CN114331617A - Commuting private car carpooling matching method based on artificial bee colony algorithm - Google Patents
Commuting private car carpooling matching method based on artificial bee colony algorithm Download PDFInfo
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Abstract
The invention relates to a commuting private car carpooling matching method based on an artificial bee colony algorithm, belongs to the technical field of data algorithm processing, and particularly relates to the technical field of commuting private car carpooling matching methods. The method improves the artificial bee colony optimization algorithm, redesigns the honey source and the fitness rule, takes the departure time of each commuter vehicle as each dimensionality of the honey source, takes the departure time interval as the honey source change interval, and takes the saved path length after vehicle sharing as the fitness, so that the method has stronger searching capability and development capability. According to the method, the commuting private car trip travel and the commuting private car trip travel subsequence are obtained through statistics according to the vehicle RFID electronic license plate identification data, the experimental efficiency can be improved, the complexity is reduced, the method is few in parameter, high in efficiency and good in effect, more routes can be saved compared with other methods, more private cars can be matched, and the solution is better.
Description
Technical Field
The invention belongs to the technical field of data algorithm processing, particularly relates to a commuting private car carpooling matching method based on an artificial bee colony algorithm, and belongs to the technical field of commuting private car carpooling matching methods.
Background
In recent years, with rapid development of economy, the amount of private car reserves is increasing. Especially in the morning and evening peak period, a large number of commuting private cars not only cause urban traffic jam, but also cause environmental pollution. Although a common private car has the bearing capacity of at least 5 persons, the vacant seat rate of the commuting private car is high in the morning and evening peak period, and the phenomenon that one person drives one car generally exists. The commuting private car with large quantity and high vacant seat rate causes the increase of urban road traffic flow, the waste of traffic resources, the increase of environmental pollution and the increase of travel cost.
The commuting private car carpooling trip in the morning and evening peak period is an effective way for solving the problems. The matching of the commuting private cars is a key problem of the commuting private car sharing. At present, the method for matching the carpools mainly comprises a traditional data mining method and a group intelligent optimization algorithm. The traditional data mining method is a greedy algorithm, and the vehicle which is optimally matched with each vehicle is found for each vehicle according to the matching conditions. However, the method cannot obtain a global optimal solution, and the method needs to consume a long time for searching once, and has high time complexity and low efficiency. The group intelligent optimization algorithm is also a method commonly used for solving car pooling matching, such as a genetic algorithm, a particle swarm algorithm, a hill climbing algorithm and the like, the algorithms obtain an optimal solution by initializing a population and evolving the population, and not only are parameters few and efficiency high, but also the method has the problems of insufficient searching capability and low efficiency.
Disclosure of Invention
In view of the above, the present invention aims to provide a commuting private car pooling matching method based on an artificial bee colony algorithm, wherein a honey source and a fitness rule are redesigned by improving the artificial bee colony optimization algorithm, the departure time of each commuting car is used as each dimension of the honey source, the departure time interval is used as a change interval of the honey source, and the saved path length after car pooling is used as the fitness, so that the commuting private car pooling matching method has the advantages of high searching capability and developing capability, low complexity, few parameters, high efficiency, and capability of obtaining a better solution.
In order to achieve the purpose, the invention provides the following technical scheme:
a commuting private car carpooling matching method based on an artificial bee colony algorithm comprises the following steps:
s1, counting commuting travel and departure time intervals of the commuting vehicle, counting subsequence paths of all the travel, and storing all the subsequence paths in a database;
s2, determining the input of the artificial bee colony algorithm: determining the dimensionality M of a honey source solution, the number of honey sources, namely the size of the population s, and the number of reconnaissance bees, namely the number of honey sources s, the iteration number n and the maximum attempt number maxInvalid count according to the number of commuter vehicles;
s3, initialization period: initializing the population, and initializing honey source vectors by using scout bees
Wherein s is the size of the population; since each honey source
Are all solution vectors of dimension M of the problem to be optimized, so each
All contain M variables xj(j ═ 1,2, … M), and mixing each xjInitializing; after initialization of the honey source vectors is completed, calculating each solution vector of the honey source according to the fitness calculation rule
The fitness value of the population is obtained, the optimal solution and the solution vector of the optimal solution are recorded, and then population initialization is completed;
s4, peak employment period: hiring bees to search for neighbors based on the location of food sources in their memory to find better food sources in the vicinity of the food source; when the employed bee finds a food source, the adapted value of the employed bee is evaluated, and the optimal solution, the optimal solution vector and the trial times are updated;
s5, bee observation period: non-hired bees consist of two groups: observing bees and scout bees; hiring bees to share their food source information with observing bees waiting in the hive, based on which the observing bees make a random selection;
s6, scouting bee period: non-hired bees randomly search for food sources, called scout bees; if the quality of the solution still cannot be improved after the hiring bee exceeds the maximum trial number maxInvalid count, the hiring bee becomes a scout bee, the solution owned by the hiring bee is abandoned, and the converted scout bee generates the solution by initializing a formula with a honey source;
and S7, iterating the step S3 to the step S6 according to the input iteration number n.
