CN103294823B - Rail transit multi-mode optimal transit transfer inquiring method based on cultural ant colony - Google Patents
Rail transit multi-mode optimal transit transfer inquiring method based on cultural ant colony Download PDFInfo
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Abstract
The invention relates to a rail transit multi-mode optimal transit transfer inquiring method based on a cultural ant colony. The method includes the following steps: (1) a central processing unit receives a query request through a touch screen, obtains website information from a database according to the query request and builds a route selecting model; (2) the central processing unit operates a cultural ant colony system on the basis of the route selecting model, calculates the optimal rail transit transfer scheme under different optimal objects, and outputs the best route; (3) values of the route selecting model are updated, whether optimization is finished is judged, if the answer is positive, a calculation result is fed back to the touch screen, and a step (4) is operate, and if the answer is negative, the step (2) is returned; (4) the touch screen displays the calculation result. Compared with the prior art, the method improves flexibility and efficiency of resident traveling for the rail transit transfer due to the fact that the culture ant colony system rapidly and precisely calculates the optimal rail transit transfer schemes under different optimal objects.
Description
Technical field
The present invention relates to a kind of optimum transfer computational methods of track traffic, more particularly, to a kind of cultural ant colony is based on
Rail transit multi-mode optimal transit transfer querying method.
Background technology
City Rail Transit System is to contact one of link the most close with city dweller's daily life, or even certain
The life style of city dweller is decide in degree, thus, at present the electronic chart product in numerous cities is all handed over track is realized
Open network optimal path inquiry is to enabling electronic chart preferably to meet the demand of user but existing as its most important thing
Some inquiry systems not only easily malfunction, and inefficiency, while multi-mode transfer can not be carried out：
On the one hand, most software developer thinks that Rail traffic network optimum route analysis are with other network analysis one
Sample, should also be based on most short, but the optimum of user is not only shortest path, so it requires also have very from user
Big gap；
On the other hand, most users think that least bus change is only key issue.Least bus change and shortest path seem system
One, but actually this is not so, therefore how to accomplish both unifications, the optimization Transfer Model and algorithm for proposing practicable has become
Problem in the urgent need to address.
The content of the invention
The purpose of the present invention is exactly the defect in order to overcome above-mentioned prior art to exist and provides that a kind of calculating speed is fast, essence
The high rail transit multi-mode optimal transit transfer querying method based on cultural ant colony of degree, improves resident trip and track is handed over
The flexibility of logical transfer and high efficiency.
The purpose of the present invention can be achieved through the following technical solutions：
A kind of rail transit multi-mode optimal transit transfer querying method based on cultural ant colony, the method includes following step
Suddenly：
1) central processing unit receives inquiry request by touch-screen, and website letter is obtained from database according to inquiry request
Breath, build path preference pattern；
2) central processing unit performs cultural ant colony on multiple populations based on path Choice Model, calculates and obtains different optimum mesh
Optimal trajectory traffic transfer scheme under mark, exports optimal path；
3) numerical value of path Choice Model is updated, and judges whether optimization terminates, it is if so, then that result of calculation is anti-
Feed touch-screen, execution step 4), if it is not, return to step 2)；
4) touch-screen shows result of calculation.
Described inquiry request includes start site and final website.
Described optimal objective includes that the time is most short, transfer is minimum and distance is minimum.
Described cultural ant colony includes the ant colony evolutionary process of group space and the renewal of knowledge process of belief space,
The ant colony evolutionary process of described group space is comprised the following steps：
A1 the pheromones distribution of group space) is initialized, and group space is divided into into multiple subgroups, each subgroup is adopted respectively
Parallel evolutionary is carried out with the Ant ColonySystem of different behaviors, the locally optimal solution of each subgroup is obtained；
A2) respective local information element is updated according to the information interactive strategy based on study mechanism between each subgroup；
A3) globally optimal solution is updated according to the locally optimal solution of each subgroup, and by it by receiving function storage to faith
Space；
A4) global information element renewal is carried out according to the output of belief space；
A5) judge whether to meet algorithm end condition, if meeting, algorithm terminates；Otherwise, a2 is gone to step)；
The renewal of knowledge process of described belief space is comprised the following steps：
B1) belief space is initialized；
B2) the current globally optimal solution that group space is provided is received by receiver function；
B3) implement 2-OPT operations to belief space, optimize belief space；
B4) optimal solution is exported, and step a4 is provided it to by influence function).
Described each subgroup is respectively adopted the Ant ColonySystem of different behaviors and carries out parallel evolutionary and is specially：
A101) on the website that each subgroup is randomly placed at m ant of varying number in n website；
A102) each subgroup carries out state transfer according to respective behavior, next node is selected, while carrying out local letter
Breath element updates, described behavior include it is random, comform, greedy or mixing；
A103) repeat step a102), until every ant is respectively formed a fullpath, i.e., each subgroup travels through respectively institute
There is node, obtain respective locally optimal solution.
