CN111985705A - Multipoint position shortest path calculation method - Google Patents
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Abstract
The invention provides a method for calculating shortest path of multipoint positions, in the calculating method, because a discrete ABC algorithm as a high-order heuristic algorithm and neighborhood search as a low-level heuristic algorithm are combined in a hyper-heuristic algorithm, a new honey source in the discrete ABC algorithm can call a low-heuristic algorithm operation according to a call list in a neighborhood so as to realize automatic updating of the new honey source, and therefore, a shortest path sequence of multipoint positions can be found more quickly. The method for calculating the shortest path of the multipoint positions can obtain a better shortest path sequence. In practical application, the method can carry out the most reasonable arrangement on the travel of the travelers according to the shortest path sequence, can also design the most efficient logistics route, can also establish a better airplane flight route for an airline company, and can solve the problem of a series of shortest paths of multipoint positions.
Description
Technical Field
The invention relates to a multipoint position shortest path calculation method.
Background
The multipoint position shortest path problem belongs to a nondeterministic polynomial problem (NP problem for short), and a traveler problem (TSP problem for short) is typical of the NP problem and has the characteristics of difficult accurate solution and easy result verification. The TSP problem describes the following scenario: a traveler wants to visit several cities and then return to his place of departure, and how to plan his route given the travel time required between the various cities so that he can make exactly one visit to each city with the minimum total time.
At present, algorithms for solving the TSP problem include a population-based random optimization technology algorithm (PSO for short), an ant colony algorithm (ACO for short), a Firefly Algorithm (FA) Bat Algorithm (BA), an artificial bee colony Algorithm (ABC), and some hybrid algorithms among population intelligent algorithms. However, the solution result of the above algorithm in the reference model of the TSP problem example is greatly deviated from the correct solution, and the TSP problem cannot be solved well.
In addition, Lin-Kernighan algorithm (LK algorithm for short) is also an important research method for solving the problem of travelers. The LK algorithm is quite effective in solving combinatorial problems, but it does not allow for non-sequential swapping, which may result in a reduction of the best solution search for multiple TSP instances, failing to solve a better approximate solution.
Therefore, no effective algorithm can solve an approximate solution of the TSP problem well, and the problem of the shortest path of the multipoint positions affecting work and life, such as logistics route planning, airline flight route planning, course sequencing in a course schedule, and the like, which is similar to the TSP problem in practical application, cannot be solved effectively.
Disclosure of Invention
In order to solve the above problems, the present invention provides a calculation method capable of planning multipoint positions to obtain a shortest path, and the present invention adopts the following technical scheme:
the invention provides a shortest path of multipoint positionsThe calculation method is used for solving the shortest path sequence of all positions which pass through and only pass through once, and is characterized by comprising the following steps: step S1, setting a random sequence formed by randomly arranging a plurality of preset positions as a honey source; step S2, randomly creating Popsize/2 feasible solutions according to the formula (1) to initialize the honey source to obtain the initial honey source aiFor each initial honey source aiAllocating one employed bee:
ai＝[1,randperm(ncitys-1)+1,1] (1)
where i ∈ {1, 2., Popsize/2}, Popsize/2 is the number of employed bees, ncitysRepresenting the number of cities; step S3, the hiring bee selects one low heuristic operation from a plurality of low heuristic operations through a predetermined algorithm so as to update the initial honey source to obtain a new honey source ni：
ni＝llhx(ai) (2)
In the formula, llhxIs the xth low heuristic operation; step S4, the employed bee compares the fitness value fit according to equation (3)iAnd using greedy method to obtain the initial honey source aiAnd new honey source niPerforming a selection operation:
in the formula (f)iIs the function value; step S5, the hiring bee recruits the following bee by the roulette method, the following bee selects the honey source corresponding to the hiring bee, the probability P that the honey source corresponding to the hiring bee is selectediAs shown in equation (4):
(ii) a Step S6, when the employed bee does not find the optimal honey source within the preset iteration time, the employed bee is forced to be converted into a scout bee and the honey source corresponding to the employed bee is abandoned; step S7, the scout bees continue to search according to a preset search method to obtain an optimal honey source; and step S8, repeating the steps S3 to S7 until reaching the preset maximum cycle number, obtaining the only optimal honey source and using the only optimal honey source as the shortest path sequence.
