CN114971089A - Traditional dealer optimal distribution route optimization system based on improved ant colony algorithm - Google Patents
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Abstract
The application discloses a traditional dealer optimal distribution route optimization system based on an improved ant colony algorithm. The system comprises: the optimal distribution route determining module is used for determining an optimal distribution route from a distributor to a distributor; the optimal distribution route optimization module is used for optimizing the optimal distribution route based on an improved ant colony algorithm; the improved ant colony algorithm is configured with an inverse variation rule which is as follows: and when the path traveled by the ant colony individuals meets a preset inversion variation condition, performing inversion variation on the ant colony individuals for a preset variation time. The system can improve the optimization efficiency of the distribution route and realize the efficient and accurate optimization of the distribution route.
Description
Technical Field
The present application relates to path planning, and more particularly, to an optimized distribution route optimization system for a traditional dealer based on an improved ant colony algorithm.
Background
At present, before the traditional dealer carries out goods distribution, the distribution route is planned through some algorithms, and the goods are distributed according to the planned distribution routes, so that the distribution efficiency can be improved as much as possible, or the distribution cost can be reduced. Therefore, the algorithm selection of route planning is important for the dealer.
In the prior art, a common route planning algorithm is an ant colony algorithm, which is a probabilistic algorithm for finding an optimized path. The basic idea of applying the ant colony algorithm to solve the optimization problem is as follows: and (3) representing a feasible solution of the problem to be optimized by using the walking paths of the ants, wherein all paths of the whole ant group form a solution space of the problem to be optimized. The shorter ants release a larger amount of pheromone, and as time advances, the concentration of pheromone accumulated on the shorter paths gradually increases, and the number of ants selecting the paths also increases. Finally, the whole ant can be concentrated on the optimal path under the action of positive feedback, and the corresponding optimal solution of the problem to be optimized is obtained.
When the population cardinality is large in the traditional ant colony model, since the information amount on the path in the initial stage of the pheromone search is small, it is difficult to retrieve a good path from a large number of paths in a short time.
Therefore, in the process of implementing path optimization by the existing ant colony algorithm, due to the problem of the ant colony search mechanism, the search efficiency is low, and thus efficient optimization of the distribution route cannot be implemented.
The information disclosed in this background section is only for enhancement of understanding of the general background of the application and should not be taken as an acknowledgement or any form of suggestion that this information forms the prior art already known to a person skilled in the art.
Disclosure of Invention
The application aims to provide a traditional dealer optimal delivery route optimization system based on an improved ant colony algorithm, which can improve the optimization efficiency of delivery routes and realize efficient and accurate optimization of the delivery routes.
To achieve the above object, an embodiment of the present application provides a traditional dealer optimal delivery route optimization system based on an improved ant colony algorithm, including: the optimal distribution route determining module is used for determining an optimal distribution route from a distributor to a distributor; the optimal distribution route optimization module is used for optimizing the optimal distribution route based on an improved ant colony algorithm; in the improved ant colony algorithm, an inverse variation rule is configured, where the inverse variation rule is: when the path of the ant colony individuals meets a preset reversion variation condition, performing reversion variation on the ant colony individuals for a preset variation time, wherein the preset reversion variation condition is represented as: d1+ d2 < d3+ d 4; wherein d1 is a distance between a current path traveled by a first ant colony individual and a current path traveled by a second ant colony individual, d2 is a distance between a current path traveled by a third ant colony individual and a current path traveled by a fourth ant colony individual, d3 is a distance between a current path traveled by the first ant colony individual and a current path traveled by the third ant colony individual, and d4 is a distance between a current path traveled by the second ant colony individual and a current path traveled by the fourth ant colony individual; the third ant colony individual is next to the first ant colony individual, the fourth ant colony individual is next to the second ant colony individual, and the ant colony individuals with reversed variation are the third ant colony individual and the second ant colony individual.
In one or more embodiments of the present application, the improved ant colony algorithm includes: heuristic factors, mobile probability selection rules and pheromone update rules.
In the embodiment, the accuracy of the improved ant colony algorithm is improved by configuring the heuristic factors, the mobile probability selection rules and the pheromone updaters.
In one or more embodiments of the present application, the heuristic factor is determined based on the total product quality required by the distributor and the distance of the path taken by the corresponding individual ant colony.
In this embodiment, the heuristic factor is determined based on the total product quality required by the distributor and the distance of the path taken by the corresponding individual ant colony, so that the heuristic factor of the ant colony algorithm is effectively determined.
In one or more embodiments of the present application, the movement probability selection rule is determined based on heuristic factors of paths traveled by the ant colony individuals, pheromone concentrations left by the ant colony individuals on the traveled paths, selectable path node sets of the ant colony individuals, pheromone importance degrees and heuristic factor importance degrees.
In the embodiment, the effective determination of the movement probability selection is realized based on the heuristic factor of the path traveled by the ant colony individuals, the pheromone concentration of the ant colony individuals on the traveled path, the optional path node set of the ant colony individuals, the pheromone importance degree and the heuristic factor importance degree.
In one or more embodiments of the present application, the pheromone update rule is: updating pheromones based on the total pheromone amount of the target path, the path length of the target path and pheromone volatilization factors; wherein the target path is: and under the preset condition of each cost limit, minimizing the total cost.
In this embodiment, the pheromone is efficiently updated based on the total pheromone amount of the target route, the route length of the target route, and the pheromone volatilization factor.