Further, in step S1, the commuter trajectory has the characteristics of high frequency, stability, space-time similarity, etc., and the resident with the commuter trajectory is called a commuter, and the commuter private car of the commuter in the city can be found by using the RFID electronic license plate data, specifically including the following steps:
s11, extracting commuting tracks of all RFID electronic license plate data of the private car A;
s12, sequencing the commuting track of the commuting private car A according to the ascending order of the time when the car passes through the RFID acquisition point, representing the commuting track by a sequence,
R＝<eid,r,t>，
wherein TraAThe track of the vehicle A is represented, R represents a piece of RFID electronic license plate data, eid represents the electronic license plate number of the vehicle, R represents the identification number of an RFID acquisition point, t represents the time when the vehicle is recognized,
the time when the vehicle A passes the ith RFID acquisition point is shown, wherein the track of the commuter vehicle A passing the RFID is
S13, counting the commuting track of the commuting private car A according to the track of each working day, extracting the earliest and latest time of each acquisition point passing by each day, and storing the earliest and latest time into a database, wherein the time interval data of the commuting track point of the commuting private car is represented as:
wherein CommuterACommute trajectory time interval data representing a commute private car a,
and
representing the earliest time and the latest time when the private commuter A passes through the nth commuting track point;
and S14, finally, performing the steps on all the commuting private cars, and storing the commuting track point time interval data of all the commuting private cars into a database after counting.
Further, in step S2, a commuter itinerary sublist is established, including:
s21, based on statistics of commuting track point time interval data of all commuting vehicles acquired in the step S1, selecting a commuting private vehicle, recording its eid, origin and destination, putting the three pieces of information into original RFID electronic license plate data for searching, finding RFID points passing between the origin and the destination of the vehicle, and recording the RFID points, wherein every two points are a subsequence and are stored in a database, and the subsequence contained in the starting point and the ending point of each vehicle can be expressed as:
si＝<eid,origin,destination,subsequence1…subsequencen>
wherein s isiIndicating ith commute sub-sequence data, eid for the vehicleElectronic license plate identification number, origin represents the commuting departure place of the commuting vehicle, destination represents the commuting destination of the commuting vehicle, and subsequencenThe nth subsequence from the starting point to the end point of the vehicle is represented;
and S21, executing the steps on all the private cars on the commuting schedule, and storing the sub-sequences obtained by all the private cars on the commuting schedule into the sub-sequence of the commuting schedule.
Further, in step S3, the method specifically includes the following steps:
s31, inputting parameters required by an artificial bee colony algorithm and a population size S, determining the dimensionality M of a bee source solution according to the number of commuting vehicles, wherein the number of the bee sources is the population size S, the number of scout bees is the number of the bee sources S, the iteration number n and the maximum trial number maxInvalid count;
s32, initializing the honey source vector by the scout bees with the same number as the number of the honey sources
Wherein s is the size of the population; since each honey source
Are all solution vectors of dimension M of the problem to be optimized, so each
All contain M variables xj(j ═ 1,2, … M), and mixing each xjInitialization is performed according to the following formula:
xj＝lj+rand(0,1)*(uj-lj)
wherein ljAnd ujIs the minimum and maximum value of the commuting departure time interval of the jth vehicle, and rand (0,1) is a random number from 0 to 1;
s33, after initialization of the honey source vectors is completed, calculating each solution vector of the honey source according to the following fitness calculation rule
And record the fitness valueThe optimal solution and the solution vector of the optimal solution can complete population initialization:
fitness＝∑m,n∈Mfitnessm,n
fitnessm,n＝carpoolm,n-tripm-tripn
wherein:
Xm≤Xn
|Xm-Xn|≤1800
fitnessm,n≤0
in the formula, fitnessm,nCarpool for the m-th and n-th car carpools and m is the loss of carpool when the driver is drivingm,nThe carpooling mileage when the mth vehicle and the nth vehicle are carpooling vehicles and the mth vehicle is a driver, tripmIs the commuting mileage of the mth commuting vehicle, Om,DmThe start point and the end point of the commuting journey of the mth commuting vehicle,
is point OmTo point DnMileage of, xmIs the departure time of the m-th vehicle, TmThe track of the m-th vehicle,
for vehicle m to pass through point OnThe earliest time of the time interval of (a), similarly,
is the m warp of the vehiclePassing point OnThe latest time of the time interval of (a).
