CN108268959A - Logistics distribution paths planning method based on primary and secondary population ant group algorithm - Google Patents
Logistics distribution paths planning method based on primary and secondary population ant group algorithm Download PDFInfo
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- CN108268959A CN108268959A CN201611254003.3A CN201611254003A CN108268959A CN 108268959 A CN108268959 A CN 108268959A CN 201611254003 A CN201611254003 A CN 201611254003A CN 108268959 A CN108268959 A CN 108268959A
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Abstract
The present invention provides a kind of logistics distribution paths planning methods based on primary and secondary population ant group algorithm, and for the ant group algorithm based on primary and secondary population, passage path goes Crossover Strategy, obtains optimal path.The beneficial effects of the present invention are, the logistics distribution paths planning method of the present invention, overcome optimal worst ant group algorithm and minimax ant group algorithm there are the drawbacks of, passage path Crossover Strategy saves strategy, the time calculated is effectively saved, the search efficiency of optimal path is improved, has obtained more optimal result.
Description
Technical field
The present invention relates to a kind of logistics distribution paths planning methods more particularly to a kind of based on primary and secondary population ant group algorithm
Logistics distribution paths planning method.
Background technology
With the fast development of global economy, there has also been broader spaces for development of the China in terms of material flow industry.Make
Modern logistics for " third party's profit source " are mainly responsible for commodity storage, transport and dispatching, are the knobs between enterprise and client
Band.Dispatching is the core link of logistics distribution process, it is the logistics activity of the part in the range of certain economical rationality.Enterprise
Staff classifies to it, is encapsulated and product is sent to client with most fast speed according to the scheduled different articles of customer
In hand.Whether dispatching can be completed, to enterprise in the eyes ofly in customer as a kind of customer and the intermediary of enterprise with high level
Position is extremely important.Therefore, in modern distribution system, home-delivery center formulates one rationally effectively according to the requirement for rationalizing dispatching
Goods handling circuit, select time saving and energy saving circulation means of transportation, do one's best and meet the needs of customer is to dispatching.It is this
Optimization in relation to distribution vehicle path will generate the transportation cost of entire logistics system particularly important influence.
Current paths planning method is by analyzing theory basis knowledge, using the side of quantification and qualification
Method, and more powerful calculating analysis software is utilized, based on ant group algorithm, according to customer in dispatching transportational process in reality
Requirement, have studied basic logistics vehicles Distribution path problem.But although this method can improve the speed for seeking optimal combination
And accuracy rate, but its search process can will appear deviation, convergence rate there are certain blindness, searching route and fall into slowly and easily
The defects of entering locally optimal solution, and the unstability demand under various burst conditions is not adapted to, there is an urgent need for further further investigateds.
In view of drawbacks described above, creator of the present invention obtains the present invention finally by prolonged research and practice.
Invention content
To solve the above problems, the technical solution adopted by the present invention is, provide a kind of based on primary and secondary population ant group algorithm
Logistics distribution paths planning method, include the following steps：
S1：Setup parameter initial value：Snum is the quantity of time population；Mnum is the number of all ants inside time population；τ
Max is the initial value of the pheromones table of main population and the secondary population, and the size of taboo list Tabu is snum*mnum, and L is path
Distance；Simultaneously initiation parameter pheromones heuristic factor ρ, heuristic factor α, apart from heuristic factor β and iterations NC；
S2：Cycle-index is counted with NC=NC+1；
S3：Time population is counted with little_sub=little_sbu+1；
S4：The path of the secondary population little_sub is recorded with little_Tabu, and is mnum inside the secondary population
Individual sets arbitrary city as starting point；
S5：Ant individual is counted with ant=ant+1；
S6：The information table that ant ant begins stepping through foundation is the little_sub population, and each ant is from cargo centre
It sets out, next city is selected according to transition probability, if meeting the capacity limit of vehicle, which is pulled in into this road
Diameter, otherwise, vehicle return to origin open up another paths again, until completing all service shops, complete primary traversal, s is production
Raw path；
S7：After carrying out processing accordingly to the path s of generation with 2opt, it is recorded in the table little_
In Tabu；
S8：Judge whether ant is equal to mnum, if equal, continue algorithm, if unequal, from the step S5：Start to continue
Algorithm；
S9：When the little_sub is fully recorded it is full after, synchronize to the taboo list Tabu and the path away from
It is updated from L；
S10：Judge whether the little_sbu is equal to the snum, if equal, continue algorithm, if unequal, from institute
Step S3 is stated to start to continue algorithm；
S11：The pheromones of the main population and time population are updated, if iterations NC is more than maximum iteration
NCmax, then optimizing terminates, and exports optimal path, otherwise continues algorithm since the step S2.
Further, in the step S11, the more new formula of main species information element is as follows：
τij(t+1)=(1- ρ) τij(t)+Δτij(t)
Wherein, Δ τij(t) when representing that the time is t, the increment of main species information element, Q represents constant, and ρ represents that pheromones increase
Coefficient of discharge, snum represent the size of time population quantity,Represent the path length that ant optimal in time population k is passed by.