In one or more embodiments of the present application, the optimal delivery route optimization module is further configured to: respectively calculating the distribution cost of the optimal distribution route and the optimized optimal distribution route under different dimensions; comparing the distribution cost of the optimal distribution route and the optimized optimal distribution route under different dimensions; if the distribution cost of the optimal distribution route under different dimensions is higher than that of the optimized optimal distribution route, determining the optimized optimal distribution route as a final distribution route; if the distribution cost of the optimal distribution route in at least one dimension is lower than the distribution cost of the optimized optimal distribution route, updating the inverse variation rule configured in the improved ant colony algorithm, and optimizing the optimal distribution route again based on the improved ant colony algorithm updating the inverse variation rule.
In the embodiment, after the optimized optimal distribution route is determined, the distribution costs of the optimal distribution route and the optimized optimal distribution route in different dimensions are compared, and the inverse variation rule is updated based on the distribution costs in different dimensions, so that the optimal distribution route is more accurately optimized.
In one or more embodiments of the present application, the optimal delivery route optimization module updates an inverse variation rule configured in the improved ant colony algorithm, including: if the distribution cost of the optimal distribution route in one dimension is lower than that of the optimized optimal distribution route, increasing the preset variation times; if the distribution cost of the optimal distribution route in at least two dimensions is lower than that of the optimized optimal distribution route, when the path traveled by the ant colony individuals meets a preset inversion variation condition, the ant colony individuals subjected to inversion variation are updated to the first ant colony individuals and the fourth ant colony individuals.
In this embodiment, under different conditions, by increasing the preset variation times and changing the ant colony individuals with the reverse variation, the reverse variation rule is effectively updated, so that the updated reverse variation rule can ensure the accuracy of the ant colony search result while ensuring the ant colony search efficiency.
In one or more embodiments of the present application, the optimal delivery route determining module is specifically configured to: determining a plurality of optimal distribution routes from a distributor to a distributor through a plurality of preset distribution route determination algorithms, wherein each distribution route determination algorithm corresponds to at least one optimal distribution route; respectively calculating the distribution cost of the optimal distribution routes in different dimensions, and determining the total distribution cost corresponding to the optimal distribution routes based on the distribution cost in different dimensions; and determining the optimal distribution route with the minimum total distribution cost as the final optimal distribution route.
In this embodiment, the initial optimal delivery route may be determined based on a plurality of delivery routes, and the optimal delivery route is the delivery route from which the determined delivery cost is the least, so that the improved ant colony algorithm achieves more efficient search.
In one or more embodiments of the present application, the optimal delivery route includes: a plurality of optimal distribution routes from the distributor to a plurality of distributors respectively; the optimal distribution route optimization module is specifically used for optimizing the optimal distribution routes respectively based on an improved ant colony algorithm to obtain multiple optimized distribution routes; the optimal delivery route optimization module is further to: and merging the optimal distribution routes based on the improved ant colony algorithm to obtain at least one merged optimized optimal distribution route.
In this embodiment, for the optimized optimal distribution routes respectively corresponding to the multiple distributors, further optimization may be implemented by using an improved ant colony algorithm, so that the finally obtained optimal distribution route meets the optimal requirement.
The embodiment of the present application further provides a traditional dealer optimal delivery route optimization method based on the improved ant colony algorithm, including: determining an optimal distribution route from a distributor to a distributor; optimizing the optimal delivery route based on an improved ant colony algorithm; in the improved ant colony algorithm, an inverse variation rule is configured, where the inverse variation rule is: when the path traveled by the ant colony individuals meets a preset inversion variation condition, performing inversion variation on the ant colony individuals for a preset variation time, wherein the preset inversion variation condition is expressed as: d1+ d2 < d3+ d 4; wherein d1 is a distance between a current path traveled by a first ant colony individual and a current path traveled by a second ant colony individual, d2 is a distance between a current path traveled by a third ant colony individual and a current path traveled by a fourth ant colony individual, d3 is a distance between a current path traveled by the first ant colony individual and a current path traveled by the third ant colony individual, and d4 is a distance between a current path traveled by the second ant colony individual and a current path traveled by the fourth ant colony individual; the third ant colony individual is next to the first ant colony individual, the fourth ant colony individual is next to the second ant colony individual, and the ant colony individuals with reversed variation are the third ant colony individual and the second ant colony individual.
Compared with the prior art, according to the system and the method for optimizing the optimal distribution route of the traditional dealer based on the improved ant colony algorithm, the traditional ant colony algorithm is improved, the reverse variation rule is added into the traditional ant colony algorithm, the same times of operation can be achieved within a short time when the ant colony is searched through the reverse variation rule, the time consumed by calculation is reduced, and the performance of a new generation can be improved through a variation operator, so that the performance of the whole ant colony is improved. Therefore, on the basis of the improvement of the performance of the ant colony, the optimization efficiency of the distribution route can be improved, the accuracy of the optimized distribution route can be ensured, and the distribution route can be efficiently and accurately optimized based on the improved ant colony algorithm.
Drawings
Fig. 1 is a flowchart of a conventional dealer optimal distribution route optimization method based on an improved ant colony algorithm according to an embodiment of the present application;
fig. 2 is a schematic structural diagram of a conventional dealer optimal distribution route optimization system based on an improved ant colony algorithm according to an embodiment of the present application;
fig. 3 is a schematic structural diagram of an electronic device according to an embodiment of the present application.
200-a traditional dealer optimal delivery route optimization system based on the improved ant colony algorithm; 210-an optimal delivery route determination module; 220-optimal delivery route optimization module.