Further, in step S4, the method specifically includes:
s41, the hiring bee searches neighbors according to the positions of the food sources in the memory of the hiring bee, finds better honey sources near the food sources, and determines the neighbor honey sources by adopting the following formula:
wherein
Is a newly generated neighbor honey source, g and k are random values, phitIs the interval [0,1]A random value of (a);
s42, after finding the newly generated honey source, calculating the fitness value of the new honey source according to a fitness formula, if the fitness value of the new honey source is better than that of the original honey source, replacing the original honey source with the new honey source, adding 1 to the trial frequency of the honey source, otherwise, keeping the trial frequency of the honey source unchanged, comparing the fitness value of the new honey source with the fitness value of the optimal honey source, if the fitness value of the new honey source is better than that of the optimal honey source, updating the optimal fitness value and the optimal honey source, otherwise, keeping the optimal fitness value and the optimal honey source unchanged.
Further, in step S5, the method specifically includes:
and S51, substituting the optimal solution into the following formula to obtain a correction solution for each honey source:
fitness′t＝(0.9*fitnesst/bestfitness+0.1)；
s52, generating random number rand belonged to [0,1 ∈]If fitness'tAnd if the result is more than or equal to rand, repeating the step S4 on the honey source to generate a new neighbor honey source, then calculating the fitness value of the neighbor honey source, updating the honey source and the trial times, and updating the optimal fitness value and the optimal honey source.
Further, in step S6, the method specifically includes:
s61, traversing all honey sources;
s62, selecting honey sources with the trial times smaller than the maximum trial times maxInvalidcount, and initializing a corresponding new honey source for each selected honey source;
s63, calculating the fitness value of each new honey source corresponding to the selected honey source;
and S64, if the fitness value of the new honey source is larger than that of the old honey source, replacing the old honey source with the new honey source, otherwise, keeping the fitness value unchanged.
The invention has the beneficial effects that:
1) according to the vehicle RFID electronic license plate identification data, the commuting private car trip travel and the commuting private car trip travel subsequence are obtained through statistics, the experimental efficiency can be improved, and the complexity is reduced.
2) The method has the advantages that the traditional artificial bee colony algorithm is improved, a new fitness calculation method is designed, and a vehicle sharing matching method based on the artificial bee colony algorithm is provided.
Additional advantages, objects, and features of the invention will be set forth in part in the description which follows and in part will become apparent to those having ordinary skill in the art upon examination of the following or may be learned from practice of the invention. The objectives and other advantages of the invention may be realized and attained by the means of the instrumentalities and combinations particularly pointed out hereinafter.
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For the purposes of promoting a better understanding of the objects, aspects and advantages of the invention, reference will now be made to the following detailed description taken in conjunction with the accompanying drawings in which:
FIG. 1 is a general flow diagram of the process of the present invention;
FIG. 2 is a schematic illustration of a carpool;
fig. 3 is an algorithm flow chart.
Detailed Description
The technical scheme of the invention is explained in detail in the following with the accompanying drawings.
