CN102094203A - Genetic algorithm-based gas station regional auxiliary anode position optimization method - Google Patents
Genetic algorithm-based gas station regional auxiliary anode position optimization method Download PDFInfo
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- CN102094203A CN102094203A CN2009102167520A CN200910216752A CN102094203A CN 102094203 A CN102094203 A CN 102094203A CN 2009102167520 A CN2009102167520 A CN 2009102167520A CN 200910216752 A CN200910216752 A CN 200910216752A CN 102094203 A CN102094203 A CN 102094203A
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Abstract
The invention discloses a genetic algorithm-based gas station regional auxiliary anode position optimization method, belongs to a position optimization technology, and mainly solves the problem that the auxiliary anode position is not ideal in the prior art. The optimization method comprises the following steps of: (1) supposing a scheme of auxiliary anode position arrangement and anode electric quantity as an individual, and coding each individual; (2) selecting an initial population; (3) designing fitness functions of the individuals; (4) calculating the fitness function of each individual, arranging the fitness functions according to the calculation results from small to big, arranging the corresponding individuals, and then selecting the individuals according to a fitness proportion method; and (5) performing intersection and variation on the selected individuals, and finally implementing optimization of the auxiliary anode position. The method is simple and feasible, and can effectively paint the position of an auxiliary anode and improve the reliability of an anode protection system.
Description
Technical field
The present invention relates to the regional supplementary anode method for optimizing position of a kind of Gas Stations, specifically, relate to the regional supplementary anode method for optimizing position of a kind of Gas Stations based on genetic algorithm.
Background technology
Supplementary anode is the important component part in the cathodic protection system, and regional galvanic protection is comparatively complicated, and protective is numerous in the district of standing; distribution range is little; for reducing the mutual shielding between protected body, make the Potential distribution of protected body even, it is extremely important that the anodic position just seems.Karamay oilfield is when carrying out regional galvanic protection test in standing, the protection potential value and the test piece protection degree that measure according to each well head draw, and supplementary anode position and quantity directly influence the protection effect of whole protecting system and the uniform distribution of protection potential.At present, more for supplementary anode situation theory research in the impressed current cathodic protection design, but mainly determine its burial place in actual applications by experience or test in place.In the station district cathodic protection system that has moved, because of the problem of bit selecting, the anode groundbed that has fails to throw people's operation so far, has caused waste.For the discrete anode groundbed, if do not come into operation, protective at a distance just can not be protected; If come into operation, overprotection just takes place in protective nearby, and consequently supplementary anode is buried in addressing underground again, seems that so neither science is uneconomical again.
By to the theoretical investigation of supplementary anode burial place, when finding that anode position is nearer apart from buried pipeline, bigger to the influence of pipe surface Potential distribution; And distance is when far away, and the protective current that needs obviously increases, and therefore, reasonably the anode layout should satisfy the Potential distribution requirement, guarantees the small electric flow density again.People such as Geng Xiaomei are utilizing analytic method to try to achieve on the cathodic protection potential distributed basis; optimization anode position and protective current mathematical model have been set up; calculation result is compared with the result who adopts empirical method; current draw has descended 8.05% when satisfying Potential distribution, confirms to optimize anode position and has remarkable economy.Since then as can be seen; it is feasible setting up galvanic protection optimization model by optimization anode position and current density; but its model is to be based upon on the analytical method solving Potential distribution basis; as previously mentioned; analytic method exists influence factor to consider characteristics such as incomplete, that error is bigger when finding the solution Potential distribution, so model has its limitation.On the other hand,, when optimizing anode position, just it is considered as the two dimensional surface problem, does not consider of the influence of anode buried depth, obviously be unfavorable for very much Model Optimization Potential distribution even adopt numerical model.