Detailed Description
The following detailed description of embodiments of the present application is provided in conjunction with the accompanying drawings, but it should be understood that the scope of the present application is not limited to the specific embodiments.
Throughout the specification and claims, unless explicitly stated otherwise, the word "comprise", or variations such as "comprises" or "comprising", will be understood to imply the inclusion of a stated element or component but not the exclusion of any other element or component.
The technical scheme of the embodiment of the application can be applied to various application scenes of route planning, and the planned route is used for the dealer to deliver commodities. The goods delivered may be various types of goods, such as: cold chain food, wherein the corresponding distribution vehicle is a refrigerator car; for another example: the common goods and the corresponding distribution vehicle are trucks and the like.
In different application scenarios, the restrictions to be considered when planning the delivery route may be different based on the characteristics of the delivered goods, or the characteristics of the delivery vehicle, or the delivery environment (e.g., delivering goods to a relatively remote area).
For example, for the distribution of cold chain food, costs are considered in several aspects, including but not limited to: the storage cost of goods entering the refrigerator, the fixed cost in the distribution process of the refrigerator car, the refrigeration cost in the distribution process of the refrigerator car, the goods loss cost in the distribution process of the refrigerator car, the carbon emission cost and the like.
Based on the cost limiting conditions, the cost limiting conditions are added into an algorithm model when a route planning algorithm is designed, and then route planning is carried out based on the cost limiting conditions when the algorithm runs.
In addition, in different application scenarios, the calculation methods of various costs need to be configured in combination with specific application scenarios.
In the embodiments of the present application, no improvement is made in the part of the limitation. That is, the ant colony algorithm may still be combined with different application scenarios, and adopt some configuration modes of limiting conditions or different cost calculation modes in the mature technology in the field. In the technical solution provided in the embodiment of the present application, the search strategy adopted by the ant colony algorithm is mainly improved, that is, the path search strategy of the ant colony individuals is improved, so that each ant colony individual can more quickly search a path that meets the constraint condition.
In the ant colony algorithm, the ant finds the shortest path thanks to pheromones and environment, assuming that there are two paths leading from the ant nest to food, the number of ants on the two paths is almost the same at the beginning: the ants return immediately after reaching the end point, the ants on the short-distance road have short round-trip time and high repetition frequency, the number of the ants in round trip in unit time is large, the left pheromone is also large, more ants can be attracted, and more pheromones can be left. While long distance paths are the opposite, so more and more ants gather on the shortest path.
Ants have intelligent behavior that benefits from their simple behavior rules, which give them diversity and positive feedback. When foraging, the diversity ensures that ants can not go into dead and endless circulation, and is innovative; the positive feedback enables good information to be stored, and the learning strengthening capability is provided. The smart combination of the two enables intelligent behaviors to emerge, if diversity is excessive, the system is too active, excessive random motion can be caused, and the system falls into a chaotic state; if the diversity is not enough, the positive feedback is too strong, so that the ant colony can not be adjusted correspondingly when the environment changes.
Therefore, when the ant colony algorithm is designed, the ant colony algorithm can be configured in various ways, so that the ant colony individuals can search for the path according to the configured rules.
Based on the introduction of the application scenario, the hardware operating environment of the technical solution provided in the embodiment of the present application may be: a dealer system, which may be in the form of: the form of server, client, server + client, etc., is not limited herein.
For ease of understanding, the technical solutions of the embodiments of the present application will be described first from the perspective of the method side.
As shown in fig. 1, a flowchart of a traditional dealer optimal distribution route optimization method based on an improved ant colony algorithm provided in an embodiment of the present application is provided, where the optimization method includes:
step 110: and determining an optimal distribution route from the distributor to the distributor.
In combination with the foregoing description of the application scenario, the dealer herein may be a dealer of various items to be delivered, and in different application scenarios, the corresponding items to be delivered are different, and the application scenario is not limited herein.
It is common for a distributor to distribute items to multiple distributors. Therefore, the distributor herein may be one distributor or a plurality of distributors. Correspondingly, the optimal delivery route can be an optimal delivery route from a distributor to a distributor; the method can also comprise the steps that the optimal distribution routes from the distributor to the plurality of distributors are respectively included, namely, the article distribution from the distributor to the plurality of distributors is realized through the optimal distribution routes respectively; it may also be an optimal delivery route of the dealer to the plurality of distributors, that is, by which the delivery of the goods from the dealer to the plurality of distributors is enabled.
The optimal delivery route here may be understood as an initial delivery route in which a plurality of route nodes are included, for example: from the A place to the B place, the middle may pass through the C-F places, and the C-F places all belong to route nodes.
As an alternative embodiment, step 110 includes: determining a plurality of optimal distribution routes from the distributor to the distributor through a plurality of preset distribution route determination algorithms; respectively calculating the distribution cost of the optimal distribution routes in different dimensions, and determining the total distribution cost corresponding to the optimal distribution routes based on the distribution cost in different dimensions; and determining the optimal distribution route with the minimum total distribution cost as a final optimal distribution route.
In such an embodiment, a plurality of optimal delivery routes from distributor to distributor may be determined using some conventional delivery route determination algorithm. The plurality of optimal distribution routes herein include: each delivery route determining algorithm determines an optimal delivery route. It will be appreciated that the determined optimal delivery routes may be the same or different due to different rules for different delivery route determination algorithms.
The predetermined distribution route determining algorithm may adopt a distribution route determining method mature in the art, and will not be described in detail herein.