The method for calculating the shortest path between the multiple points according to the present invention may further have the following technical features, wherein step S3 includes the following sub-steps: step S3-1, creating a null selection list; step S3-2, designing a plurality of low heuristic algorithm operations of different types in the empty selection table to obtain a neighborhood operation table; step S3-3, recording the calling scores corresponding to a plurality of calls of the low heuristic algorithm in a neighborhood operation table and forming a call table, wherein the calling scores are obtained by calculation according to a formula (5):
in the formula (f)_currentIs the current initial honey source, f_newIs a new honey source, w is the current initial honey source f_currentWith new honey source f_newThe difference in fitness between, α and β are adaptation values, n is the nth call of the call table, tcurrentRepresenting the number of current iterations, ttotalRepresenting a predetermined total number of iterations, theta being a cost factor, Wi,jDenotes the call fraction, W, of the ith row and jth column cells in the call tablei,iRepresenting the calling fraction of the ith row and ith column cells in the calling table; step S3-4, selecting a low heuristic algorithm operation based on the call list to obtain a new honey source ni。
The method for calculating the shortest path between multipoint locations provided by the invention can also have the technical characteristics that the low heuristic algorithm operation is any one or a combination of a plurality of RRS operation, RI operation, RIS operation, RS operation, RSS operation and SS operation.
The method for calculating the shortest path between the multipoint positions provided by the invention can also have the technical characteristics that the calling score is any more of an incentive calling score, a penalty calling score and an unmodified calling score.
The method for calculating shortest path between multipoint locations provided by the present invention may further have the technical features as described above, wherein step S8 may further include the following sub-steps: step S8-1: repeating the steps S3 to S7 until reaching the preset maximum cycle number to obtain the only optimal honey source; step S8-2: and optimizing the unique optimal honey source by a preset local search method to obtain the shortest path sequence.
The method for calculating the shortest path between the multipoint positions provided by the invention also has the technical characteristics that the local search method is an improved LK algorithm, and the method specifically comprises the following steps: step T1, creating two closed circles from the optimal honey source through a 2-opt method; step T2, judging whether the total length of the two closed circles is less than the total length of the optimal honey source, if so, entering step T3, and if not, entering step T4; step T3, connecting the two closed circles by a 2-opt method to form a complete stroke, and taking the complete stroke as an optimal honey source; step T4, creating two closed circles from the optimal honey source through a 3-opt method; and step T5, connecting the two closed circles by adopting a 3-opt method to form a complete stroke, and taking the complete stroke as an optimal honey source.
Action and Effect of the invention
According to the multipoint position shortest path calculation method, the discrete ABC algorithm serving as a high-order heuristic algorithm and the neighborhood search serving as a low-level heuristic algorithm are combined in the hyper-heuristic algorithm, and a new honey source in the discrete ABC algorithm can call a low-heuristic algorithm operation according to a call list in a neighborhood so as to realize automatic update of the new honey source, so that a shortest path sequence of multipoint positions can be found more quickly. The method for calculating the shortest path of the multipoint positions can obtain a better shortest path sequence. In practical application, the method can carry out the most reasonable arrangement on the travel of the travelers according to the shortest path sequence, can also design the most efficient logistics route, can also make a better airplane flight route for an airline company, and can solve the problem of the shortest paths of a series of multipoint positions.
Drawings
Fig. 1 is a flowchart of a multipoint position shortest path calculation method according to an embodiment of the present invention;
fig. 2 is a schematic diagram of a flow of a method for calculating a shortest path between multipoint locations according to an embodiment of the present invention;
FIG. 3 is an exemplary diagram of a neighborhood operations table according to an embodiment of the present invention;
FIG. 4 is a diagram illustrating a local search method according to an embodiment of the present invention; and
fig. 5 is a diagram illustrating the effects of 30 exemplary traveler questions according to an embodiment of the present invention.
Detailed Description
In order to make the technical means, the inventive features, the objectives and the functions realized by the present invention easy to understand, the following will specifically describe a method for calculating a shortest path between multiple points according to the present invention with reference to the following embodiments and the accompanying drawings.
