CN105096006A - Method for optimizing a routing of an intelligent ammeter distributing vehicle - Google Patents
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Abstract
The present invention relates to a method for optimizing a routing of an intelligent ammeter distributing vehicle. The optimization method mainly considers an intelligent ammeter distributing plan from a perspective of time and space, converts a problem of routing planning of the intelligent ammeter distributing vehicle into a problem of vehicle routing optimal scheduling with a distribution center, many vehicles, and a restriction of capacity and time by combining with a real situation of a provincial level measuring center, establishes a corresponding mathematical model, and solves and analyze the mathematical model by adopting an improved genetric algorithm so as to obtain a reasonable routing plan of the intelligent ammeter distribution vehicle. The method avoids many defects of a traditional intelligent meter distributing plan which is manually made, and provides a powerful support for the timely, economic, efficient and accurate intelligent ammeter distribution.
Description
Technical field
The invention belongs to electric energy meter dispensing field, especially a kind of distribution vehicle method for optimizing route of intelligent electric energy meter.
Background technology
Along with the fast development of intelligent grid, the intelligent electric energy meter as primary mental ability measuring equipment progressively replaces traditional electric energy meter.Because intelligent meter needs centralized pay, concentrated calibrating, centralized warehousing, unified dispensing, the extensive installation of intelligent meter causes the continuous expansion of electric energy meter logistics network and the sharp increase of dispensed amounts, has higher requirement also to the dispensing efficiency of provincial measurement centre simultaneously.Traditional intelligent meter distribution plan is arranged to master with artificial experience, there is subjectivity and repeatability is high, poor in timeliness, information interaction difficulty, shortcoming such as dispensing inefficiency etc., in the case, the distribution vehicle method for optimizing route designing a kind of new intelligent meter has been extremely urgent thing.
Through retrieval, find the text of publication of following analogous technical fields.
A kind of generation method of vehicle path planning, device (CN104567905A), described method comprises: initialization ant group and ant, determines vehicle to be scheduled; During for next dispensing node of Transporting Arrangement to be scheduled, consider the requirement of the requirement of hard time window, dead weight capacity and weak rock mass; Calculate the punishment cost that vehicle to be scheduled causes because violating weak rock mass, and according to the distribution cost of described punishment cost and vehicle, after calculating the total cost of limit cost and current ant, circulation generates N ant, and obtain the wiring diagram of each ant and the total cost of each ant, last according to the wiring diagram of each ant and the total cost of each ant, obtain the optimum Distribution path of vehicle.The present invention, when for Transporting Arrangement path, considers these four models of VRPMVT, VRPTW, VRPSTW and VRPPD, achieves solving the higher complicated optimum problem of practicality, thus meet the demand of multiple practical applications.
A kind of Distribution path planing method and system (CN104732289A), method comprises: handheld terminal receives the operational order of user, input initial seed point information and multiple points of destination dot information, and input path Planning Model, then is sent to server; Wherein, described path planning pattern comprises the shortest Planning Model of distance, the most laborsaving Planning Model of transport, meets subscription time Planning Model and User Defined Planning Model; Server obtains according to described initial seed point information, point of destination dot information, path planning pattern and the electronic chart that prestores the path that in the path of many whole object websites of serial connection, projecting parameter sum is minimum, and corresponding for path minimum for projecting parameter sum track is sent to handheld terminal; Handheld terminal receives the corresponding track in the minimum path of projecting parameter, and shows on handheld terminal.Present invention achieves the path planning based on single starting point and many destinations, provide more comprehensively, route planning information more accurately.
Through contrast, above patent documentation compared with present patent application, the concrete technical problems solved, and concrete technical scheme all exists relatively big difference.
Summary of the invention
The object of the invention is to overcome the deficiencies in the prior art part, a kind of distribution vehicle method for optimizing route of intelligent electric energy meter is provided.