Therefore, for a plurality of optimal delivery routes, the deduplication processing may be performed first. That is, for the same optimal distribution route among the multiple optimal distribution routes, only one optimal distribution route may be reserved, and the others are reserved. The same optimal distribution route refers to a route with the same route nodes and route walking modes, namely, the sequence between the required route nodes and the route nodes needs to be completely the same, and if only the route nodes are the same but the traveling modes are different, the route nodes and the route nodes do not belong to the same optimal distribution route.
After the deduplication processing is performed, a plurality of optimal distribution routes are obtained, and at this time, distribution costs of the optimal distribution routes in different dimensions are calculated respectively. Different dimensions here, in different application scenarios, can be flexibly configured, for example: time cost, road toll cost, other special costs, and the like, but are not limited thereto.
After the delivery costs in different dimensions are determined, total delivery costs corresponding to the optimal delivery routes can be determined based on the delivery costs in different dimensions.
In some embodiments, the total delivery cost is the sum of the delivery costs in different dimensions. In other embodiments, the total delivery cost is a weighted sum of the delivery costs in different dimensions. Namely, a weight value is correspondingly set for the costs of different dimensions, and each delivery cost is multiplied by the weight value to obtain a weighted cost value, and then the weighted cost values are summed.
After the total distribution cost corresponding to each of the optimal distribution routes is determined, the distribution route with the minimum total distribution cost may be determined as the final optimal distribution route. If the distribution route with the minimum total distribution cost comprises a plurality of distribution routes, any one of the distribution routes can be determined as a final optimal distribution route; the distribution route with the minimum distribution cost under the specified dimensionality can also be determined as the final optimal distribution route; or other determination methods, and is not limited herein.
In this embodiment, the initial optimal delivery route may be determined based on a plurality of delivery routes, and the optimal delivery route is the delivery route from which the determined delivery cost is the least, so that the improved ant colony algorithm achieves more efficient search.
Further, if the optimal delivery route corresponding to each of the plurality of dealers is involved in step 110, the optimal delivery route is determined for each dealer in the above-described embodiment.
Further, the optimal delivery route determined in step 110 may only take into account some cost constraints, or may not be searched more thoroughly, and may be further searched using the improved ant colony algorithm. For the improved ant colony algorithm, path planning can be performed again based on some paths formed by all the route nodes in the optimal distribution route so as to optimize the route.
Step 120: and optimizing the optimal distribution route based on the improved ant colony algorithm.
In the improved ant colony algorithm, an inverse variation rule is configured, and the inverse variation rule is as follows: when the path traveled by the ant colony individuals meets a preset inversion variation condition, performing inversion variation on the ant colony individuals for a preset variation time, wherein the preset inversion variation condition is represented as: d1+ d2 < d3+ d 4; wherein d1 is a distance between a current path taken by the first ant colony individual and a current path taken by the second ant colony individual, d2 is a distance between a current path taken by the third ant colony individual and a current path taken by the fourth ant colony individual, d3 is a distance between a current path taken by the first ant colony individual and a current path taken by the third ant colony individual, and d4 is a distance between a current path taken by the second ant colony individual and a current path taken by the fourth ant colony individual; the third ant colony individual is next to the first ant colony individual, the fourth ant colony individual is next to the second ant colony individual, and the ant colony individuals with the reverse variation are the third ant colony individual and the second ant colony individual.
The reverse variation rule is that in a genetic algorithm, population variation is completed by imitating the process of species evolution, so that the algorithm has random searching capability. According to the idea of genetic algorithm mutation operators, the traditional ant colony model is improved, the circulation process is simplified, the ant colony individuals at s1+1 and s2 are reversed, mutation is realized, certain local searching capability is increased on the basis of global searching capability, and the searching time is shortened.
Where s1 may be understood as the first colony individual described above, s2 may be understood as the second colony individual, and s1+1 is the third colony individual, and s2+1 is the fourth colony individual. Then, when the four ant colony individuals satisfy the variation condition of d1+ d2 < d3+ d4, the positions of the third ant colony individual and the second ant colony individual can be varied a preset number of times.
In some embodiments, the path currently traveled by the first, second, third, and fourth ant colony individuals refers to a path from the route node i to the route node j, and accordingly, a distance between the paths currently traveled by the ant colony individuals may be a distance between starting points of the path, a distance between end points of the path, or a distance between current positions, or a distance between center positions of the path, and the like, which are not limited herein.
During application, for each ant colony individual, the ant colony individual corresponding to the ant colony individual is found, then the inversion variation rule is judged, if the condition of the inversion variation rule is met, the position of the ant colony individual is inverted, and then the path search of the ant colony individual is continued.
Therefore, the above-mentioned inverse mutation rule can be understood as inverse mutation of the ant colony individuals in the course of the path search of the ant colony individuals, so that the ant colony individuals after the inverse mutation can realize faster search.
In some embodiments, the predetermined variation number may be a random variation number or a designated variation number, and is not limited herein.
In addition to the above-mentioned inverse mutation rules, the improved ant colony algorithm also includes some rules necessary for other ant colony algorithms, such as the constraints described in the foregoing embodiments, which constitute the objective functions corresponding to the ant colony algorithm. For these parts, since the embodiments of the present application do not improve on these parts, different application scenarios can be combined, and reference is made to technologies mature in the field, which will not be described in detail herein.
In the embodiment of the application, besides adding the inverse variation rule to the ant colony algorithm, some basic rules of the ant colony algorithm are configured.
Therefore, as an alternative embodiment, the improved ant colony algorithm further includes: heuristic factors, mobile probability selection rules and pheromone update rules.
