CN110288297A - A method of optimal Distribution path in Cold Chain Logistics is applied to based on Heuristic Ant Colony Algorithm - Google Patents
A method of optimal Distribution path in Cold Chain Logistics is applied to based on Heuristic Ant Colony Algorithm Download PDFInfo
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Abstract
The invention discloses a kind of methods for being applied to optimal Distribution path planning in Cold Chain Logistics based on heuristic mechanism and improvement ant group algorithm.Environmental information is divided into a series of grids with two value informations with grid method by the present invention, and each grid, which has, to be occupied and free two states, be may then pass through modeling and simulating and is carried out path planning on grid map；It is planned using the improvement ant group algorithm based on Self-configuring.Test result shows that compared with classical ant group algorithm, the improvement ant group algorithm based on heuristic mechanism can be improved convergence speed of the algorithm, while the penalty introduced can be avoided algorithm and fall into local optimum.
Description
[technical field]
The present invention relates to the path plannings in Cold Chain Logistics, especially for the ant group algorithm in path planning problem.
[background technique]
Classical ant group algorithm is longer there are search time in path planning, and being easily trapped into local optimum causes algorithm to be stagnated
The problems such as, invention introduces the thoughts of heuristic mechanism, improve the update mechanism of pheromones, pass through both candidate nodes and target
Point the distance between relationship dynamic adjustment heuristic function so that distribution weight with apart from size inversely, to improve algorithm
Convergence rate.In addition, introducing penalty to avoid algorithm from falling into local optimum, reduces the influence of search positive feedback, make to calculate
Method jumps out local optimum.
[summary of the invention]
What the present invention designed is applied to optimal Distribution path in Cold Chain Logistics based on heuristic mechanism and improvement ant group algorithm
A kind of method of planning, comprising the following steps:
(1) handled with grid method environmental information: Grid Method is that a kind of common environment describes method, it is by environment
It is divided into a series of grids with two value informations, each grid, which has, to be occupied and free two states, may then pass through optimization
Algorithm carries out path planning on grating map, and black grid indicates that barrier, white grid indicate clear.
(2) the update rule of local information element is set: after every ant is moved to next node j from a certain node i, according to
Formula (1) updates the pheromones on arc (i, j), and the update mode of such information element is more prone to ant during path construction
In the selection path different from last path, the formula of local updating rule are as follows:
τij(t+1)=(1- ξ) τij(t)+ξτ0 (1)
(3) the update rule of global information element is set: the route searching of ant can be made to have more needle with such update mode
To property, ant is more likely to the optimal path found until searching algorithm current iteration.The overall situation updates rule are as follows:
τij(t+1)=(1- ρ) τij(t)+ρΔτij (2)
(4) determine inspiration value: enabling η is an inspiration value, this value depends on side (i, j), this value is set in the present invention
It is set to the inverse of distance between customer and distribution point.
(5) transition probability is determined: as follows in the probability distribution that t moment ant k is transferred to position j by position i:
(6) pheromones: the track intensity renewal equation of adjustment information amount are updated are as follows:
τij(t+1)=ρ τij(t)+Δτij, ρ ∈ (0,1) (4)
[the advantages and positive effects of the present invention]
The present invention is longer there are search time in path planning for classical ant group algorithm, is easily trapped into local optimum and leads
The problems such as causing algorithm to stagnate, introduces the thought of heuristic mechanism herein, improves the update mechanism of pheromones, propose one kind
Improvement ant group algorithm based on heuristic mechanism, for solving the path planning problem of optimal dispatching in Cold Chain Logistics.Pass through time
The distance between node and target point relationship dynamic adjustment heuristic function is selected, so that distribution weight is inversely proportional pass with apart from size
System, to improve convergence speed of the algorithm.In addition, introducing penalty to avoid algorithm from falling into local optimum, it is positive and negative to reduce search
The influence of feedback, makes algorithm jump out local optimum.
Emulation experiment is carried out using MATLAB, classic algorithm is compared, based on the improvement ant group algorithm of heuristic mechanism most
There is good performance boost in short distance and convergence rate.
[Detailed description of the invention]
Fig. 1 is Heuristic Ant Colony Algorithm flow chart；
Fig. 2 is special obstacle substance environment path planning figure；
Fig. 3 is convergence curve figure under special obstacle substance environment；
Fig. 4 is path planning figure under general obstacle environment；
Fig. 5 is convergence curve figure under general obstacle environment.