Described is based on the information interactive strategy of study mechanism：
Each subgroup carries out information exchange with other neighbour two subgroups, by current locally optimal solution and neighbour its
He is compared the locally optimal solution of two subgroups, and the local information element for updating itself with more excellent locally optimal solution.
Described function Accept () that receives is：
Accept ()=T
T is the constant of setting.
Described 2-OPT operations of implementing to belief space are specially：
B301) r is set0For a given constant in [0,1], random number r of [0, a 1] scope is produced, if r
＞ r0Then go to step b4)；
B302) if there is node c in current optimal pathi、cj, wherein j >=i+2, and
d(ci, ci+1)+d(cj, cj+1) ＞ d (ci, cj)+d(ci+1, cj+1)
So by side (ci, cj)、(ci+1, cj+1) replace (ci, ci+1)、(cj, cj+1), the path after exchange in circuit
(cj..., ci+1) be reversed；Otherwise go to step b4).
Described influence function Influence () is：
Wherein, EndStep is Ant ColonySystem set in advance maximum evolution algebraically, and CurrentStep is that ant colony evolution is worked as
Front algebraically, BaseNum and C are constant.
Compared with prior art, the present invention has advantages below：
1st, the present invention carries out optimal path solution using cultural ant colony, and cultural ant colony is a kind of by Ant ColonySystem
The new effectively optimizing method of Cultural Algorithm framework is included, the computation model is comprising the group space based on Ant ColonySystem and is based on
The belief space of current optimal solution, two spaces have respective colony and independent parallel develops, improve Algorithm for Solving speed and
Precision；
2nd, the Ant ColonySystem that the present invention is developed using multigroup parallel, and by the information based on study mechanism between each subgroup
Interactive strategy is interacted, and improves the precision of algorithm；
3rd, the belief space of cultural ant colony of the present invention adopts random 2-OPT swap operations, and to optimal solution row variation is entered
Optimization, the solution Jing after developing is individual to be used for updating group space global information element, and the evolutionary process of group space is instructed in help,
So as to reach the diversity for improving population, precocity is prevented, reduce the purpose of calculation cost；
4th, the inventive method has more preferable accuracy and robustness, even for extensive problem, also can be with less
Population invariable number and shorter run time try to achieve the less satisfactory solution of relative error.
In a word, the beneficial effects of the present invention is：The present invention has devised and embodied a kind of new intelligence computation method, energy
It is enough that efficient optimization design is carried out to the transfer of different resident's multi-modes, resident trip is improve to the flexible of orbit traffic transfer
Property, high efficiency.The present invention has adapted to the tomorrow requirement of Traffic Development, sustainable growing to scale and complexity
Orbit traffic transfer carries out the management of science.
Description of the drawings
Fig. 1 is the schematic flow sheet of the present invention；
Fig. 2 is the system framework figure of cultural ant colony on multiple populations of the invention；
Fig. 3 is the block schematic illustration that multigroup parallel of the present invention develops；
Fig. 4 is the principle schematic of cultural ant colony of the present invention.
Specific embodiment
Below in conjunction with the accompanying drawings the present invention is described in detail with specific embodiment.The present embodiment is with technical solution of the present invention
Premised on implemented, give detailed embodiment and specific operating process, but protection scope of the present invention is not limited to
Following embodiments.
As shown in figure 1, a kind of rail transit multi-mode optimal transit transfer querying method based on cultural ant colony, the method
Comprise the following steps：
1) central processing unit receives inquiry request, including start site and final website by touch-screen, and according to inquiry
Request obtains site information, build path preference pattern from database；
2) central processing unit performs cultural ant colony on multiple populations based on path Choice Model, calculates and obtains different optimum mesh
Optimal trajectory traffic transfer scheme under mark (including the time is most short, transfer is minimum and distance is minimum etc.), exports optimal path；
3) numerical value of path Choice Model is updated, and judges whether optimization terminates, it is if so, then that result of calculation is anti-
Feed touch-screen, execution step 4), if it is not, return to step 2)；
4) touch-screen shows result of calculation, shows path Choice Model, represent content including orbit traffic transfer information and
Line map.
As shown in figs 2-4, described cultural ant colony includes the ant colony evolutionary process and belief space of group space
Renewal of knowledge process, the ant colony evolutionary process of described group space comprises the following steps：
A1 the pheromones distribution of group space) is initialized, and group space is divided into into multiple subgroups (in Fig. 2, Fig. 3
Subgroup 1, subgroup 2, subgroup 3, subgroup 4), m ant of varying number is randomly placed at one in n website for each subgroup
On website, state transfer is carried out according to respective behavior, select next node, while local information element renewal is carried out, institute
The behavior stated include it is random, comform, greedy or mixing；Until every ant is respectively formed a fullpath, i.e., each subgroup
All nodes are traveled through respectively, obtain respective locally optimal solution.