The heuristic factor may be used in both the mobile probability selection rule and the pheromone update rule. And (3) moving a probability selection rule, adopting a selection strategy combining the determinacy and the randomness, and dynamically adjusting the state transition probability in the path search, namely expressing the probability of the ant colony individual selecting the next node j at the node i.
For the pheromone updating rule, after all the ant colony individuals form paths, the path which enables the total cost to be minimum in each cost limiting condition is judged, the path is found, and the pheromone generated by the ant colony individuals on the ant colony individuals is updated according to the pheromone updating rule.
In the embodiment, the accuracy of the improved ant colony algorithm is improved by configuring the heuristic factors, the mobile probability selection rules and the pheromone updaters.
As an alternative embodiment, the heuristic factor is determined based on the total product quality required by the distributor and the distance of the path taken by the corresponding individual ant colony.
In the model considering carbon emissions, for example, cold chain distribution, the load of a refrigerated vehicle affects fuel consumption. Therefore, with the goal of reducing pollution and ensuring less cost, the ratio of the cargo demand of the distributor to the distance traveled between the nodes should be set as a heuristic factor, taking into account the savings in fuel consumption when the refrigerated vehicle chooses to go to the next route node. Therefore, the conditions of small distance between nodes and large demand are arranged to be preferentially distributed, so that the fuel pollution is reduced, and the distribution cost is reduced.
Thus, in some embodiments, the heuristic factor is a ratio of the total product quality desired by the distributor to the distance traveled by the corresponding individual ant colony. The distance of the path traveled by the corresponding ant colony individual can be understood as the distance between the path node i and the path node j corresponding to the path.
In this embodiment, the heuristic factor is determined based on the total product quality required by the distributor and the distance of the path taken by the corresponding individual ant colony, so that the heuristic factor of the ant colony algorithm is effectively determined.
As an optional implementation manner, the movement probability selection rule is determined based on heuristic factors of paths traveled by the ant colony individuals, pheromone concentrations left by the ant colony individuals on the traveled paths, selectable path node sets of the ant colony individuals, pheromone importance degrees and heuristic factor importance degrees.
The ant colony individual determines the next path node j from the path node i by adopting a probability selection mechanism through pheromone concentration and self-heuristic quantity, and releases certain pheromone in the updating process. At the initial time, the pheromone concentrations on each path are equal
For a fixed value, the transition probability of an individual ant colony moving from a node to a node in its neighborhood may be:
is a heuristic factor on the edge (i, j),
is the unit length trace pheromone concentration left by the ant colony individual h at the side (i, j),
a set of path nodes that can be selected for an individual ant colony after passing through path node i,
to the extent that the pheromone is important,
is the importance of the heuristic factor.
Based on the transition probability, a selection strategy combining determinacy and randomness is adopted, and the state transition probability is dynamically adjusted in the path search, so that the probability that the ant colony individual selects the next node j at the node i can be determined.
In the embodiment, the effective determination of the movement probability selection is realized based on the heuristic factor of the path traveled by the ant colony individuals, the pheromone concentration of the ant colony individuals on the traveled path, the optional path node set of the ant colony individuals, the pheromone importance degree and the heuristic factor importance degree.
As an optional implementation, the pheromone update rule is: updating pheromones based on the total pheromone amount of the target path, the path length of the target path and the pheromone volatilization factors; wherein, the target path is: and under the preset condition of each cost limit, minimizing the total cost.
In this embodiment, after all ant colony individuals form their paths, the path that minimizes the total cost within the cost constraints is determined, and the pheromone generated by the ant colony individuals on the path is updated according to the pheromone update rule after the path is found.
During updating, the pheromone can be updated based on the total pheromone amount of the target path, the path length of the target path and the pheromone volatilization factor, and the specifically adopted rule can be flexibly configured by combining different application scenes, which is not limited herein.
The pheromone volatilization factor can be preset in combination with a specific application scene, and is not limited herein.
In this embodiment, the pheromone is efficiently updated based on the total pheromone amount of the target route, the route length of the target route, and the pheromone volatilization factor.
By introducing the improved ant colony algorithm, in step 120, an algorithm model of the improved ant colony algorithm is constructed based on the rules involved in the improved ant colony algorithm, and then the optimal distribution route is input into the constructed algorithm model, so that the algorithm model can output a corresponding optimization result, that is, the optimized optimal distribution route.
In some embodiments, the improved ant colony algorithm model may be constructed by Matlab software implementation. And moreover, the method can be subjected to simulation application through Matlab software so as to verify the effect of the algorithm.
In some embodiments, the optimization result output by the improved ant colony algorithm may not necessarily be the optimization result meeting the user's requirement. Therefore, after step 120, further optimization may also be performed.
As an optional implementation, the method further comprises: respectively calculating the distribution cost of the optimal distribution route and the optimized optimal distribution route under different dimensions; comparing the distribution cost of the optimal distribution route and the optimized optimal distribution route under different dimensions; if the distribution cost of the optimal distribution route in different dimensions is higher than that of the optimized optimal distribution route, determining the optimized optimal distribution route as a final distribution route; if the distribution cost of the optimal distribution route in at least one dimension is lower than that of the optimized optimal distribution route, updating the inverse variation rule configured in the improved ant colony algorithm, and optimizing the optimal distribution route again based on the improved ant colony algorithm updating the inverse variation rule.
In this embodiment, the distribution cost in different dimensions may have different configurations also in different application scenarios, which is not limited herein.