Further, in the step S11, the more new formula of secondary species information element is as follows：
Wherein,When representing that the time is t, the pheromone concentration in k-th population on side (i, j)；It represents
When time is t, the pheromones increment in k-th population on side (i, j), mnum represents the quantity of ant inside time population；
It is to represent k-th population inside, the mnum ant paths traversed length when the time is t；Represent that the time is
During t, other secondary populations are shared with the pheromones increment of k-th population；Snum represents time population total amount；Represent the time
During for t, the pheromones value on secondary population m inner edges (i, j)；ε is the coefficient of a contribution amount, refers to the tribute of other secondary populations
It offers, value could be provided as ε=1/ (2 × (snum-1))；When representing that the time is t, main population is supplied to time population
Pheromones amount.
Compared with the prior art the beneficial effects of the present invention are：The logistics distribution paths planning method of the present invention, overcomes
Optimal worst ant group algorithm and minimax ant group algorithm there are the drawbacks of, passage path Crossover Strategy or save strategy,
The time calculated has effectively been saved, the search efficiency of optimal path has been improved, has obtained more optimal result.
Description of the drawings
Fig. 1 is the functional block diagram of the logistics distribution paths planning method the present invention is based on primary and secondary population ant group algorithm.
Specific embodiment
Below in conjunction with attached drawing, the forgoing and additional technical features and advantages are described in more detail.
Referring to Fig. 1, it is the function of the logistics distribution paths planning method the present invention is based on primary and secondary population ant group algorithm
Block diagram.
As shown in Figure 1, its paths planning method, includes the following steps：
S1：Setup parameter initial value：Snum is the quantity of time population；Mnum is the number of all ants inside time population；τ
Max is the initial value of the pheromones table of main population and the secondary population, and the size of taboo list Tabu is snum*mnum, and L is path
Distance；Simultaneously initiation parameter pheromones heuristic factor ρ, heuristic factor α, apart from heuristic factor β and iterations NC；
S2：Cycle-index is counted with NC=NC+1；
S3：Time population is counted with little_sub=little_sbu+1；
S4：The path of the secondary population little_sub is recorded with little_Tabu, and is mnum inside the secondary population
Individual sets arbitrary city as starting point；
S5：Ant individual is counted with ant=ant+1；
S6：The information table that ant ant begins stepping through foundation is the little_sub population, and each ant is from cargo centre
It sets out, next city is selected according to transition probability, if meeting the capacity limit of vehicle, which is pulled in into this road
Diameter, otherwise, vehicle return to origin open up another paths again, until completing all service shops, complete primary traversal, s is production
Raw path；
S7：After carrying out processing accordingly to the path s of generation with 2opt, it is recorded in the table little_
In Tabu；
S8：Judge whether ant is equal to mnum, if equal, continue algorithm, if unequal, from the step S5：Start to continue
Algorithm；
S9：When the little_sub is fully recorded it is full after, synchronize to the taboo list Tabu and the path away from
It is updated from L；
S10：Judge whether the little_sbu is equal to the snum, if equal, continue algorithm, if unequal, from institute
Step S3 is stated to start to continue algorithm；
S11：The pheromones of the main population and time population are updated, if iterations NC is more than maximum iteration
NCmax, then optimizing terminates, and exports optimal path, otherwise continues algorithm since the step S2.
In the step S11, the more new formula of main species information element is as follows：
τij(t+1)=(1- ρ) τij(t)+Δτij(t)
Wherein, Δ τij(t) when representing that the time is t, the increment of main species information element, Q represents constant, and ρ represents that pheromones increase
Coefficient of discharge, snum represent the size of time population quantity,Represent the path length that ant optimal in time population k is passed by.
In the step S11, the more new formula of secondary species information element is as follows：
Wherein,When representing that the time is t, the pheromone concentration in k-th population on side (i, j)；It represents
When time is t, the pheromones increment in k-th population on side (i, j), mnum represents the quantity of ant inside time population；
It is to represent k-th population inside, the mnum ant paths traversed length when the time is t；Represent that the time is
During t, other secondary populations are shared with the pheromones increment of k-th population；Snum represents time population total amount；Represent the time
During for t, the pheromones value on secondary population m inner edges (i, j)；ε is the coefficient of a contribution amount, refers to the tribute of other secondary populations
It offers, value could be provided as ε=1/ (2 × (snum-1))；When representing that the time is t, main population is supplied to time population
Pheromones amount.
The logistics distribution paths planning method based on primary and secondary population ant group algorithm of the present invention, overcomes optimal worst ant colony
Algorithm and minimax ant group algorithm there are the drawbacks of, passage path Crossover Strategy or save strategy, effectively saved meter
The time of calculation improves the search efficiency of optimal path, has obtained more optimal result.
The above is only the preferred embodiment of the present invention, it is noted that for the ordinary skill people of the art
Member, under the premise of the method for the present invention is not departed from, can also make several improvement and supplement, these are improved and supplement also should be regarded as
Protection scope of the present invention.