What each subgroup was developed concretely comprises the following steps：
A101) initialize：T=0, Nc=0, τij(t)=τ0, Δ τijT ()=0, by m ant n station is randomly placed at
Point on；
A102 taboo table index s=1) is put, and its starting point website is added in respective taboo list, judge taboo list whether
It is full, if so, then execution step a104), if it is not, then s=s+1, execution step a103),
A103) the transition probability that each ant is each calculated by itNext website is selected, and the website is added into taboo list
In, while carrying out local information element renewal：
τij=(1- ρ) τij+ρτ0
Wherein：ρ (0 ＜ ρ ＜ 1) is the local volatilization factor of pheromones；τ0It is the initial information element concentration on each paths
Value；
A104) every ant is respectively formed a fullpath, that is, travel through all nodes, and what all ants of calculating were passed by travels round
Length Lk, current optimal solution is updated, obtain locally optimal solution.
A2) respective local information element is updated according to the information interactive strategy based on study mechanism between each subgroup.
As shown in figure 3, each subgroup annularly connects, each subgroup carries out information exchange with other neighbour two subgroups,
Current locally optimal solution is compared with the locally optimal solution of other neighbour two subgroups, and with more excellent local optimum
Solution updates the local information element of itself.
A3) globally optimal solution is updated according to the locally optimal solution of each subgroup, and by it by receiving function storage to faith
Space；
Described function Accept () that receives is：
Accept ()=T
T is the constant of setting, can be set to 20；
A4) global information element renewal is carried out according to the output of belief space：
Wherein：It is the global volatilization factor of pheromones,
LgbRepresent the path (length in the global optimum path from obtained by on-test) of current globally optimal solution；
A5) judge whether to meet algorithm end condition, if meeting, algorithm terminates；Otherwise, all taboo lists are emptied, is turned
Step a103)：
Algorithm end condition is that the maximum evolution algebraically or globally optimal solution for reaching setting is not continuously sent out in setting algebraically
Changing.
The Ant ColonySystem that each subgroup space adopts is that a kind of local updating rule of pheromones and the global rule that updates are entered
Pheromone update on walking along the street footpath, so that the search space of expansion algorithm and algorithm are able to convergence energy organic unity.Such as
Shown in Fig. 3, A represents that each subgroup exchanges optimal solution and updates local information element by regional cooperative, and B represents each subgroup interaction by the overall situation
Optimal solution is supplied to belief space to update global information element.
The renewal of knowledge process of described belief space is comprised the following steps：
B1) belief space is initialized；
B2) the current globally optimal solution that group space is provided is received by receiver function；
B3) implement 2-OPT operations to belief space, optimize belief space；
Described 2-OPT operations of implementing to belief space are specially：
B301) r is set0For a given constant in [0,1], random number r of [0, a 1] scope is produced, if r
＞ r0Then go to step b4)；
B302) if there is node c in current optimal pathi、cj, wherein j >=i+2, and
d(ci, ci+1)+d(cj, cj+1) ＞ d (ci, cj)+d(ci+1, cj+1)
So by side (ci, cj)、(ci+1, cj+1) replace (ci, ci+1)、(cj, cj+1), the path after exchange in circuit
(ci..., ci+1) be reversed；Otherwise go to step b4).
B4) optimal solution is exported, and step a4 is provided it to by influence function).
Described influence function Influence () is：
Wherein, EndStep is Ant ColonySystem set in advance maximum evolution algebraically, and CurrentStep is that ant colony evolution is worked as
Front algebraically, BaseNum and C are constant, are set by the user.Generally BaseNum values are 30, C：EndStep values are 1: 3, this
The starting stage that sample develops in ant colony, the knowledge solution of belief space affects less to it so as to rapid evolution is ensure that, in ant
In the later stage that group develops, knowledge solution affects to be gradually increased on it so as to can more receive the guiding of knowledge space, while expanding
Search space, possesses more preferable ability of searching optimum.
Group space individuality forms during evolution individual experience, is delivered to individual experience by function accept ()
The individual experience for receiving is compared and is optimized by belief space, belief space according to certain rule of conduct, forms optimal solution.
Belief space, using random 2-OPT swap operations, enters row variation excellent to the optimal solution found in evolutionary process to optimal solution
Change, and make full use of random 2-OPT algorithms to be concisely and efficiently feature, complete the variation of itself, the solution individuality Jing after developing is used for
Group space global information element is updated, the evolutionary process of group space is instructed in help, so as to reach the diversity for improving population,
Precocity is prevented, the purpose of calculation cost is reduced.Belief space is after colony's experience is formed by influence function in group space
Individual rule of conduct is modified, so that individual space obtains higher efficiency of evolution.