The present invention solves its technical matters and takes following technical scheme to realize:
A distribution vehicle method for optimizing route for intelligent electric energy meter, is characterized in that: step is as follows:
S1 obtains related data, comprise: home-delivery center's distribution vehicle number, dead weight capacity, maximum operating range and driver's travel cost coefficient, distance parameter between any dispensing point and unit distance transportation cost, the intelligent meter demand of each dispensing point and receive the time restriction of intelligent meter;
S2 builds intelligent meter distribution vehicle path optimization mathematical model;
S3, based on Revised genetic algorithum, first determines individual cryptoprinciple;
S4 adopts greedy algorithm initialization population, forms preliminary vehicle delivery scheme;
S5 determines individual fitness function, and calculates individual fitness value successively;
S6 adopts saving best result algorithm to preserve the optimum individual of contemporary population, and compares with successive dynasties population optimum individual, thus ensures the optimum individual in the contemporary population of the unlikely destruction of randomness of genetic algorithm;
S7 judges whether the stopping criterion for iteration meeting genetic algorithm optimization criterion, if met, exports successive dynasties population optimum individual, and decodes, enter S11; If do not met, then proceed to step S8;
S8 carries out selection operation to population, thus selects excellent individuality from current population, makes them have larger probability to carry out next generation's procreation as parent;
S9 carries out interlace operation to population, by individual for each in population random collocation, and exchange the genetic fragment between them with certain crossover probability, thus it is individual to produce a new generation;
S10 carries out mutation operation to population, exchanges the encoded radio of wherein one or more gene position with certain mutation probability, thus improves the local search ability in genetic algorithm, proceeds to step S5 afterwards;
S11 optimum results exports, and after meeting stopping criterion for iteration, and decodes to the population optimum individual exported, thus obtains best distribution vehicle path optimization scheme.
And, in step S2, build intelligent meter distribution vehicle path optimization mathematical model and comprise: path optimization's objective function and constraint condition;
Described path optimization objective function is:
it mainly comprises two parts: one is the transport driving total cost of vehicle, and two is travel cost of vehicle drivers.Wherein: m is the distribution vehicle come into operation; x
ijkbe 0,1 variable, x
ijk=1 represents that vehicle k drives to dispensing point j from dispensing point i, otherwise x
ijk=0; d
ijrepresent the transportation range of dispensing point i to dispensing point j; c
1and c
2represent the transportation cost of distribution vehicle unit distance and the travel cost coefficient of single driver respectively;
Described path optimization constraint condition comprises:
1. number of vehicles constraint, m≤A, A represent the operational vehicle of home-delivery center;
2. the capacity of carriage constraint of distribution vehicle,
n represents that all dispensings are counted out, and Q represents the maximum load number of vehicle, q
irepresent the intelligent meter demand number of dispensing point i, y
ikbe 0,1 variable, y
ik=1 represents that the intelligent meter of dispensing point i is provided and delivered by vehicle k, otherwise y
ik=1;
3. distribution vehicle line-spacing constraint,
l represents the maximum operating range of distribution vehicle;
4. ET
j≤ t
j≤ LT
j, t
j=t
i+ xbt
i+ t
ij, i, j=1,2 ... n, t
irepresent that distribution vehicle arrives the moment of dispensing point i, xbt
irepresent the table time of unloading needed for dispensing point i, t
ijrepresent running time the road of distribution vehicle from dispensing point i to dispensing point j, ET
j, LT
jrepresent the moment the earliest that distribution vehicle arrives and moment restriction the latest respectively.
And, in described S3, the principle of improved adaptive GA-IAGA individual UVR exposure is: directly adopt numeral to encode to dispensing point, wherein i=0 represents provincial measurement centre, i=1,2 ... n represents different dispensing points respectively, and then determine individual UVR exposure according to the relative position of dispensing point in whole path, each individuality namely in genetic algorithm is exactly the access order of several separate lines.