< example >
Fig. 1 is a flowchart of a multipoint position shortest path calculation method according to an embodiment of the present invention; fig. 2 is a schematic diagram of a flow of a method for calculating a shortest path between multipoint locations according to an embodiment of the present invention.
As shown in fig. 1 and fig. 2, a multipoint position shortest path calculation method includes the following steps:
in step S1, a random sequence consisting of a plurality of predetermined positions is set as a honey source.
In this embodiment, taking the problem of the traveling salesman as an example, the route passing through a plurality of cities (i.e., the predetermined positions) only once is randomly planned to obtain a complete random route, the complete random route is presented in a random ordering form of each city, i.e., a random sequence, and the random sequence is set as a honey source.
As shown in fig. 2, the discrete ABC algorithm is selected as the high-order heuristic algorithm in the present invention.
Step S2, randomly creating Popsize/2 feasible solutions to initialize the honey source to obtain the initial honey source a according to the formula (1)iFor each initial honey source aiAllocating one employed bee:
ai＝[1,randperm(ncitys-1)+1,1] (1)
where i ∈ {1, 2., Popsize/2}, Popsize/2 is the number of employed bees, ncitysRepresenting the number of cities.
Wherein randderm (n)citys) Is a function (1, n) for generating a vector with random and non-repeating positive integerscitys) The range of (1).
In this example, Popsize refers to the size of the bee population, and Popsize/2 of the bee population is employed.
Step S3, the hiring bee selects one low heuristic operation from a plurality of low heuristic operations through a preset algorithm so as to update the high-order heuristic initial honey source to obtain a new honey source ni：
ni＝llhx(ai) (2)
In the formula, llhxIs the xth low heuristic operation (LLH for short).
As shown in fig. 2, a high-order heuristic algorithm, i.e., a discrete ABC algorithm, selects a low heuristic algorithm operation by the Greedy method.
Wherein, step S3 includes the following substeps:
step S3-1, an empty selection table is created.
FIG. 3 is a diagram illustrating a neighborhood operations table according to an embodiment of the present invention.
Step S3-2, a plurality of low heuristic algorithm operations of different types are designed in the empty selection table to obtain a neighborhood operation table (as shown in fig. 3).
In this embodiment, there are 6 types of LLHs designed in the empty selection table, which are respectively RRS operation, RI operation, RIs operation, RS operation, RSs operation, and SS operation, and are respectively labeled as L1, L2, L3, L4, L5, and L6 (as shown in fig. 3).
Step S3-3, recording the calling scores corresponding to a plurality of calls of the low heuristic algorithm in a neighborhood operation table and forming a call table, wherein the calling scores are obtained by calculation according to a formula (5):
in the formula (f)_currentIs the current initial honey source, f_newIs a new honey source, w is the current initial honey source f_currentWith new honey source f_newThe difference in fitness between, α and β are adaptation values, n is the nth call of the call table, tcurrentRepresenting the number of current iterations, ttotalRepresenting a predetermined total number of iterations, theta being a cost factor, Wi,jDenotes the call score, W, of the ith row and jth column cell in the call tablei,iRepresents the call score of the ith row and ith column cell in the call table.
Where θ is obtained empirically and means that the call score decreases each time an LLH is called.
In this embodiment, the scores of the entire call list are updated every iteration, and the call score is determined by the current heuristic score and the current heuristic score of the next iteration.
Step S3-4, selecting a low heuristic algorithm operation based on the call list to obtain a new honey source ni。
In this embodiment, the next call to LLH is based on the one with the highest call score on the diagonal in the call table.
As shown in fig. 2, the selection mechanism of LLH is based on call tables, and the call score is updated every call of LLH.
Step S4, the employed bee compares the fitness value fit according to equation (3)iAnd using greedy method to obtain the initial honey source aiAnd new honey source niTo perform the selected operation:
in the formula (f)iIs a function of the honey source.
In this embodiment, the selected operation is to hire bees to the original honey source aiAnd new honey source niHas better retention inA honey source.