[specific embodiment]
The following further describes the present invention with reference to the drawings.
It is as shown in Figure 1 that Heuristic Ant Colony Algorithm executes process.It is as shown in table 1 that pseudo-code of the algorithm is provided in conjunction with flow chart.
Table 1: ant group algorithm pseudocode is improved
Parameter is first set before starting experiment, as shown in table 2:
2 parameter setting of table
Preparation first by parameter initialization, before carrying out emulation experiment.Environmental information is handled with grid method: general
There was only home-delivery center and place to be sent in the delivery system of dispatching problem, model is mostly from home-delivery center, and traversal is all
The path optimization model of home-delivery center is returned to after node, and traversal order is random.For the traversal of realization " first picking, rear delivery "
Path optimization model is set unidirectional path by sequence requirement, the present invention, and unidirectional path includes that businessman's collection is directed toward by initial position
Unidirectional path and the unidirectional path of corresponding customer is directed toward by businessman.In addition, dispatching task terminates at customer, without returning
Beginning position.
The service request that can not be able to satisfy is solved and prevented in order to simplify, it is assumed that without departing from vehicle when dispatching person's order
Dead weight, and have it is assumed hereinafter that:
1) before not completing all dispatching tasks at this stage, dispatching person's no longer order；
2) dispatching person is from current initial position, it is necessary to which first going to cold chain warehouse just can go at corresponding customer；
3) cold chain warehouse and corresponding customer can and can only service primary；
4) it does not need to return to initial position after completing all dispatching tasks, may be selected to terminate to dispense or carry out down at this stage
Stage dispatching；
It is as shown in Figure 2 that effect is completed in modeling.
In conjunction with Fig. 1 and table 1, the algorithm flow designed the present invention is described below:
(1) the update rule of local information element is set: after every ant is moved to next node j from a certain node i, according to
Formula (1) updates the pheromones on arc (i, j), and the update mode of such information element is more prone to ant during path construction
In the selection path different from last path, the formula of local updating rule are as follows:
τij(t+1)=(1- ξ) τij(t)+ξτ0 (1)
(2) the update rule of global information element is set: the route searching of ant can be made to have more needle with such update mode
To property, ant is more likely to the optimal path found until searching algorithm current iteration.The overall situation updates rule are as follows:
τij(t+1)=(1- ρ) τij(t)+ρΔτij (2)
(3) determine inspiration value: enabling η is an inspiration value, this value depends on side (i, j), this value is set in the present invention
It is set to the inverse of distance between customer and distribution point.
(4) transition probability is determined: as follows in the probability distribution that t moment ant k is transferred to position j by position i:
(5) pheromones: the track intensity renewal equation of adjustment information amount are updated are as follows:
τij(t+1)=ρ τij(t)+Δτij, ρ ∈ (0,1) (4)
The Pheromone update mechanism and penalty that the present invention is directed in classical ant group algorithm improve, and detailed process is such as
Under:
1) Pheromone update mechanism
Residual risk is excessive on the path passed by order to avoid ant, weakens the effect of heuristic information, to hinder algorithm
Globally optimal solution is found, locally optimal solution is fallen into, so needing to cover a step in every ant or complete to all target points
Traversal (i.e. one circulation terminates) after, processing is updated to residual risk, with improve solution quality and algorithm convergence speed
Degree.
The update rule of local information element is set: after every ant is moved to next node j from a certain node i, according to formula
(1) pheromones on arc (i, j) are updated, the update mode of such information element is more likely to ant during path construction
Select the path different from last path, the formula of local updating rule are as follows:
τij(t+1)=(1- ξ) τij(t)+ξτ0 (1)
The update rule of global information element is set: the route searching of ant can be made more to be directed to such update mode
Property, ant is more likely to the optimal path found until searching algorithm current iteration.The overall situation updates rule are as follows:
τij(t+1)=(1- ρ) τij(t)+ρΔτij (2)
2) penalty
In order to avoid classical ant group algorithm falls into local optimum, penalty τ is introduced hereinij=λ τij, λ ∈ (0,1), tool
Body way is as follows:
1. during Ant Search, when encountering complicated landform, it is easy to form path deadlock state (ant in other words
Into " death " state).To make the pheromones rapid decrease on trap surrounding paths without influencing the search of ant next time, draw
Enter penalty.