Fig. 1 is a general flow chart of the method of the present invention, and as shown in the figure, the method provided by the present invention includes the following steps:
s1, counting commuting travel and departure time intervals of the commuting vehicle, counting subsequence paths of all the travel, and storing all the subsequence paths in a database;
s2, determining the input of the artificial bee colony algorithm: determining the dimensionality M of a honey source solution, the number of honey sources, namely the size of the population s, and the number of reconnaissance bees, namely the number of honey sources s, the iteration number n and the maximum attempt number maxInvalid count according to the number of commuter vehicles;
s3, initialization period: initializing the population, and initializing honey source vectors by using scout bees
Wherein s is the size of the population; since each honey source
Are all solution vectors of dimension M of the problem to be optimized, so each
All contain M variables xj(j ═ 1,2, … M), and mixing each xjInitializing; after initialization of the honey source vectors is completed, calculating each solution vector of the honey source according to the fitness calculation rule
The fitness value of the population is obtained, the optimal solution and the solution vector of the optimal solution are recorded, and then population initialization is completed;
s4, peak employment period: hiring bees to search for neighbors based on the location of food sources in their memory to find better food sources in the vicinity of the food source; when the employed bee finds a food source, the adapted value of the employed bee is evaluated, and the optimal solution, the optimal solution vector and the trial times are updated;
s5, bee observation period: non-hired bees consist of two groups: observing bees and scout bees; hiring bees to share their food source information with observing bees waiting in the hive, based on which the observing bees make a random selection;
s6, scouting bee period: non-hired bees randomly search for food sources, called scout bees; if the quality of the solution still cannot be improved after the hiring bee exceeds the maximum trial number maxInvalid count, the hiring bee becomes a scout bee, the solution owned by the hiring bee is abandoned, and the converted scout bee generates the solution by initializing a formula with a honey source;
and S7, iterating the step S3 to the step S6 according to the input iteration number n.
Fig. 2 is a car sharing schematic diagram, and fig. 3 is an algorithm flow chart. The method specifically comprises the following steps:
in step S1, the commuter trajectory has the characteristics of high frequency, stability, space-time similarity, etc., and the resident with the commuter trajectory is called a commuter, and the commuter private car of the commuter in the city can be found by using the RFID electronic license plate data, which specifically includes the following steps:
s11, extracting commuting tracks of all RFID electronic license plate data of the private car A;
s12, sequencing the commuting track of the commuting private car A according to the ascending order of the time when the car passes through the RFID acquisition point, representing the commuting track by a sequence,
R＝<eid,r,t>，
wherein TraAThe track of the vehicle A is represented, R represents a piece of RFID electronic license plate data, eid represents the electronic license plate number of the vehicle, R represents the identification number of an RFID acquisition point, t represents the time when the vehicle is recognized,
the time when the vehicle A passes the ith RFID acquisition point is shown, wherein the track of the commuter vehicle A passing the RFID is
S13, counting the commuting track of the commuting private car A according to the track of each working day, extracting the earliest and latest time of each acquisition point passing by each day, and storing the earliest and latest time into a database, wherein the time interval data of the commuting track point of the commuting private car is represented as:
wherein CommuterACommute trajectory time interval data representing a commute private car a,
and
representing the earliest time and the latest time when the private commuter A passes through the nth commuting track point;
and S14, finally, performing the steps on all the commuting private cars, and storing the commuting track point time interval data of all the commuting private cars into a database after counting.
In step S2, a commuter itinerary sublist is created, including:
s21, based on statistics of commuting track point time interval data of all commuting vehicles acquired in the step S1, selecting a commuting private vehicle, recording its eid, origin and destination, putting the three pieces of information into original RFID electronic license plate data for searching, finding RFID points passing between the origin and the destination of the vehicle, and recording the RFID points, wherein every two points are a subsequence and are stored in a database, and the subsequence contained in the starting point and the ending point of each vehicle can be expressed as:
si＝<eid,origin,destination,subsequence1…subsequencen>
wherein s isiRepresenting the ith commuting journey sub-sequence data, eid representing the electronic license plate identification number of the vehicle, origin representing the commuting departure place of the commuting vehicle, destination representing the commuting destination of the vehicle, and subsequencenThe nth subsequence from the start point to the end point of the vehicle；
And S21, executing the steps on all the private cars on the commuting schedule, and storing the sub-sequences obtained by all the private cars on the commuting schedule into the sub-sequence of the commuting schedule.