Step S5, the hiring bee recruits the following bee by the roulette method, the following bee selects the honey source corresponding to the hiring bee, the probability P that the honey source corresponding to the hiring bee is selectediAs shown in formula (4):
in step S6, when the employed bee does not find the optimal honey source within the predetermined iteration time, the employed bee is forced to turn into a scout bee and abandon the honey source corresponding to the employed bee.
In this embodiment, abandoning the honey source corresponding to the employed bee means abandoning a complete route of the feasible solution corresponding to the honey source, i.e. the problem of the traveler.
And step S7, the scout bees continue to search according to a preset search method to obtain the optimal honey source.
And step S8, repeating the steps S3 to S7 until reaching the preset maximum circulation times, and obtaining the only optimal honey source as the shortest path sequence.
In this embodiment, the optimal honey source obtained once per cycle is compared with the optimal honey source obtained in the previous cycle, and only the better one is reserved. After the maximum cycle number is reached, the calculation method only obtains an optimal honey source, namely the shortest path sequence.
Wherein, step S8 may further include the following sub-steps:
step S8-1: and repeating the steps S3 to S7 until reaching the preset maximum cycle number, and obtaining the only optimal honey source.
Step S8-2: and optimizing the unique optimal honey source by a preset local search method to obtain the shortest path sequence.
Fig. 4 is a schematic diagram of a local search method according to an embodiment of the present invention.
Wherein, the local search method is to improve the LK algorithm. The improved LK algorithm specifically comprises the following steps:
step T1, two closed circles are created from the optimal honey source by the 2-opt method.
And step T2, judging whether the total length of the two closed circles is less than the total length of the optimal honey source, if so, entering step T3, and if not, entering step T4.
And step T3, connecting the two closed circles by adopting a 2-opt method to form a complete stroke, and taking the complete stroke as an optimal honey source.
Step T4, two closed circles are created from the optimal honey source by the 3-opt method.
And step T5, connecting the two closed circles by adopting a 3-opt method to form a complete stroke, and taking the complete stroke as an optimal honey source.
As shown in FIG. 4, the 2-opt method is used in (a) and (b) of FIG. 4, and the 3-opt method is used in (c), (d), (e) and (f) of FIG. 4.
In this embodiment, the improved LK algorithm is optimized for the traveler problem with the number of cities less than 100. However, when the improved LK algorithm is used for optimizing the traveling salesman problem with the number of cities being greater than or equal to 100, the shortest path sequence output is influenced because the operation time is too long, so the improved LK algorithm is not used for optimizing the traveling salesman problem with the number of cities being greater than or equal to 100, and the optimal honey source obtained by calculation through the hyper-heuristic algorithm is directly used as the shortest path sequence output.
As shown in fig. 2, when the traveler problem with the number of cities less than 100 is handled, local search optimization is performed on the optimal honey source (i.e., the solution to be optimized in fig. 2) calculated by the super-launch algorithm, and finally the optimal solution, i.e., the shortest path sequence, is obtained.
In order to verify the calculation effect of the multipoint position shortest path calculation method, experiments are performed on 30 typical traveler problems, specifically as follows:
fig. 5 is a diagram illustrating the effects of 30 exemplary traveler questions according to an embodiment of the present invention.
As shown in FIG. 5, Instances are the names of 30 typical traveler questions, and from Instances, it can be seen that the traveler questions contain the number of cities, which is at least 30 and at most 225. Optimum refers to the shortest path known to each typical traveler questionAnd (6) sequencing the calculated total distance. Mu is the total distance calculated after the shortest path sequence is solved by the solving method of the invention. SigmaavgWhich refers to the percentage deviation of the two total legs. The percentage deviations of the 30 typical traveler questions were averaged to give an average score of 0.51%.
It can be seen from fig. 5 that there are 11 typical traveler problems with a percentage deviation of 0, so the method can consistently solve 11 out of 30 instances (i.e., 36.7%) to a known optimal solution for 30 replicates.
