terminates, obtain globally optimal solution, as final wiring side
Case.
Improved differential evolution algorithm in the present embodiment, coding strategy is using edge point to encoded particles, so-called edge point pair
Coding is exactly to be used to record the spanning tree wire length by n-1 side of spanning tree, n-1 Steiner point selection mode and one
The redundant bit of (adaptive value) constitutes the coding to particle, and each particle represents a solution for indicating wiring problem.As Fig. 2 represents X
One example of structure Steiner minimum tree Construct question.Fig. 5 gives 4 kinds of selection modes of Steiner point, from left to right
Respectively 0 selection, 1 selection, 2 selections, 3 selections are mainly defined by the cabling mode on side.Fig. 6 gives wiring example (Fig. 4)
Particle coding situation, by bits of coded last it is found that therefore the wire length length of particle is 102.071.
Secondly as the problem of solved is discrete optimization problems of device, therefore, conventional differential evolution algorithm mutation operator is improved
It is as follows with crossover operator:
Define following set operation:
The difference set result of A and B is sought in expression
Indicate by the element in A withRule is added in B, until the condition that B, which meets operation, to be terminated terminates.
Indicate A and B withRule makees crossover operation
For this problemRule is are as follows:
1. taking two o'clock to make a variation A if B is empty set
2. in conjunction with Union-find Sets strategy, being added to element in A as side to be selected in B, if B is empty if can be added in A
Adding rear B also to the side of B is an illegal tree, then cabling mode when not connected point being linked to be while and initialized at random
It is added in B until B is that a legal tree terminates.
Then improved mutation operation formula is as follows:
Wherein Xp1(g), Xp2(g), Xp3It (g) is g for three random individuals in population, and p1≠p2≠p3≠ i, Vi
It (g) is g for the variation individual that i-th of individual generates in population.
As shown in figure 5, for the disposition of rule 1., will be selected at random during giving the mutation operation of algorithm
Three individual p1, p2, p3 generations into formula,For sky, then1. operation then uses rule, so-called two o'clock
The side m1 that variation randomly chooses a pending mutation operation to p1 individual is deleted, and p1 individual is divided into two stalks after deletion
Tree randomly selects two pin points from two stalk trees respectively in conjunction with the strategy of Union-find Sets, and line simultaneously walk by the random initializtion side
The individual generated after line mode as variation.
As shown in fig. 6, for the disposition of rule 2., will be selected at random during giving the mutation operation of algorithm
Three individual p1, p2, p3 generations into formula,It is not sky, then2. operation then uses rule, i.e., by p1
Side in individual is continuously addedResult in, in conjunction with Union-find Sets strategy, untilStructure
Terminate at a complete tree
According to operation as defined above, then the formula of improved crossover operation is as follows:
Wherein Vi(g), Xi(g) difference g is for i-th of individual and i-th of variation individual, XM in populationiIt (g) is g generation kind
The intersection individual that i-th of individual and i-th of variation individual generate in group.
As shown in fig. 6, provide the crossover operation process of algorithm, variation individual that mutation operation is generated and current individual into
Row crossing operation, hereinRule is as follows: the variation individual and current individual generate to variation carries out crossover operation and according to probability
Cr is intersected.As shown in Fig. 4-3, after mutation operation, the variation individual m of generation and when the one before i carry out crossover operation, intersect
The common edge individual using two as rise an initial line collection, remaining collection while as to be selected, in conjunction with Union-find Sets strategy, constantly to
Select while concentrate choose it is legal while be added to initial line collection until constituting a complete tree, using the result as being generated after intersection
Intersection individual.
In the present embodiment, when algorithm iteration number is more than that threshold value, that is, algorithm iteration number reaches maximum number of iterations
After 0.25 times, the cabling mode of opposite side is operated, therefore the population result obtained from early period directlys adopt conventional differential evolution
The mutation operator and crossover operator of algorithm are iterated operation, and the mutation operation formula of traditional differential evolution algorithm is such as
Under:
Vi(g)=Xp1(g)+F*(Xp2(g)-Xp3(g))
Wherein Xp1(g), Xp2(g), Xp3It (g) is g for three random individuals in population, and p1≠p2≠p3≠ i, Vi
It (g) is g for the variation individual that i-th of individual generates in population.