It can be understood that the distribution costs of the optimal distribution route before optimization and the optimal distribution route after optimization in different dimensions are compared, and if the distribution costs of the optimal distribution route before optimization in different dimensions are all higher than the distribution costs of the optimal distribution route after optimization, it is indicated that the optimal distribution route after optimization has reached a corresponding optimization effect, and at this time, the optimal distribution route after optimization can be directly determined as a final distribution route.
However, if the distribution cost of the optimal distribution route in at least one dimension is lower than the distribution cost of the optimized optimal distribution route, it indicates that this optimization may not be the best optimization, and needs to be optimized again. In order to ensure the optimization effect of the re-optimization, the inverse variation rule configured in the improved ant colony algorithm may be updated first, and then the optimal distribution route is optimized again based on the improved ant colony algorithm updating the inverse variation rule.
In the embodiment, after the optimized optimal distribution route is determined, the distribution costs of the optimal distribution route and the optimized optimal distribution route in different dimensions are compared, and the inverse variation rule is updated based on the distribution costs in different dimensions, so that the optimal distribution route is more accurately optimized.
As an optional implementation, the updating of the configured inverse mutation rule in the improved ant colony algorithm includes: if the distribution cost of the optimal distribution route in one dimension is lower than that of the optimized optimal distribution route, increasing the preset variation times; and if the distribution cost of the optimal distribution route in at least two dimensions is lower than that of the optimized optimal distribution route, updating the ant colony individuals subjected to reverse variation into a first ant colony individual and a fourth ant colony individual when the paths traveled by the ant colony individuals meet a preset reverse variation condition.
In this embodiment, if the delivery cost of the optimal delivery route in one dimension is lower than that of the optimized optimal delivery route, it is still better to show that the improved ant colony algorithm is optimized, and some simple improvements can be made to the optimized ant colony algorithm, for example: increasing the preset variation times and optimizing again.
If the distribution cost of the optimal distribution route in at least two dimensions is lower than the distribution cost of the optimized optimal distribution route, it is indicated that the optimization effect of the improved ant colony algorithm has a larger problem, and the corresponding inverse variation rule can be directly changed, for example: the ant colony individuals with the reversed variation are updated into the first ant colony individual and the fourth ant colony individual, and then optimization is tried again.
It will be understood that some of the improvements described above may be substituted with other improvements. For example: on the premise of better optimization effect, the preset variation times may not be changed, but other rules in the algorithm may be changed, for example: limit cost rules, etc. For another example: on the premise of poor optimization effect, the inverse mutation rule may be changed without changing other probability transition rules, pheromone updating rules, or other cost constraints, and the like, which is not limited herein.
In other embodiments, different improvements may be combined, that is, an improvement is randomly adopted, and if the optimization effect reaches the expected optimization effect after the improvement, the improvement is not needed. If the optimization effect still does not reach the expected optimization effect after improvement, other optimization modes are adopted for optimization, and the like, until the optimization effect reaches the expected optimization effect, the ant colony algorithm is not improved.
In this embodiment, under different conditions, by increasing the preset variation times and changing the ant colony individuals with the reverse variation, the reverse variation rule is effectively updated, so that the updated reverse variation rule can ensure the accuracy of the ant colony search result while ensuring the ant colony search efficiency.
In the foregoing embodiments, it is mentioned that the optimal delivery route may be a delivery route corresponding to a plurality of distributors, that is, the optimal delivery route includes: the distributor respectively reaches a plurality of optimal distribution routes among a plurality of distributors.
Then, in step 120, the finally determined optimized optimal delivery route should also be the optimized optimal delivery route corresponding to each of the plurality of distributors. Namely, the multiple optimal distribution routes are optimized respectively based on the improved ant colony algorithm, and the multiple optimized optimal distribution routes are obtained.
At this time, the method may further include: and merging the optimal distribution routes based on the improved ant colony algorithm to obtain at least one merged optimized optimal distribution route.
In this embodiment, a plurality of optimal distribution routes may be used as an input of the improved ant colony algorithm, and the algorithm model may be integrated based on the plurality of optimal distribution routes to determine an optimal distribution route, where the optimal distribution route is combined with the plurality of optimal distribution routes and the combined optimization effect is also achieved.
Of course, in some embodiments, the multiple optimal delivery routes may not merge perfectly. Therefore, after merging, the merged delivery route can be compared with the cost before merging, and if the cost of the merged delivery route is far greater than the cost before merging, the merging is invalid, and a plurality of optimized optimal delivery routes before merging still remain.
In addition, when multiple optimal distribution routes are merged based on the improved ant colony algorithm, some limiting conditions involved in the ant colony algorithm may be adjusted, for example: cost constraints in different dimensions may need to be considered in combination with the combined cost.
In this embodiment, for the optimized optimal distribution routes respectively corresponding to the multiple distributors, further optimization may be implemented by using an improved ant colony algorithm, so that the finally obtained optimal distribution route meets the optimal requirement.
Through the introduction of the embodiment, compared with the prior art, according to the technical scheme of the embodiment of the application, the traditional ant colony algorithm is improved, the reverse mutation rule is added into the traditional ant colony algorithm, the same times of operation can be realized in a short time when the ant colony is searched through the reverse mutation rule, the time consumed by calculation is reduced, and the mutation operator can improve the performance of a new generation, so that the performance of the whole ant colony is improved. On the basis of the improvement of the performance of the ant colony, the improved ant colony algorithm can not only improve the optimization efficiency of the distribution route, but also ensure the accuracy of the optimized distribution route, and realize the efficient and accurate optimization of the distribution route.