Examples effects and effects
According to the method for calculating the shortest path between the multipoint locations provided by the embodiment, because the discrete ABC algorithm serving as a high-order heuristic algorithm and the neighborhood search serving as a low-level heuristic algorithm are combined in the hyper-heuristic algorithm, and a low-heuristic algorithm operation can be called by a new honey source in the discrete ABC algorithm according to a call table in a neighborhood, so that the automatic update of the new honey source is realized, and therefore, the shortest path sequence between the multipoint locations can be found more quickly. The method for calculating the shortest path of the multipoint positions can obtain a better shortest path sequence. In practical application, the method can carry out the most reasonable arrangement on the travel of the travelers according to the shortest path sequence, can also design the most efficient logistics route, can also customize a better airplane flight route for an airline company, and can solve the problem of a series of shortest paths of multipoint positions.
In addition, in the embodiment, although the shortest path sequence can be calculated through the hyper-heuristic algorithm, it cannot be ensured that the shortest path sequence obtained by the method under some conditions is the one closest to the correct solution, and therefore, the shortest path sequence is ensured to be closest to the correct solution because the unique shortest path sequence calculated through the hyper-heuristic algorithm is further optimized through improving the LK algorithm.
The above-mentioned embodiments are merely illustrative of specific embodiments of the present invention, and the present invention is not limited to the description of the above-mentioned embodiments.
In the above embodiment, the low-heuristic operation only uses 6 operations, namely, RRS operation, RI operation, RIs operation, RS operation, RSs operation, and SS operation, and the invention can also use RRSs operation, RRIS operation, RSIS operation, RSSs operation, and other low-heuristic operations.
In the above embodiment, since it takes too long to perform local search optimization on the problems with the number of predetermined locations being greater than or equal to 100, the shortest path sequence is directly calculated by the hyper-heuristic algorithm for the problems with the number of predetermined locations being greater than or equal to 100, and the present invention may perform or not perform improved LK algorithm optimization on the problems with the number of predetermined locations being greater than or equal to 100.
Claims (6)
1. A multipoint position shortest path calculation method is used for solving a shortest path sequence passing through all positions only once, and is characterized by comprising the following steps:
step S1, setting a random sequence formed by randomly arranging a plurality of preset positions as a honey source;
step S2, randomly creating Popsize/2 feasible solutions according to the formula (1) to initialize the honey source to obtain the initial honey source aiFor each of said initial honey sources aiAllocating one employed bee:
ai＝[1,randperm(ncitys-1)+1,1] (1)
where i ∈ {1, 2., Popsize/2}, Popsize/2 is the number of said employed bees, ncitysRepresenting the number of the cities;
step S3, the hiring bee selects one low-heuristic operation from a plurality of low-heuristic operations through a predetermined algorithm so as to update the initial honey source to obtain a new honey source ni：
ni＝llhx(ai) (2)
In the formula, llhxIs the xth of the low heuristic operation;
step S4, the hiring bee compares the fitness value fit according to equation (3)iAnd using greedy method to obtain the initial honey source aiAnd the new honey source niTo perform the selected operation:
in the formula (f)iIs the function value;
step S5, the hiring bee recruits following bees by the roulette method, the following bees select the honey sources corresponding to the hiring bee, and the probability P that the honey sources corresponding to the hiring bee are selectediAs shown in equation (4):
step S6, when the employed bee does not find the optimal honey source within the preset iteration time, the employed bee is forced to be converted into a scout bee and abandons the honey source corresponding to the employed bee;
step S7, the scout bees continue to search according to a preset search method to obtain the optimal honey source;
and step S8, repeating the steps S3 to S7 until reaching the preset maximum circulation times, and obtaining the only optimal honey source as the shortest path sequence.
2. A multipoint position shortest path calculation method according to claim 1, characterized by:
wherein the step S3 includes the following sub-steps:
step S3-1, creating a null selection list;
step S3-2, designing a plurality of low heuristic algorithm operations of different types in the null selection table to obtain a neighborhood operation table;
step S3-3, recording the calling scores corresponding to a plurality of calls of the low heuristic algorithm in the neighborhood operation table and forming a call table, wherein the calling scores are obtained by calculation according to a formula (5):
in the formula (f)_currentIs the current initial honey source, f_newIs the new honey source, w is the current initial honey source f_currentWith the new honey source f_newThe difference in fitness between, α and β are adaptation values, n is the nth call of the call table, tcurrentRepresenting the number of current iterations, ttotalRepresenting a predetermined total number of iterations, theta being a cost factor, Wi,jRepresenting the call score, W, of the ith row and jth column cells in the call tablei,iRepresenting the call score of the ith row and ith column cell in the call table;
step S3-4, selecting a low heuristic algorithm operation based on the call list to obtain the new honey source ni。
3. A multipoint position shortest path calculation method according to claim 2, characterized by:
the low heuristic algorithm operation is any one or a combination of a plurality of RRS operation, RI operation, RIS operation, RS operation, RSS operation and SS operation.