Wherein, F uses adaptive strategy, and specific rules are as follows:
F in formulal=0.1, Fu=0.9, fb, fm, fwThree individual fitnesses respectively selected in mutation process from
Result after arriving small sequence greatly；
Crossover operation formula is as follows:
WhereinIndicate that j-th of bit of i-th of individual, cr use adaptive strategy, specific rules are as follows:
Wherein crl=0.1, cru=0.6, fi, fmin, fmax,The respectively adaptive value of current individual, it is minimum suitable in population
Individual should be worth, in population maximum adaptation value individual and population in average individual adaptive value.
Wherein it is as follows to calculate function for adaptive value:
Wherein TxIndicate the side collection set of wiring tree, l (ei) indicate side collection element eiLength.
In the present embodiment, the greedy selection strategy specifically:
Wherein: f (XMi(g)) it indicates to intersect individual XMi(g) adaptive value, f (Xi(g)) current individual X is indicatedi(g) suitable
It should be worth, Xi(g+1) individual of next-generation population is indicated entry into.
It, will be using the methods and results and random initializtion of minimum spanning tree initialization in order to verify effectiveness of the invention
Methods and results compare, and as shown in table 1, the present invention achieves average 35.72% optimization rate on solving the problems, such as this.
1 minimum spanning tree strategy bring optimization rate of table
In order to verify effectiveness of the invention, by the present invention with two kinds of Steiner minimum tree constructing methods in ten groups of tests
It is compared in circuit, as shown in table 2.In view of total this optimization aim of wiring wire length, our methods relatively it is existing its
His two methods RSMT (rectangular configuration Steiner minimum tree) and OSMT (X architecture Steiner minimum tree) obtains average respectively
9.74% and 0.51% optimization rate.Wherein, effect of optimization is compared with method RSMT, can reach 9.74%
Wire length optimization rate.It can be seen that the present invention has good optimization ability on the problem of solving X architecture Steiner minimum tree.
The comparative situation that table 2 our algorithm and other two kinds of algorithms are grown in wiring bus
The foregoing is merely presently preferred embodiments of the present invention, all equivalent changes done according to scope of the present invention patent with
Modification, is all covered by the present invention.
Claims (5)
1. the high quality Steiner minimum tree constructing method under a kind of X architecture with differential evolution, which is characterized in that including following
Step:
Step S1: initializing population using Prim algorithm, obtains the population of initial solution；
Step S2: improved differential evolution algorithm and traditional differential evolution algorithm are constructed；
Step S3: judge whether algorithm iteration number reaches threshold value threshold if not up to threshold value according to improved difference
Evolution algorithm carries out mutation operation to the population of initial solution；According to traditional differential evolution algorithm to initial solution if reaching threshold value
Population carry out mutation operation；
Step S4: based on greedy selection strategy, the optimum individual in selected population is entered in the next generation of population；
Step S4: circulation step S3-S4, until population iteration terminates, obtain globally optimal solution, as final cabling scenario.
2. the high quality Steiner minimum tree constructing method under X architecture according to claim 1 with differential evolution, special
Sign is: the mutation operation formula of the improved differential evolution algorithm is as follows:
Wherein Xp1(g), Xp2(g), Xp3It (g) is g for three random individuals in population, and p1≠p2≠p3≠ i, ViIt (g) is the
G is for the variation individual that i-th of individual generates in population.
The formula of crossover operation is as follows:
Wherein Vi(g), Xi(g) difference g is for i-th of individual and i-th of variation individual, XM in populationiIt (g) is g in population
The intersection individual that i-th of individual and i-th of variation individual generate.
It is byThe difference set result of A and B is sought in expression；If expression B is empty set, two o'clock is taken to make a variation A；If B
Or not sky, in conjunction with Union-find Sets strategy, it is added to element in A as side to be selected in B, if after the side that can be added to B in A adds
B is also an illegal tree, then cabling mode when not connected point being linked to be while and initialized at random be added in B until
B is that a legal tree terminates；Indicate that A and B makees crossover operation according to probability cr.
3. the high quality Steiner minimum tree constructing method under X architecture according to claim 1 with differential evolution, special
Sign is: the mutation operation formula of traditional differential evolution algorithm is as follows:
Vi(g)=Xp1(g)+F*(Xp2(g)-Xp3(g))
Wherein Xp1(g), Xp2(g), Xp3It (g) is g for three random individuals in population, and p1≠p2≠p3≠ i, ViIt (9) is the
G is for the variation individual that i-th of individual generates in population.