Based on the same inventive concept, as shown in fig. 2, an embodiment of the present application further provides a traditional dealer optimal distribution route optimization system 200 based on the improved ant colony algorithm, including: an optimal delivery route determination module 210 and an optimal delivery route optimization module 220.
An optimal distribution route determining module 210 for determining an optimal distribution route from the dealer to the distributor; an optimal delivery route optimization module 220, configured to optimize the optimal delivery route based on an improved ant colony algorithm; in the improved ant colony algorithm, an inverse variation rule is configured, where the inverse variation rule is: when the path traveled by the ant colony individuals meets a preset inversion variation condition, performing inversion variation on the ant colony individuals for a preset variation time, wherein the preset inversion variation condition is expressed as: d1+ d2 < d3+ d 4; wherein d1 is a distance between a current path traveled by a first ant colony individual and a current path traveled by a second ant colony individual, d2 is a distance between a current path traveled by a third ant colony individual and a current path traveled by a fourth ant colony individual, d3 is a distance between a current path traveled by the first ant colony individual and a current path traveled by the third ant colony individual, and d4 is a distance between a current path traveled by the second ant colony individual and a current path traveled by the fourth ant colony individual; the third ant colony individual is next to the first ant colony individual, the fourth ant colony individual is next to the second ant colony individual, and the reverse variation ant colony individuals are the third ant colony individual and the second ant colony individual.
In one or more embodiments of the present application, the improved ant colony algorithm includes: heuristic factors, mobile probability selection rules and pheromone update rules.
In one or more embodiments of the present application, the heuristic factor is determined based on the total product quality required by the distributor and the distance of the path taken by the corresponding individual ant colony.
In one or more embodiments of the present application, the movement probability selection rule is determined based on heuristic factors of paths traveled by the ant colony individuals, pheromone concentrations left by the ant colony individuals on the traveled paths, selectable path node sets of the ant colony individuals, pheromone importance degrees and heuristic factor importance degrees.
In one or more embodiments of the present application, the pheromone update rule is: updating pheromones based on the total pheromone amount of the target path, the path length of the target path and pheromone volatilization factors; wherein the target path is: and under the preset condition of each cost limit, minimizing the total cost.
In one or more embodiments of the present application, the optimal delivery route optimization module 220 is further configured to: respectively calculating the distribution cost of the optimal distribution route and the optimized optimal distribution route under different dimensions; comparing the distribution cost of the optimal distribution route and the optimized optimal distribution route under different dimensions; if the distribution cost of the optimal distribution route in different dimensions is higher than that of the optimized optimal distribution route, determining the optimized optimal distribution route as a final distribution route; if the distribution cost of the optimal distribution route in at least one dimension is lower than that of the optimized optimal distribution route, updating the inverse variation rule configured in the improved ant colony algorithm, and optimizing the optimal distribution route again based on the improved ant colony algorithm updating the inverse variation rule.
In one or more embodiments of the present application, the optimal delivery route optimization module 220 updates the inverse mutation rule configured in the improved ant colony algorithm, including: if the distribution cost of the optimal distribution route in one dimension is lower than that of the optimized optimal distribution route, increasing the preset variation times; if the distribution cost of the optimal distribution route in at least two dimensions is lower than that of the optimized optimal distribution route, when the path traveled by the ant colony individuals meets a preset inversion variation condition, the ant colony individuals subjected to inversion variation are updated to the first ant colony individuals and the fourth ant colony individuals.
In one or more embodiments of the present application, the optimal delivery route determining module 210 is specifically configured to: determining a plurality of optimal distribution routes from the distributor to the distributor through a plurality of preset distribution route determination algorithms; respectively calculating the distribution cost of the optimal distribution routes in different dimensions, and determining the total distribution cost corresponding to the optimal distribution routes based on the distribution cost in different dimensions; and determining the optimal distribution route with the minimum total distribution cost as the final optimal distribution route.
In one or more embodiments of the present application, the optimal delivery route includes: a plurality of optimal distribution routes from the distributor to a plurality of distributors respectively; the optimal distribution route optimization module 220 is specifically configured to optimize the multiple optimal distribution routes based on an improved ant colony algorithm, respectively, to obtain multiple optimized optimal distribution routes; the optimal delivery route optimization module 220 is further configured to: and merging the optimal distribution routes based on the improved ant colony algorithm to obtain at least one merged optimized optimal distribution route.
The system 200 for optimizing the optimal distribution route of the traditional dealer based on the improved ant colony algorithm corresponds to the method for optimizing the optimal distribution route of the traditional dealer based on the improved ant colony algorithm, and each functional module corresponds to each step of the method, so that the implementation of each functional module refers to the implementation of each step of the method, and is not described repeatedly herein.
Referring to fig. 3, an embodiment of the present application further provides an electronic device 300, including: processor 310 and memory 320, processor 310 and memory 320 being communicatively coupled. The electronic device 300 may be an executive body of the aforementioned conventional dealer optimal distribution route optimization method based on the improved ant colony algorithm.
The memory 320 stores instructions executable by the processor 310, and the instructions are executed by the processor 310, so that the processor 310 can execute the optimized distribution route of the traditional dealer based on the improved ant colony algorithm in the foregoing embodiment.
In some embodiments, the processor 310 and the memory 320 are communicatively coupled via a communication bus.
It is understood that the electronic device 300 may further include more required general modules, which are not described in the embodiments of the present application.