And in described S4, greedy algorithm initialization population first builds " dispensing point adjacency matrix ": distance is pressed in 3 nearest for distance dispensing point i dispensings and sorts successively, thus composition rank, n+1 × 3 matrix, wherein elements A
ijrepresent the dispensing point numbering that distance dispensing point i jth is near; Then the coding i of stochastic generation the 1st dispensing point, and from " dispensing point an adjacency matrix " i-th row element prioritizing selection apart from this dispensing put nearest dispensing point j as the 2nd coding, if corresponding coding and the Code conflicts occurred above in " dispensing point adjacency matrix ", then another dispensing point coding do not occurred of stochastic generation; By that analogy, unduplicated sequence of natural numbers between a 1-n is generated; Consider the dead weight capacity restriction of distribution vehicle, can in each sequence of natural numbers, the intelligent meter demand that the corresponding dispensing that adds up successively is from left to right put, and symbol " ︱ " is inserted successively before the coding of dead weight capacity being just no more than separate unit distribution vehicle, natural sequence between each " ︱ " and " ︱ " represents the separate unit vehicle route from home-delivery center, and each initialization individuality represents a kind of initialization distribution project; If the population scale in genetic algorithm is nr, then the individual similar sequence of natural numbers of stochastic generation nr is individual as stated above, thus forms the initialization population on nr × n rank.
And, in described S5, fitness function is fit (i)=F (i)+DF (i)+TF (i), wherein: F (i) represents the objective function of path optimization, DF (i) represents that distribution vehicle exceedes the rejection penalty function of maximum operating range L, and TF (i) definites time-lag ET in advance for distribution time exceedes
j, LT
jrejection penalty function.
And in described S6, saving best result algorithm sorts, individual fit the highest for fitness value according to contemporary ideal adaptation angle value size
dmaxpreserve, and with the successive dynasties population optimum individual fit preserved in previous iteration computing
lmaxcompare, if fit
dmax>=fit
lmax, replace the fit in successive dynasties optimum individual with contemporary optimum individual
lmax, i.e. fit
lmax=fit
dmaxif, fit
dmax≤ fit
lmax, then show that optimum individual has suffered destruction in genetic process, need again to recover, specific practice replaces with the successive dynasties population optimum individual preserved the individuality that in contemporary population, fitness is minimum.
And in described S7, termination of iterations condition is 1000 iterative loop, or the successive dynasties population optimum individual difference of continuous several times iteration is not more than 0.01.
And in described S8, select operating principle to be that ideal adaptation angle value is larger, adaptability is stronger, it is higher that it becomes parent procreation individual probability of future generation.Concrete operating process is as follows:
1. the fitness sum of all individualities in population is calculated,
nr represents the individual amount of population;
2. the relative adaptability degrees of each individuality in population is calculated respectively,
and by individual relative fitness value by size order carry out sorting and adding up, in this, as this individuality selected probability foundation as parent in lower generation seed procedure;
3. adopt roulette wheel selection, namely adopt rand () algorithm to produce random number between one (0,1), when it is just greater than all relative adaptability degrees accumulated values before certain individuality, namely select this individuality as parent.
And in described S9, interlace operation adopts better simply order Hybrid Algorithm, and detailed process is as follows:
1. first random pair is between two carried out to the former generation's individuality in population;
2. individual former generation of each pairing, exchange mutual two individual portion gene sections according to certain principle behind random selecting 2 point of crossing; And crossover probability is 0.9.
And in described S10, mutation operation adopts exchange mutation operation, 2 different genes positions of Stochastic choice in same individuality, and exchanges the encoded radio of these 2 gene position, and mutation probability is 0.001.
Advantage of the present invention and good effect are:
The inventive method considers the optimization method of intelligent meter distribution plan from the angle emphasis of Time and place, actual in conjunction with provincial measurement centre, the distribution vehicle path planning problem of intelligent meter is converted into a home-delivery center, many vehicles, there is the optimizing and scheduling vehicle problem of capacity and time restriction, set up corresponding mathematical model, and adopt improved adaptive GA-IAGA to carry out solving analysis, draw rational intelligent meter distribution vehicle route scheme, thus avoid many drawbacks of traditional artificial formulation intelligent meter distribution plan, for in time, economical, efficiently, intelligent meter supply accurately provides strong support.
Accompanying drawing explanation
Fig. 1 is this method FB(flow block).