Wherein, F uses adaptive strategy, and specific rules are as follows:
F in formulal=0.1, Fu=0.9, fb, fm, fwThree individual fitnesses respectively selected in mutation process from greatly to
Result after small sequence；
Crossover operation formula is as follows:
Its byIndicate that j-th of bit of i-th of individual, cr use adaptive strategy, specific rules are as follows:
Wherein crl=0.1, cru=0.6, fi, fmin, fmax,The respectively adaptive value of current individual, minimum adaptive value in population
Individual, average individual adaptive value in maximum adaptation value individual and population in population；
Wherein it is as follows to calculate function for adaptive value:
Wherein TxIndicate the side collection set of wiring tree, l (ei) indicate side collection element ei length.
4. the high quality Steiner minimum tree constructing method under X architecture according to claim 1 with differential evolution, special
Sign is: the greed selection strategy specifically:
Wherein: f (XMi(g)) it indicates to intersect individual XMi(g) adaptive value, f (Xi(g)) current individual X is indicatedi(g) adaptive value,
Xi(g+1) individual of next-generation population is indicated entry into.
5. the high quality Steiner minimum tree building side with differential evolution under the X architecture according to claim 1
Method, it is characterised in that: the threshold value threshold is set as 0.25 times that algorithm iteration number reaches maximum number of iterations.
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Cited By (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN110795907A (en) * | 2019-09-30 | 2020-02-14 | 福州大学 | X-structure Steiner minimum tree construction method considering wiring resource relaxation |
| CN111539181A (en) * | 2020-04-28 | 2020-08-14 | 福州大学 | Multi-strategy optimization X structure minimum tree construction method based on discrete differential evolution |
| CN112395822A (en) * | 2020-11-26 | 2021-02-23 | 福州大学 | Time delay driven non-Manhattan structure Steiner minimum tree construction method |
| WO2021227463A1 (en) * | 2020-05-14 | 2021-11-18 | 福州大学 | Two-step x-architecture steiner minimum tree construction method |
Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN103902775A (en) * | 2014-03-31 | 2014-07-02 | 福州大学 | Multilayer obstacle-avoiding Steiner minimal tree construction method for very large scale integration |
| CN107247844A (en) * | 2017-06-10 | 2017-10-13 | 福州大学 | The minimum tree algorithms of X architecture Steiner based on adaptive PSO and mixing switching strategy |
| US20180357584A1 (en) * | 2017-06-12 | 2018-12-13 | Hefei University Of Technology | Method and system for collaborative scheduling of production and transportation in supply chains based on improved particle swarm optimization |
-
2019
- 2019-04-16 CN CN201910306100.XA patent/CN110059405B/en active Active
Patent Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN103902775A (en) * | 2014-03-31 | 2014-07-02 | 福州大学 | Multilayer obstacle-avoiding Steiner minimal tree construction method for very large scale integration |
| CN107247844A (en) * | 2017-06-10 | 2017-10-13 | 福州大学 | The minimum tree algorithms of X architecture Steiner based on adaptive PSO and mixing switching strategy |
| US20180357584A1 (en) * | 2017-06-12 | 2018-12-13 | Hefei University Of Technology | Method and system for collaborative scheduling of production and transportation in supply chains based on improved particle swarm optimization |
Non-Patent Citations (2)
| Title |
|---|
| HAILINWU, SAIJUAN XU,ZHEN ZHUANG, GENGGENG LIU: ""X-Architecture Steiner Minimal Tree Construction Based on Discrete Differential Evolution"", 《HTTPS://DOI.ORG/10.1007/978-3-030-32456-8_47》 * |
| 刘耿耿，郭文忠，陈国龙: ""X结构下VLSI多层绕障Steiner最小树算法"", 《计算机辅助设计与图形学学报》 * |
Cited By (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN110795907A (en) * | 2019-09-30 | 2020-02-14 | 福州大学 | X-structure Steiner minimum tree construction method considering wiring resource relaxation |
| CN111539181A (en) * | 2020-04-28 | 2020-08-14 | 福州大学 | Multi-strategy optimization X structure minimum tree construction method based on discrete differential evolution |
| WO2021218157A1 (en) * | 2020-04-28 | 2021-11-04 | 福州大学 | Discrete differential evolution-based multi-strategy optimization x-architecture minimum tree construction method |
| CN111539181B (en) * | 2020-04-28 | 2022-05-13 | 福州大学 | Multi-strategy optimization X structure minimum tree construction method based on discrete differential evolution |
| WO2021227463A1 (en) * | 2020-05-14 | 2021-11-18 | 福州大学 | Two-step x-architecture steiner minimum tree construction method |
| CN112395822A (en) * | 2020-11-26 | 2021-02-23 | 福州大学 | Time delay driven non-Manhattan structure Steiner minimum tree construction method |
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