2. optimal path length continuous 10 generation does not change, penalty is introduced.
Improved Pheromone update formula are as follows:
In order to verify the validity for improving ant group algorithm, has chosen classical AS algorithm and ACS and compare experiment.
Experimental result is as shown in Figures 2 and 3 under special obstacle substance environment, and table 3 is corresponding detail statistics result.
Table 3: the statistical result of algorithms of different
Many spill barriers are provided in Fig. 2, it can be seen that classical AS and ACS algorithm (is arranged near starting point
Point is the upper left corner, and terminal is the lower right corner) fall into local optimum.And IAS algorithm proposed by the present invention is due to introducing punishment
Function, the pheromones on current optimal path are reallocated, it can be made to reduce the influence that ant is searched for next time, from
And local optimum is jumped out, global optimum path is obtained, can be seen that the present invention in the quality of solution by data in Fig. 3 and table 3
Show good performance.Result also indicates that IAS algorithm also has a clear superiority in convergence rate in Fig. 3 simultaneously.
For more general obstacle environment and larger sized grid environment, carries out second and tested, experiment knot
As shown in figs. 4 and 5, table 4 is corresponding detail statistics result to fruit.
Table 4: the statistical result of algorithms of different
As can be seen that in more general obstacle environment, IAS algorithm of the invention can still obtain than AS and
ACS algorithm more preferably solves, and algorithm the convergence speed is faster.In summary two experiments, improvement ant colony proposed by the present invention are calculated
Method is feasible and effective.
Claims (1)
1. what the present invention designed is applied to optimal Distribution path rule in Cold Chain Logistics based on heuristic mechanism and improvement ant group algorithm
A kind of method drawn, comprising the following steps:
(1) handled with grid method environmental information: Grid Method is that a kind of common environment describes method, and environment is divided by it
A series of grids with two value informations, each grid, which has, to be occupied and free two states, may then pass through optimization algorithm
Path planning is carried out on grating map, black grid indicates that barrier, white grid indicate clear.
(2) the update rule of local information element is set: after every ant is moved to next node j from a certain node i, according to formula
(1) pheromones on arc (i, j) are updated, the update mode of such information element is more likely to ant during path construction
Select the path different from last path, the formula of local updating rule are as follows:
τij(t+1)=(1- ξ) τij(t)+ξτ0 (1)
(3) the update rule of global information element is set: the route searching of ant can be made to have more specific aim with such update mode,
Ant is more likely to the optimal path found until searching algorithm current iteration.The overall situation updates rule are as follows:
τij(t+1)=(1- ρ) τij(t)+ρΔτij (2)
(4) determine inspiration value: enabling η is an inspiration value, this value depends on side (i, j), this value is set as in the present invention
The inverse of distance between customer and distribution point.
(5) transition probability is determined: as follows in the probability distribution that t moment ant k is transferred to position j by position i:
(6) pheromones: the track intensity renewal equation of adjustment information amount are updated are as follows:
τij(t+1)=ρ τij(t)+Δτij, ρ ∈ (0,1) (4).
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Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN111967668A (en) * | 2020-08-17 | 2020-11-20 | 安徽理工大学 | Cold chain logistics path optimization method based on improved ant colony algorithm |
| CN115829183A (en) * | 2023-02-22 | 2023-03-21 | 四川港投新通道物流产业投资集团有限公司 | Cold-chain logistics path planning method, device, equipment and readable storage medium |
-
2019
- 2019-06-26 CN CN201910558060.8A patent/CN110288297A/en active Pending
Cited By (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN111967668A (en) * | 2020-08-17 | 2020-11-20 | 安徽理工大学 | Cold chain logistics path optimization method based on improved ant colony algorithm |
| CN115829183A (en) * | 2023-02-22 | 2023-03-21 | 四川港投新通道物流产业投资集团有限公司 | Cold-chain logistics path planning method, device, equipment and readable storage medium |
| CN115829183B (en) * | 2023-02-22 | 2023-05-02 | 四川港投新通道物流产业投资集团有限公司 | Cold chain logistics path planning method, device, equipment and readable storage medium |
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