In step S3, the method specifically includes the following steps:
s31, inputting parameters required by an artificial bee colony algorithm and a population size S, determining the dimensionality M of a bee source solution according to the number of commuting vehicles, wherein the number of the bee sources is the population size S, the number of scout bees is the number of the bee sources S, the iteration number n and the maximum trial number maxInvalid count;
s32, initializing the honey source vector by the scout bees with the same number as the number of the honey sources
Wherein s is the size of the population; since each honey source
Are all solution vectors of dimension M of the problem to be optimized, so each
All contain M variables xj(j ═ 1,2, … M), and mixing each xjInitialization is performed according to the following formula:
xj＝lj+rand(0,1)*(uj-lj)
wherein ljAnd ujIs the minimum and maximum value of the commuting departure time interval of the jth vehicle, and rand (0,1) is a random number from 0 to 1;
s33, after initialization of the honey source vectors is completed, calculating each solution vector of the honey source according to the following fitness calculation rule
And recording the optimal solution and the solution vector of the optimal solution, thereby completing population initialization:
fitness＝∑m,n∈Mfitnessm,n
fitnessm,n＝carpoolm,n-tripm-tripn
wherein:
Xm≤Xn
|Xm-Xn|≤1800
fitnessm,n≤0
in the formula, fitnessm,nCarpool for the m-th and n-th car carpools and m is the loss of carpool when the driver is drivingm,nThe carpooling mileage when the mth vehicle and the nth vehicle are carpooling vehicles and the mth vehicle is a driver, tripmIs the commuting mileage of the mth commuting vehicle, Om,DmThe start point and the end point of the commuting journey of the mth commuting vehicle,
is point OmTo point DnMileage of, xmIs the departure time of the m-th vehicle, TmThe track of the m-th vehicle,
for vehicle m to pass through point OnThe earliest time of the time interval of (a), similarly,
for vehicle m to pass through point OnThe latest time of the time interval of (a).
In step S4, the method specifically includes:
s41, the hiring bee searches neighbors according to the positions of the food sources in the memory of the hiring bee, finds better honey sources near the food sources, and determines the neighbor honey sources by adopting the following formula:
wherein
Is a newly generated neighbor honey source, g and k are random values, phitIs the interval [0,1]A random value of (a);
s42, after finding the newly generated honey source, calculating the fitness value of the new honey source according to a fitness formula, if the fitness value of the new honey source is better than that of the original honey source, replacing the original honey source with the new honey source, adding 1 to the trial frequency of the honey source, otherwise, keeping the trial frequency of the honey source unchanged, comparing the fitness value of the new honey source with the fitness value of the optimal honey source, if the fitness value of the new honey source is better than that of the optimal honey source, updating the optimal fitness value and the optimal honey source, otherwise, keeping the optimal fitness value and the optimal honey source unchanged.
In step S5, the method specifically includes:
and S51, substituting the optimal solution into the following formula to obtain a correction solution for each honey source:
fitness′t＝(0.9*fitnesst/bestfitness+0.1)；
s52, generating random number rand belonged to [0,1 ∈]If fitness'tAnd if the result is more than or equal to rand, repeating the step S4 on the honey source to generate a new neighbor honey source, then calculating the fitness value of the neighbor honey source, updating the honey source and the trial times, and updating the optimal fitness value and the optimal honey source.
In step S6, the method specifically includes:
s61, traversing all honey sources;
s62, selecting honey sources with the trial times smaller than the maximum trial times maxInvalidcount, and initializing a corresponding new honey source for each selected honey source;
s63, calculating the fitness value of each new honey source corresponding to the selected honey source;
and S64, if the fitness value of the new honey source is larger than that of the old honey source, replacing the old honey source with the new honey source, otherwise, keeping the fitness value unchanged.
In summary, the commuting private car carpooling matching method based on the artificial bee colony algorithm can select a reasonable population size s, the maximum trial times maxInvalid count and the iteration times n as the input part of the algorithm, determine the constraint condition of carpooling matching and the calculation method of the fitness, and finally obtain the optimal carpooling matching scheme.
Finally, the above embodiments are only used for illustrating the technical solutions of the present invention and not for limiting, although the present invention has been described in detail with reference to the preferred embodiments, it should be understood by those skilled in the art that modifications can be made to the technical solutions of the present invention without departing from the spirit and scope of the technical solutions, and all that should be covered by the claims of the present invention.