Embodiment
Below in conjunction with accompanying drawing, also by specific embodiment, the invention will be further described, and following examples are descriptive, are not determinate, can not limit protection scope of the present invention with this.
A distribution vehicle method for optimizing route for intelligent electric energy meter, step is as follows:
S1 obtains related data, comprise: home-delivery center's distribution vehicle number, dead weight capacity, maximum operating range and driver's travel cost coefficient, distance parameter between any dispensing point and unit distance transportation cost, the intelligent meter demand of each dispensing point and receive the time restriction etc. of intelligent meter;
S2 builds intelligent meter distribution vehicle path optimization mathematical model;
Mathematical model comprises: path optimization's objective function and constraint condition.
Described path optimization objective function is:
it mainly comprises two parts: one is the transport driving total cost of vehicle, and two is travel cost of vehicle drivers.Wherein: m is the distribution vehicle come into operation; x
ijkbe 0,1 variable, x
ijk=1 represents that vehicle k drives to dispensing point j from dispensing point i, otherwise x
ijk=0; d
ijrepresent the transportation range of dispensing point i to dispensing point j; c
1and c
2represent the transportation cost of distribution vehicle unit distance and the travel cost coefficient of single driver respectively.
Described path optimization constraint condition comprises:
1. number of vehicles constraint, m≤A, A represent the operational vehicle of home-delivery center;
2. the capacity of carriage constraint of distribution vehicle,
n represents that all dispensings are counted out, and Q represents the maximum load number of vehicle, q
irepresent the intelligent meter demand number of dispensing point i, y
ikbe 0,1 variable, y
ik=1 represents that the intelligent meter of dispensing point i is provided and delivered by vehicle k, otherwise y
ik=0;
3. distribution vehicle line-spacing constraint,
l represents the maximum operating range of distribution vehicle;
4. ET
j≤ t
j≤ LT
j, t
j=t
i+ xbt
i+ t
ij, i, j=1,2 ... n, t
irepresent that distribution vehicle arrives the moment of dispensing point i, xbt
irepresent the table time of unloading needed for dispensing point i, t
ijrepresent running time the road of distribution vehicle from dispensing point i to dispensing point j, ET
j, LT
jrepresent the moment the earliest that distribution vehicle arrives and moment restriction the latest respectively.
S3, based on Revised genetic algorithum, first determines individual cryptoprinciple;
The principle of improved adaptive GA-IAGA individual UVR exposure is: directly adopt numeral to encode to dispensing point, wherein i=0 represents provincial measurement centre, i=1,2, n represents different dispensing points respectively, then determine individual UVR exposure according to the relative position of dispensing point in whole path, each individuality namely in genetic algorithm is exactly the access order of several separate lines.
S4 adopts greedy algorithm initialization population, forms preliminary vehicle delivery scheme;
Greedy algorithm initialization population first builds " dispensing point adjacency matrix ": distance is pressed in 3 nearest for distance dispensing point i dispensings and sorts successively, thus composition rank, n+1 × 3 matrix, wherein elements A
ijrepresent the dispensing point numbering that distance dispensing point i jth is near; Then the coding i of stochastic generation the 1st dispensing point, and from " dispensing point an adjacency matrix " i-th row element prioritizing selection apart from this dispensing put nearest dispensing point j as the 2nd coding, if corresponding coding and the Code conflicts occurred above in " dispensing point adjacency matrix ", then another dispensing point coding do not occurred of stochastic generation; By that analogy, unduplicated sequence of natural numbers between a 1-n is generated; Consider the dead weight capacity restriction of distribution vehicle, can in each sequence of natural numbers, the intelligent meter demand that the corresponding dispensing that adds up successively is from left to right put, and symbol " ︱ " is inserted successively before the coding of dead weight capacity being just no more than separate unit distribution vehicle, natural sequence between each " ︱ " and " ︱ " represents the separate unit vehicle route from home-delivery center, and each initialization individuality represents a kind of initialization distribution project; If the population scale in genetic algorithm is nr, then the individual similar sequence of natural numbers of stochastic generation nr is individual as stated above, thus forms the initialization population on nr × n rank.