4. A multipoint position shortest path calculation method according to claim 2, characterized by:
wherein the invocation score is any more of a reward invocation score, a penalty invocation score, and an unmodified invocation score.
5. A multipoint position shortest path calculation method according to claim 1, characterized by:
wherein, the step S8 may further include the following sub-steps:
step S8-1: repeating the steps S3 to S7 until a predetermined maximum number of cycles is reached, resulting in a unique optimal honey source;
step S8-2: and optimizing the unique optimal honey source by a preset local search method to obtain the shortest path sequence.
6. A multipoint position shortest path calculation method according to claim 5, characterized by:
wherein the local search method is to improve LK algorithm,
the step S8-2 includes the following sub-steps:
step T1, creating two closed circles from the optimal honey source through a 2-opt method;
step T2, judging whether the total length of the two closed circles is less than the total length of the optimal honey source, if so, entering step T3, and if not, entering step T4;
step T3, connecting the two closed circles by a 2-opt method to form a complete stroke, and taking the complete stroke as the optimal honey source;
a step T4 of creating two said closed circles from said optimal honey source by means of the 3-opt method;
and step T5, connecting two closed circles by adopting a 3-opt method to form the complete stroke, and taking the complete stroke as the shortest path sequence.
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Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| WO2014087590A1 (en) * | 2012-12-05 | 2014-06-12 | 日本電気株式会社 | Optimization device, optimization method and optimization program |
| CN107045717A (en) * | 2017-03-21 | 2017-08-15 | 南京邮电大学 | The detection method of leucocyte based on artificial bee colony algorithm |
| CN108830371A (en) * | 2018-06-07 | 2018-11-16 | 福州大学 | A kind of improvement artificial bee colony algorithm solving the problems, such as TSP |
| CN108875896A (en) * | 2018-06-08 | 2018-11-23 | 福州大学 | A kind of disturbance chaos artificial bee colony algorithm certainly of global optimum's guidance |
| CN109116816A (en) * | 2018-07-25 | 2019-01-01 | 昆明理工大学 | The Optimization Scheduling of printing process under a kind of Flexible Manufacture environment |
| US20190080270A1 (en) * | 2017-09-11 | 2019-03-14 | Hefei University Of Technology | Production scheduling method and system based on improved artificial bee colony algorithm and storage medium |
Patent Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| WO2014087590A1 (en) * | 2012-12-05 | 2014-06-12 | 日本電気株式会社 | Optimization device, optimization method and optimization program |
| CN107045717A (en) * | 2017-03-21 | 2017-08-15 | 南京邮电大学 | The detection method of leucocyte based on artificial bee colony algorithm |
| US20190080270A1 (en) * | 2017-09-11 | 2019-03-14 | Hefei University Of Technology | Production scheduling method and system based on improved artificial bee colony algorithm and storage medium |
| CN108830371A (en) * | 2018-06-07 | 2018-11-16 | 福州大学 | A kind of improvement artificial bee colony algorithm solving the problems, such as TSP |
| CN108875896A (en) * | 2018-06-08 | 2018-11-23 | 福州大学 | A kind of disturbance chaos artificial bee colony algorithm certainly of global optimum's guidance |
| CN109116816A (en) * | 2018-07-25 | 2019-01-01 | 昆明理工大学 | The Optimization Scheduling of printing process under a kind of Flexible Manufacture environment |
Non-Patent Citations (1)
| Title |
|---|
| 刘聪;程龙;曾庆田;闻立杰;欧阳春;: "基于Petri网的分层业务过程挖掘方法", 计算机集成制造系统, no. 06, 15 June 2020 (2020-06-15) * |
Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN114462764A (en) * | 2021-12-22 | 2022-05-10 | 上海新时达电气股份有限公司 | Dispatching method of multilayer multi-port hoister |
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