Claims (7)
1. A commuting private car carpooling matching method based on an artificial bee colony algorithm is characterized by comprising the following steps: the method comprises the following steps:
s1, counting commuting travel and departure time intervals of the commuting vehicle, counting subsequence paths of all the travel, and storing all the subsequence paths in a database;
s2, determining the input of the artificial bee colony algorithm: determining the dimensionality M of a honey source solution, the number of honey sources, namely the size of the population s, and the number of reconnaissance bees, namely the number of honey sources s, the iteration number n and the maximum attempt number maxInvalid count according to the number of commuter vehicles;
s3, initialization period: initializing the population, and initializing honey source vectors by using scout bees
Wherein s is the size of the population; since each honey source
Are all solution directions with dimension of M of a problem to be optimizedAmount of, thus each
All contain M variables xj(j ═ 1,2, … M), and mixing each xjInitializing; after initialization of the honey source vectors is completed, calculating each solution vector of the honey source according to the fitness calculation rule
The fitness value of the population is obtained, the optimal solution and the solution vector of the optimal solution are recorded, and then population initialization is completed;
s4, peak employment period: hiring bees to search for neighbors based on the location of food sources in their memory to find better food sources in the vicinity of the food source; when the employed bee finds a food source, the adapted value of the employed bee is evaluated, and the optimal solution, the optimal solution vector and the trial times are updated;
s5, bee observation period: non-hired bees consist of two groups: observing bees and scout bees; hiring bees to share their food source information with observing bees waiting in the hive, based on which the observing bees make a random selection;
s6, scouting bee period: non-hired bees randomly search for food sources, called scout bees; if the quality of the solution still cannot be improved after the hiring bee exceeds the maximum trial number maxInvalid count, the hiring bee becomes a scout bee, the solution owned by the hiring bee is abandoned, and the converted scout bee generates the solution by initializing a formula with a honey source;
and S7, iterating the step S3 to the step S6 according to the input iteration number n.
2. The commuting private car carpooling matching method based on the artificial bee colony algorithm, according to claim 1, is characterized in that: in step S1, the method specifically includes the following steps:
s11, extracting commuting tracks of all RFID electronic license plate data of the private car A;
s12, the commuting track of the commuting private car A is according to the time when the car passes through the RFID acquisition pointThe sorting is performed in ascending order, represented by a sequence,
R＝<eid,r,t>，
wherein TraAThe track of the vehicle A is represented, R represents a piece of RFID electronic license plate data, eid represents the electronic license plate number of the vehicle, R represents the identification number of an RFID acquisition point, t represents the time when the vehicle is recognized,
the time when the vehicle A passes the ith RFID acquisition point is shown, wherein the track of the commuter vehicle A passing the RFID is
S13, counting the commuting track of the commuting private car A according to the track of each working day, extracting the earliest and latest time of each acquisition point passing by each day, and storing the earliest and latest time into a database, wherein the time interval data of the commuting track point of the commuting private car is represented as:
wherein CommuterACommute trajectory time interval data representing a commute private car a,
and
representing the earliest time and the latest time when the private commuter A passes through the nth commuting track point;
and S14, finally, performing the steps on all the commuting private cars, and storing the commuting track point time interval data of all the commuting private cars into a database after counting.
3. The commuting private car carpooling matching method based on the artificial bee colony algorithm, as claimed in claim 2, wherein: in step S2, a commuter itinerary sublist is created, including:
s21, based on statistics of commuting track point time interval data of all commuting vehicles acquired in the step S1, selecting a commuting private vehicle, recording its eid, origin and destination, putting the three pieces of information into original RFID electronic license plate data for searching, finding RFID points passing between the origin and the destination of the vehicle, and recording the RFID points, wherein every two points are a subsequence and are stored in a database, and the subsequence contained in the starting point and the ending point of each vehicle can be expressed as:
si＝<eid,origin,destination,subsequence1…subsequencen>
wherein s isiRepresenting the ith commuting journey sub-sequence data, eid representing the electronic license plate identification number of the vehicle, origin representing the commuting departure place of the commuting vehicle, destination representing the commuting destination of the vehicle, and subsequencenThe nth subsequence from the starting point to the end point of the vehicle is represented;
and S21, executing the steps on all the private cars on the commuting schedule, and storing the sub-sequences obtained by all the private cars on the commuting schedule into the sub-sequence of the commuting schedule.