S5 determines individual fitness function, and calculates individual fitness value successively;
Fitness function is fit (i)=F (i)+DF (i)+TF (i), wherein: F (i) represents the objective function of path optimization, DF (i) represents that distribution vehicle exceedes the rejection penalty function of maximum operating range L, and TF (i) definites time-lag ET in advance for distribution time exceedes
j, LT
jrejection penalty function.
S6 adopts saving best result algorithm to preserve the optimum individual of contemporary population, and compares with successive dynasties population optimum individual, thus ensures the optimum individual in the contemporary population of the unlikely destruction of randomness of genetic algorithm;
Saving best result algorithm sorts, individual fit the highest for fitness value according to contemporary ideal adaptation angle value size
d.maxpreserve, and with the successive dynasties population optimum individual fit preserved in previous iteration computing
l.maxcompare, if fit
d.max>=fit
l.max, replace the fit in successive dynasties optimum individual with contemporary optimum individual
l.max, i.e. fit
l.max=fit
d.maxif, fit
d.max≤ fit
l.max, then show that optimum individual has suffered destruction in genetic process, need again to recover, specific practice replaces with the successive dynasties population optimum individual preserved the individuality that in contemporary population, fitness is minimum.
S7 judges whether the stopping criterion for iteration meeting genetic algorithm optimization criterion, if met, exports successive dynasties population optimum individual, and decodes, enter S11; If do not met, then proceed to step S8;
Described termination of iterations condition is 1000 iterative loop, or the successive dynasties population optimum individual difference of continuous several times iteration is not more than 0.01.
S8 carries out selection operation to population, thus selects excellent individuality from current population, makes them have larger probability to carry out next generation's procreation as parent;
Select operating principle to be that ideal adaptation angle value is larger, adaptability is stronger, and it is higher that it becomes parent procreation individual probability of future generation.Concrete operating process is as follows:
1. the fitness sum of all individualities in population is calculated,
nr represents the individual amount of population;
2. the relative adaptability degrees of each individuality in population is calculated respectively,
and by individual relative fitness value by size order carry out sorting and adding up, in this, as this individuality selected probability foundation as parent in lower generation seed procedure;
3. adopt roulette wheel selection, namely adopt rand () algorithm to produce random number between one (0,1), when it is just greater than all relative adaptability degrees accumulated values before certain individuality, namely select this individuality as parent.
S9 carries out interlace operation to population, by individual for each in population random collocation, and exchange the genetic fragment between them with certain crossover probability, thus it is individual to produce a new generation;
Interlace operation adopts better simply order Hybrid Algorithm, and detailed process is as follows:
1. first random pair is between two carried out to the former generation's individuality in population;
2. individual former generation of each pairing, exchange mutual two individual portion gene sections according to certain principle behind random selecting 2 point of crossing.
Crossover probability in the present embodiment is 0.9.
S10 carries out mutation operation to population, exchanges the encoded radio of wherein one or more gene position with certain mutation probability, thus improves the local search ability in genetic algorithm, proceeds to step S5 afterwards;
Mutation operation adopts exchange mutation operation, 2 different genes positions of Stochastic choice in same individuality, and exchanges the encoded radio of these 2 gene position.
Mutation probability is 0.001.
S11 optimum results exports, and after meeting stopping criterion for iteration, and decodes to the population optimum individual exported, thus obtains best distribution vehicle path optimization scheme.
Although disclose embodiments of the invention and accompanying drawing for the purpose of illustration, but it will be appreciated by those skilled in the art that: in the spirit and scope not departing from the present invention and claims, various replacement, change and amendment are all possible, therefore, scope of the present invention is not limited to the content disclosed in embodiment and accompanying drawing.