Basis idea of Genetic Algorithm is to begin from the population that the representative problem may potential disaggregation, and population then is made up of the individuality through the certain number of genes encoding.Each individuality is actually the characteristic entity of chromosome band.Therefore will realize at the beginning that individual is coding work from phenotype to genotypic mapping.Utilize genetic algorithm to separate optimization problem, the point of at first tackling in the feasible region is encoded (as adopting binary coding), some code set of random choose and are calculated the target function value that each is separated as the first-generation population of evolution starting point in feasible region then, just Ge Ti fitness.With that as the evolution of occurring in nature, utilize choice mechanism random choose individuality individual specimen before as evolutionary process from population.Choice mechanism should guarantee that the higher individuality of fitness can keep more sample, and the individuality that fitness is lower then keeps less sample, even is eliminated.In subsequent evolutionary process, genetic algorithm provides the intersection and the two kinds of operators that make a variation that the sample after selecting is carried out conversion.Conversion is then directly carried out to a certain position coding of random choose in some individuality in some position of two individual codings of crossover operator exchange random choose, mutation operator.Just produced population of future generation by selection and conversion like this.Repeat above-mentioned selection and conversion process, till termination condition is met.The optimum individual of evolutionary process in last generation is exactly the approximate optimal solution of this optimization problem again by decoding.Its basic procedure is:
Coding: with the genetic algorithm for solving problem time, what at first run into is encoded question.Separating with the form that is suitable for genetic algorithm for solving of problem encoded, be called the expression of genetic algorithm.And intersect, operation such as variation is relevant with the form of coding, therefore will consider intersection and variation problem when encoding.The simplest coded system is: binary coding, in addition, the mode of coding also has integer coding, real number volume, tree-encoding etc.
The generation of initial population: producing initial population is before finding the solution, and selects several body to form initial population in the alternative space of separating, and what produce the initial population employing usually is random approach.
Fitness evaluation: according to the principle of organic evolution " survival of the fittest ", need portray, thereby introduce fitness to the ability of each ideal adaptation environment.Fitness is unique information that genetic algorithm is used in colony's evolutionary process, and how it duplicates for character string has provided quantitative description.Fitness function comes relatively more individual fitness by calculating individual adaptive value.Fitness function is divided into the fitness function of unconfined condition and the fitness function of constraint condition is arranged.
Select: the individuality in the population will be selected before intersecting.The purpose of selecting is to obtain more excellent individuality as parent, carries out next step intersection.The foundation of selecting is individual fitness, and the individual selected possibility that fitness value is high is big, and the individual selected probability that fitness is low is little.The individuality that fitness is high may repeatedly be duplicated.And the low individuality of fitness may be once not selected yet.Select operator also to cry sometimes and duplicate operator.System of selection commonly used is the fitness scaling method, also is the roulette method, and its fundamental principle is to select according to the fitness size of individuality.
Intersect: intersection is also referred to as mating, and the portion gene that is about to the coded strings of two parent individualities exchanges, and produces new individuality.Crossover operator is the important operator in the Population Genetic Algorithm, is that population produces new individual main means.For binary coding, the concrete method of implementing to intersect has that single-point intersects, 2 intersection, multiple spot intersection, consistent intersect etc.For real coding, the method for intersection has discrete recombination, middle reorganization, linear reorganization etc.
Variation: mutation operation is at first selected body one by one at random in population, for the individuality of choosing according to certain value in certain probability randomly changing string structure, promptly to each individuality in the population, the value that changes on some or certain some locus with a certain probability is other gene.The same with organic sphere, the probability that morphs in the genetic algorithm is very low, and mutation operation provides chance for new individual generation.
End condition is judged: end condition judges to be meant when think that algorithm has found optimum solution, thereby can stop algorithm.Owing to when using genetic algorithm to solve particular problem usually, and do not know what the optimum solution of problem is, also do not know the target function value of its optimum solution, thereby need stop, and obtain optimum solution by algorithm.
Summary of the invention
The object of the present invention is to provide the regional supplementary anode method for optimizing position of a kind of Gas Stations, be used to realize the optimization of Gas Stations supplementary anode position based on genetic algorithm.