4. The commuting private car carpooling matching method based on the artificial bee colony algorithm, according to claim 3, is characterized in that: in step S3, the method specifically includes the following steps:
s31, inputting parameters required by an artificial bee colony algorithm and a population size S, determining the dimensionality M of a bee source solution according to the number of commuting vehicles, wherein the number of the bee sources is the population size S, the number of scout bees is the number of the bee sources S, the iteration number n and the maximum trial number maxInvalid count;
s32, initializing the honey source vector by the scout bees with the same number as the number of the honey sources
Wherein s is the size of the population; since each honey source
Are all solution vectors of dimension M of the problem to be optimized, so each
All contain M variables xj(j ═ 1,2, … M), and mixing each xjInitialization is performed according to the following formula:
xj＝lj+rand(0,1)*(uj-lj)
wherein ljAnd ujIs the minimum and maximum value of the commuting departure time interval of the jth vehicle, and rand (0,1) is a random number from 0 to 1;
s33, after initialization of the honey source vectors is completed, calculating each solution vector of the honey source according to the following fitness calculation rule
And recording the optimal solution and the solution vector of the optimal solution, thereby completing population initialization:
fitness＝∑m,n∈Mfitnessm,n
fitnessm,n＝carpoolm,n-tripm-tripn
wherein:
Xm≤Xn
|Xm-Xn|≤1800
fitnessm,n≤0
in the formula, fitnessm,nCarpool for the m-th and n-th car carpools and m is the loss of carpool when the driver is drivingm,nThe carpooling mileage when the mth vehicle and the nth vehicle are carpooling vehicles and the mth vehicle is a driver, tripmIs the commuting mileage of the mth commuting vehicle, Om,DmThe start point and the end point of the commuting journey of the mth commuting vehicle,
is point OmTo point DnMileage of, xmIs the departure time of the m-th vehicle, TmThe track of the m-th vehicle,
for vehicle m to pass through point OnThe earliest time of the time interval of (a), similarly,
for vehicle m to pass through point OnThe latest time of the time interval of (a).
5. The commuting private car carpooling matching method based on the artificial bee colony algorithm, according to claim 4, is characterized in that: in step S4, the method specifically includes:
s41, the hiring bee searches neighbors according to the positions of the food sources in the memory of the hiring bee, finds better honey sources near the food sources, and determines the neighbor honey sources by adopting the following formula:
wherein
Is a newly generated neighbor honey source, g and k are random values, phitIs the interval [0,1]A random value of (a);
s42, after finding the newly generated honey source, calculating the fitness value of the new honey source according to a fitness formula, if the fitness value of the new honey source is better than that of the original honey source, replacing the original honey source with the new honey source, adding 1 to the trial frequency of the honey source, otherwise, keeping the trial frequency of the honey source unchanged, comparing the fitness value of the new honey source with the fitness value of the optimal honey source, if the fitness value of the new honey source is better than that of the optimal honey source, updating the optimal fitness value and the optimal honey source, otherwise, keeping the optimal fitness value and the optimal honey source unchanged.
6. The commuting private car carpooling matching method based on the artificial bee colony algorithm, according to claim 5, is characterized in that: in step S5, the method specifically includes:
and S51, substituting the optimal solution into the following formula to obtain a correction solution for each honey source:
fitness′t＝(0.9*fitnesst/bestfitness+0.1)；
s52, generating random number rand belonged to [0,1 ∈]If fitness'tAnd if the result is more than or equal to rand, repeating the step S4 on the honey source to generate a new neighbor honey source, then calculating the fitness value of the neighbor honey source, updating the honey source and the trial times, and updating the optimal fitness value and the optimal honey source.
7. The commuting private car carpooling matching method based on the artificial bee colony algorithm as claimed in claim 6, wherein: in step S6, the method specifically includes:
s61, traversing all honey sources;
s62, selecting honey sources with the trial times smaller than the maximum trial times maxInvalidcount, and initializing a corresponding new honey source for each selected honey source;
s63, calculating the fitness value of each new honey source corresponding to the selected honey source;
and S64, if the fitness value of the new honey source is larger than that of the old honey source, replacing the old honey source with the new honey source, otherwise, keeping the fitness value unchanged.
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