Claims (10)
1. a distribution vehicle method for optimizing route for intelligent electric energy meter, is characterized in that: step is as follows:
S1 obtains related data, comprise: home-delivery center's distribution vehicle number, dead weight capacity, maximum operating range and driver's travel cost coefficient, distance parameter between any dispensing point and unit distance transportation cost, the intelligent meter demand of each dispensing point and receive the time restriction of intelligent meter;
S2 builds intelligent meter distribution vehicle path optimization mathematical model;
S3, based on Revised genetic algorithum, first determines individual cryptoprinciple;
S4 adopts greedy algorithm initialization population, forms preliminary vehicle delivery scheme;
S5 determines individual fitness function, and calculates individual fitness value successively;
S6 adopts saving best result algorithm to preserve the optimum individual of contemporary population, and compares with successive dynasties population optimum individual, thus ensures the optimum individual in the contemporary population of the unlikely destruction of randomness of genetic algorithm;
S7 judges whether the stopping criterion for iteration meeting genetic algorithm optimization criterion, if met, exports successive dynasties population optimum individual, and decodes, enter S11; If do not met, then proceed to step S8;
S8 carries out selection operation to population, thus selects excellent individuality from current population, makes them have larger probability to carry out next generation's procreation as parent;
S9 carries out interlace operation to population, by individual for each in population random collocation, and exchange the genetic fragment between them with certain crossover probability, thus it is individual to produce a new generation;
S10 carries out mutation operation to population, exchanges the encoded radio of wherein one or more gene position with certain mutation probability, thus improves the local search ability in genetic algorithm, proceeds to step S5 afterwards;
S11 optimum results exports, and after meeting stopping criterion for iteration, and decodes to the population optimum individual exported, thus obtains best distribution vehicle path optimization scheme.
2. the distribution vehicle method for optimizing route of intelligent electric energy meter according to claim 1, is characterized in that: in step S2, builds intelligent meter distribution vehicle path optimization mathematical model and comprises: path optimization's objective function and constraint condition;
Described path optimization objective function is: min
it mainly comprises two parts: one is the transport driving total cost of vehicle, and two is travel cost of vehicle drivers.Wherein: m is the distribution vehicle come into operation; x
ijkbe 0,1 variable, x
ijk=1 represents that vehicle k drives to dispensing point j from dispensing point i, otherwise x
ijk=0; d
ijrepresent the transportation range of dispensing point i to dispensing point j; c
1and c
2represent the transportation cost of distribution vehicle unit distance and the travel cost coefficient of single driver respectively;
Described path optimization constraint condition comprises:
1. number of vehicles constraint, m≤A, A represent the operational vehicle of home-delivery center;
2. the capacity of carriage constraint of distribution vehicle,
k=1,2 ... n, n represent that all dispensings are counted out, and Q represents the maximum load number of vehicle, q
irepresent the intelligent meter demand number of dispensing point i, y
ikbe 0,1 variable, y
ik=1 represents that the intelligent meter of dispensing point i is provided and delivered by vehicle k, otherwise y
ik=0;
3. distribution vehicle line-spacing constraint,
l represents the maximum operating range of distribution vehicle;
4. ET
j≤ t
j≤ LT
j, t
j=t
i+ xbt
i+ t
ij, i, j=1,2 ... n, t
irepresent that distribution vehicle arrives the moment of dispensing point i, xbt
irepresent the table time of unloading needed for dispensing point i, t
ijrepresent running time the road of distribution vehicle from dispensing point i to dispensing point j, ET
j, LT
jrepresent the moment the earliest that distribution vehicle arrives and moment restriction the latest respectively.
3. the distribution vehicle method for optimizing route of intelligent electric energy meter according to claim 1, it is characterized in that: in described S3, the principle of improved adaptive GA-IAGA individual UVR exposure is: directly adopt numeral to encode to dispensing point, wherein i=0 represents provincial measurement centre, i=1,2 ... n represents different dispensing points respectively, then determine individual UVR exposure according to the relative position of dispensing point in whole path, each individuality namely in genetic algorithm is exactly the access order of several separate lines.