To achieve these goals, the technical solution used in the present invention is as follows:
Based on the regional supplementary anode method for optimizing position of the Gas Stations of genetic algorithm, it is characterized in that may further comprise the steps: the scheme that (1) establishes a kind of supplementary anode location arrangements and anode electric weight is body one by one, and each individuality is encoded respectively; (2) select initial population; (3) the individual fitness function of design; (4) calculate each individual fitness function respectively, according to from small to large series arrangement, corresponding individuality is also arranged according to calculation result, selects individual then according to the fitness scaling method; (5) to the individuality of the selecting processing that intersects, makes a variation, realize the optimization of supplementary anode position at last.
Crossover probability is 0.75 in described step (5) cross processing.
The variation probability was 0.08 during described step (5) variation was handled.
Genetic algorithm in the gene engineering is applied to the optimization of supplementary anode position; not only crossed over great technical field; and the method simple possible that sums up of the present invention; can optimize effectively the supplementary anode position; improve the reliability of anodic protection system greatly, have high utility value.
Embodiment
Below the invention will be further described.
Based on the regional supplementary anode method for optimizing position of the Gas Stations of genetic algorithm, it is characterized in that, may further comprise the steps:
(1) scheme of establishing a kind of supplementary anode location arrangements and anode electric weight is body one by one, and each individuality is encoded respectively;
Among the present invention one by one body promptly represent a kind of supplementary anode location arrangements and anode electric weight scheme, in the individuality each component is represented coordinate figure of anodic and anode electric weight, as (x, y, z, Q
e).
(2) select initial population;
Each coordinate components in the initial population individuality is all chosen within the limits prescribed, and genetic operator is also followed this constraint during evolution.
(3) the individual fitness function of design;
The present invention is with the fitness function of objective function as individuality, and fitness is more little like this, illustrates that protection potential distributes evenly more, and this individuality is best.
(4) calculate each individual fitness function respectively, according to from small to large series arrangement, corresponding individuality is also arranged according to calculation result, selects individual then according to the fitness scaling method;
Suppose that the number of individuals in the population is N, use P
iRepresent i individual numbering, calculate each individual fitness function F (P
i), then according to F (P
i) value by from small to large rank order, corresponding individual P
iAlso sort.Chosen process adopts the roulette system of selection to select.
(5) to the individuality of the selecting processing that intersects, makes a variation, realize the optimization of supplementary anode position at last.Crossover location is selected in each individual component place, and crossover probability gets 0.75; All with identical probability variation, the variation probability gets 0.08 to each individual component.
Claims (3)
1. based on the regional supplementary anode method for optimizing position of the Gas Stations of genetic algorithm, it is characterized in that, may further comprise the steps:
(1) scheme of establishing a kind of supplementary anode location arrangements and anode electric weight is body one by one, and each individuality is encoded respectively;
(2) select initial population;
(3) the individual fitness function of design;
(4) calculate each individual fitness function respectively, according to from small to large series arrangement, corresponding individuality is also arranged according to calculation result, selects individual then according to the fitness scaling method;
(5) to the individuality of the selecting processing that intersects, makes a variation, realize the optimization of supplementary anode position at last.
2. the regional supplementary anode method for optimizing position of the Gas Stations based on genetic algorithm according to claim 1 is characterized in that crossover probability is 0.75 in described step (5) cross processing.
3. the regional supplementary anode method for optimizing position of the Gas Stations based on genetic algorithm according to claim 1 is characterized in that the variation probability was 0.08 during described step (5) variation was handled.
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| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102768701A (en) * | 2012-07-02 | 2012-11-07 | 河海大学常州校区 | High-voltage switch cabinet insulator electric field optimization method based on quantum genetic algorithm |
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Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102768701A (en) * | 2012-07-02 | 2012-11-07 | 河海大学常州校区 | High-voltage switch cabinet insulator electric field optimization method based on quantum genetic algorithm |
| CN102768701B (en) * | 2012-07-02 | 2015-06-24 | 河海大学常州校区 | High-voltage switch cabinet insulator electric field optimization method based on quantum genetic algorithm |
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Application publication date: 20110615 |