Claims (3)
1. the logistics distribution paths planning method based on primary and secondary population ant group algorithm, which is characterized in that include the following steps：
S1：Setup parameter initial value：Snum is the quantity of time population；Mnum is the number of all ants inside time population；τmax
For main population and the initial value of the pheromones table of the secondary population, the size of taboo list Tabu is snum*mnum, L for path away from
From；Simultaneously initiation parameter pheromones heuristic factor ρ, heuristic factor α, apart from heuristic factor β and iterations NC；
S2：Cycle-index is counted with NC=NC+1；
S3：Time population is counted with little_sub=little_sbu+1；
S4：The path of the secondary population little_sub is recorded with little_Tabu, and is mnum individuals inside the secondary population
Arbitrary city is set as starting point；
S5：Ant individual is counted with ant=ant+1；
S6：The information table that ant ant begins stepping through foundation is the little_sub population, and each ant goes out from cargo centre
Hair selects next city according to transition probability, if meeting the capacity limit of vehicle, which is pulled in the paths,
Otherwise, vehicle return to origin opens up another paths again, until completing all service shops, completes primary traversal, s is generates
Path；
S7：After carrying out processing accordingly to the path s of generation with 2opt, it is recorded in the table little_Tabu；
S8：Judge whether ant is equal to mnum, if equal, continue algorithm, if unequal, from the step S5：Start to continue to calculate
Method；
S9：When the little_sub is fully recorded it is full after, synchronize to the taboo list Tabu and path distance L into
Row update；
S10：Judge whether the little_sbu is equal to the snum, if equal, continue algorithm, if unequal, from the step
Rapid S3 starts to continue algorithm；
S11：The pheromones of the main population and time population are updated, if iterations NC is more than maximum iteration
NCmax, then optimizing terminates, and exports optimal path, otherwise continues algorithm since the step S2.
2. the logistics distribution paths planning method according to claim 1 based on primary and secondary population ant group algorithm, feature exist
In,
In the step S11, the more new formula of main species information element is as follows：
τij(t+1)=(1- ρ) τij(t)+Δτij(t)
Wherein, Δ τij(t) when representing that the time is t, the increment of main species information element, Q represents constant, and ρ represents pheromones increment system
Number, snum represent the size of time population quantity,Represent the path length that ant optimal in time population k is passed by.
3. the logistics distribution paths planning method according to claim 1 based on primary and secondary population ant group algorithm, feature exist
In,
In the step S11, the more new formula of secondary species information element is as follows：
Wherein,When representing that the time is t, the pheromone concentration in k-th population on side (i, j)；The expression time is t
When, the pheromones increment in k-th population on side (i, j), mnum represents the quantity of ant inside time population；It is when representing
Between the mnum ant paths traversed length inside k-th population when being t；When representing that the time is t, other times
Population is shared with the pheromones increment of k-th population；Snum represents time population total amount；When representing that the time is t, secondary population
Pheromones value on m inner edges (i, j)；ε is the coefficient of a contribution amount, refers to the contribution of other secondary populations, value can be set
It is set to ε=1/ (2 × (snum-1))；When representing that the time is t, main population is supplied to the pheromones amount of time population.
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Cited By (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN109359760A (en) * | 2018-08-10 | 2019-02-19 | 中国电子科技集团公司电子科学研究院 | A kind of logistics route optimization method, device and server |
| CN109919396A (en) * | 2019-04-01 | 2019-06-21 | 南京邮电大学 | A kind of route planning method of Logistics Oriented dispatching |
| CN110245776A (en) * | 2019-04-26 | 2019-09-17 | 惠州学院 | A kind of intelligent transportation paths planning method based on more attribute ant group algorithms |
| WO2021135208A1 (en) * | 2019-12-31 | 2021-07-08 | 苏宁云计算有限公司 | Delivery path planning method and system taking order aggregation degree into consideration |
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Cited By (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN109359760A (en) * | 2018-08-10 | 2019-02-19 | 中国电子科技集团公司电子科学研究院 | A kind of logistics route optimization method, device and server |
| CN109359760B (en) * | 2018-08-10 | 2022-08-16 | 中国电子科技集团公司电子科学研究院 | Logistics path optimization method and device and server |
| CN109919396A (en) * | 2019-04-01 | 2019-06-21 | 南京邮电大学 | A kind of route planning method of Logistics Oriented dispatching |
| CN109919396B (en) * | 2019-04-01 | 2022-07-26 | 南京邮电大学 | Route planning method for logistics distribution |
| CN110245776A (en) * | 2019-04-26 | 2019-09-17 | 惠州学院 | A kind of intelligent transportation paths planning method based on more attribute ant group algorithms |
| CN110245776B (en) * | 2019-04-26 | 2023-08-01 | 惠州学院 | Intelligent traffic path planning method based on multi-attribute ant colony algorithm |
| WO2021135208A1 (en) * | 2019-12-31 | 2021-07-08 | 苏宁云计算有限公司 | Delivery path planning method and system taking order aggregation degree into consideration |
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Application publication date: 20180710 |