As will be appreciated by one skilled in the art, embodiments of the present application may be provided as a method, system, or computer program product. Accordingly, the present application may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present application may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein.
The present application is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the application. It will be understood that each flow and/or block of the flow diagrams and/or block diagrams, and combinations of flows and/or blocks in the flow diagrams and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
The foregoing descriptions of specific exemplary embodiments of the present application have been presented for purposes of illustration and description. It is not intended to limit the application to the precise form disclosed, and obviously many modifications and variations are possible in light of the above teaching. The exemplary embodiments were chosen and described in order to explain certain principles of the present application and its practical application to enable one skilled in the art to make and use various exemplary embodiments of the present application and various alternatives and modifications thereof. It is intended that the scope of the application be defined by the claims and their equivalents.
Claims (9)
1. A traditional dealer optimal delivery route optimization system based on an improved ant colony algorithm is characterized by comprising:
the optimal distribution route determining module is used for determining an optimal distribution route from a distributor to a distributor;
the optimal distribution route optimization module is used for optimizing the optimal distribution route based on an improved ant colony algorithm;
in the improved ant colony algorithm, an inverse variation rule is configured, where the inverse variation rule is: when the path traveled by the ant colony individuals meets a preset inversion variation condition, performing inversion variation on the ant colony individuals for a preset variation time, wherein the preset inversion variation condition is expressed as: d1+ d2 < d3+ d 4;
wherein d1 is a distance between a current path traveled by a first ant colony individual and a current path traveled by a second ant colony individual, d2 is a distance between a current path traveled by a third ant colony individual and a current path traveled by a fourth ant colony individual, d3 is a distance between a current path traveled by the first ant colony individual and a current path traveled by the third ant colony individual, and d4 is a distance between a current path traveled by the second ant colony individual and a current path traveled by the fourth ant colony individual;
the third ant colony individual is next to the first ant colony individual, the fourth ant colony individual is next to the second ant colony individual, and the ant colony individuals with reversed variation are the third ant colony individual and the second ant colony individual.
2. The system of claim 1, wherein the improved ant colony algorithm comprises: heuristic factors, mobile probability selection rules, and pheromone update rules.
3. The system of claim 2, wherein the heuristic factors are determined based on a total product quality required by the distributor and a distance of a path taken by the corresponding individual ant colony.
4. The improved ant colony algorithm-based optimal distribution route optimization system for traditional dealers according to claim 2, wherein the movement probability selection rule is determined based on heuristic factors of paths traveled by the individual ant colonies, pheromone concentrations left by the individual ant colonies on the traveled paths, selectable path node sets of the individual ant colonies, pheromone importance degrees and heuristic factor importance degrees.
5. The improved ant colony algorithm-based traditional dealer optimal delivery route optimization system of claim 2, wherein the pheromone update rule is:
updating pheromones based on the total pheromone amount of the target path, the path length of the target path and pheromone volatilization factors; wherein the target path is: and under a plurality of preset cost limiting conditions, minimizing the total cost.
6. The improved ant colony algorithm-based traditional dealer optimal delivery route optimization system of claim 1, wherein the optimal delivery route optimization module is further configured to:
respectively calculating the distribution cost of the optimal distribution route and the optimized optimal distribution route under different dimensions;
comparing the distribution cost of the optimal distribution route and the optimized optimal distribution route under different dimensions;
if the distribution cost of the optimal distribution route under different dimensions is higher than that of the optimized optimal distribution route, determining the optimized optimal distribution route as a final distribution route;
if the distribution cost of the optimal distribution route in at least one dimension is lower than the distribution cost of the optimized optimal distribution route, updating the inverse variation rule configured in the improved ant colony algorithm, and optimizing the optimal distribution route again based on the improved ant colony algorithm updating the inverse variation rule.
7. The improved ant colony algorithm-based traditional dealer optimal delivery route optimization system of claim 6, wherein the optimal delivery route optimization module updates an inverse variation rule configured in the improved ant colony algorithm, and comprises:
if the distribution cost of the optimal distribution route in one dimension is lower than that of the optimized optimal distribution route, increasing the preset variation times;
if the distribution cost of the optimal distribution route in at least two dimensions is lower than that of the optimized optimal distribution route, when the path traveled by the ant colony individuals meets a preset inversion variation condition, the ant colony individuals subjected to inversion variation are updated to the first ant colony individuals and the fourth ant colony individuals.
8. The improved ant colony algorithm-based traditional dealer optimal delivery route optimization system of claim 1, wherein the optimal delivery route determination module is specifically configured to:
determining a plurality of optimal distribution routes from a distributor to a distributor through a plurality of preset distribution route determination algorithms, wherein each distribution route determination algorithm corresponds to at least one optimal distribution route;
respectively calculating the distribution cost of the optimal distribution routes in different dimensions, and determining the total distribution cost corresponding to the optimal distribution routes based on the distribution cost in different dimensions;
and determining the optimal distribution route with the minimum total distribution cost as a final optimal distribution route.
9. The improved ant colony algorithm-based traditional dealer optimal delivery route optimization system of claim 1, wherein the optimal delivery route comprises: a plurality of optimal distribution routes from the distributor to a plurality of distributors respectively;
the optimal distribution route optimization module is specifically used for optimizing the optimal distribution routes respectively based on an improved ant colony algorithm to obtain multiple optimized distribution routes;
the optimal delivery route optimization module is further to: and merging the optimal distribution routes based on the improved ant colony algorithm to obtain at least one merged optimized optimal distribution route.
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