4. the distribution vehicle method for optimizing route of intelligent electric energy meter according to claim 1, it is characterized in that: in described S4, greedy algorithm initialization population first builds " dispensing point adjacency matrix ": distance is pressed in 3 nearest for distance dispensing point i dispensings and sorts successively, thus composition rank, n+1 × 3 matrix, wherein elements A
ijrepresent the dispensing point numbering that distance dispensing point i jth is near; Then the coding i of stochastic generation the 1st dispensing point, and from " dispensing point an adjacency matrix " i-th row element prioritizing selection apart from this dispensing put nearest dispensing point j as the 2nd coding, if corresponding coding and the Code conflicts occurred above in " dispensing point adjacency matrix ", then another dispensing point coding do not occurred of stochastic generation; By that analogy, unduplicated sequence of natural numbers between a 1-n is generated; Consider the dead weight capacity restriction of distribution vehicle, can in each sequence of natural numbers, the intelligent meter demand that the corresponding dispensing that adds up successively is from left to right put, and symbol " ︱ " is inserted successively before the coding of dead weight capacity being just no more than separate unit distribution vehicle, natural sequence between each " ︱ " and " ︱ " represents the separate unit vehicle route from home-delivery center, and each initialization individuality represents a kind of initialization distribution project; If the population scale in genetic algorithm is nr, then the individual similar sequence of natural numbers of stochastic generation nr is individual as stated above, thus forms the initialization population on nr × n rank.
5. the distribution vehicle method for optimizing route of intelligent electric energy meter according to claim 1, it is characterized in that: in described S5, fitness function be fit (i)=F (i)+DF (i)+TF (i) wherein: F (i) represents the objective function of path optimization, DF (i) represents that distribution vehicle exceedes the rejection penalty function of maximum operating range L, and TF (i) definites time-lag ET in advance for distribution time exceedes
j, LT
jrejection penalty function.
6. the distribution vehicle method for optimizing route of intelligent electric energy meter according to claim 1, is characterized in that: in described S6, saving best result algorithm, is to sort, individual fit the highest for fitness value according to contemporary ideal adaptation angle value size
dmaxpreserve, and with the successive dynasties population optimum individual fit preserved in previous iteration computing
lmaxcompare, if fit
dmax>=fit
lmaxthe fit in successive dynasties optimum individual is replaced with contemporary optimum individual
lmaxi.e. fit
lmax=fit
dmaxif fit
dmax≤ fit
lmaxthen show that optimum individual has suffered destruction in genetic process, need again to recover, specific practice replaces with the successive dynasties population optimum individual preserved the individuality that in contemporary population, fitness is minimum.
7. the distribution vehicle method for optimizing route of intelligent electric energy meter according to claim 1, is characterized in that: in described S7, and termination of iterations condition is 1000 iterative loop, or the successive dynasties population optimum individual difference of continuous several times iteration is not more than 0.01.
8. the distribution vehicle method for optimizing route of intelligent electric energy meter according to claim 1, is characterized in that: in described S8, and select operating principle to be that ideal adaptation angle value is larger, adaptability is stronger, and it is higher that it becomes parent procreation individual probability of future generation.Concrete operating process is as follows:
1. the fitness sum of all individualities in population is calculated,
nr represents the individual amount of population;
2. the relative adaptability degrees of each individuality in population is calculated respectively,
k=1,2 ... nr, and by individual relative fitness value by size order carry out sorting and adding up, in this, as this individuality selected probability foundation as parent in lower generation seed procedure;
3. adopt roulette wheel selection, namely adopt rand () algorithm to produce random number between one (0,1), when it is just greater than all relative adaptability degrees accumulated values before certain individuality, namely select this individuality as parent.
9. the distribution vehicle method for optimizing route of intelligent electric energy meter according to claim 1, is characterized in that: in described S9, and interlace operation adopts better simply order Hybrid Algorithm, and detailed process is as follows:
1. first random pair is between two carried out to the former generation's individuality in population;
2. individual former generation of each pairing, exchange mutual two individual portion gene sections according to certain principle behind random selecting 2 point of crossing; And crossover probability is 0.9.
10. the distribution vehicle method for optimizing route of intelligent electric energy meter according to claim 1, it is characterized in that: in described S10, mutation operation adopts exchange mutation operation, 2 different genes positions of Stochastic choice in same individuality, and exchanging the encoded radio of these 2 gene position, mutation probability is 0.001.
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