Claims (7)
1. a kind of rail transit multi-mode optimal transit transfer querying method based on cultural ant colony, it is characterised in that the method
Comprise the following steps：
1) central processing unit receives inquiry request by touch-screen, and site information is obtained from database according to inquiry request,
Build path preference pattern；
2) central processing unit performs cultural ant colony on multiple populations based on path Choice Model, calculates and obtains under different optimal objectives
Optimal trajectory traffic transfer scheme, export optimal path；
3) numerical value of path Choice Model is updated, and judges whether optimization terminates, if so, then feed back to result of calculation
Touch-screen, execution step 4), if it is not, return to step 2)；
4) touch-screen shows result of calculation；
Described cultural ant colony includes the ant colony evolutionary process of group space and the renewal of knowledge process of belief space, described
The ant colony evolutionary process of group space comprise the following steps：
A1 the pheromones distribution of group space) is initialized, and group space is divided into into multiple subgroups, each subgroup is respectively adopted not
Parallel evolutionary is carried out with the Ant ColonySystem of behavior, the locally optimal solution of each subgroup is obtained；
A2) respective local information element is updated according to the information interactive strategy based on study mechanism between each subgroup；
A3) globally optimal solution is updated according to the locally optimal solution of each subgroup, and by it by receiving function storage to belief space；
A4) global information element renewal is carried out according to the output of belief space；
A5) judge whether to meet algorithm end condition, if meeting, algorithm terminates；Otherwise, a2 is gone to step)；
The renewal of knowledge process of described belief space is comprised the following steps：
B1) belief space is initialized；
B2) the current globally optimal solution that group space is provided is received by receiver function；
B3) implement 2-OPT operations to belief space, optimize belief space；
B4) optimal solution is exported, and step a4 is provided it to by influence function)；
Described each subgroup is respectively adopted the Ant ColonySystem of different behaviors and carries out parallel evolutionary and is specially：
A101) on the website that each subgroup is randomly placed at m ant of varying number in n website；
A102) each subgroup carries out state transfer according to respective behavior, next node is selected, while carrying out local information element
Update, described behavior include it is random, comform, greedy or mixing；
A103) repeat step a102), until every ant is respectively formed a fullpath, i.e., each subgroup travels through respectively all sections
Point, obtains respective locally optimal solution.
2. a kind of rail transit multi-mode optimal transit transfer issuer based on cultural ant colony according to claim 1
Method, it is characterised in that described inquiry request includes start site and final website.
3. a kind of rail transit multi-mode optimal transit transfer issuer based on cultural ant colony according to claim 1
Method, it is characterised in that described optimal objective includes that the time is most short, transfer is minimum and distance is minimum.
4. a kind of rail transit multi-mode optimal transit transfer issuer based on cultural ant colony according to claim 1
Method, it is characterised in that described is based on the information interactive strategy of study mechanism：
Each subgroup carries out information exchange with other neighbour two subgroups, by current locally optimal solution and neighbour other two
The locally optimal solution of individual subgroup is compared, and the local information element for updating itself with more excellent locally optimal solution.
5. a kind of rail transit multi-mode optimal transit transfer issuer based on cultural ant colony according to claim 1
Method, it is characterised in that described function Accept () that receives is：
Accept ()=T
T is the constant of setting.
6. a kind of rail transit multi-mode optimal transit transfer issuer based on cultural ant colony according to claim 1
Method, it is characterised in that described 2-OPT operations of implementing to belief space are specially：
B301) r is set0For a given constant in [0,1], random number r of [0, a 1] scope is produced, if r>r0Then
Go to step b4)；
B302) if there is node c in current optimal pathi、cj, wherein j >=i+2, and
d(ci,ci+1)+d(cj,cj+1)>d(ci,cj)+d(ci+1,cj+1)
So by side (ci,cj)、(ci+1,cj+1) replace (ci,ci+1)、(cj,cj+1), the path (c after exchange in circuitj,…,
ci+1) be reversed；Otherwise go to step b4).
7. a kind of rail transit multi-mode optimal transit transfer issuer based on cultural ant colony according to claim 1
Method, it is characterised in that described influence function Influence () is：
Wherein, EndStep is Ant ColonySystem set in advance maximum evolution algebraically, and CurrentStep is that former generation is worked as in ant colony evolution
Number, BaseNum and C is constant.
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| CN103648139B (en) * | 2013-12-09 | 2017-06-20 | 天津工业大学 | Wireless sensor network node deployment method for designing based on cultural ant group algorithm |
| CN106775944A (en) * | 2016-12-12 | 2017-05-31 | 天津工业大学 | The method integrated based on cultural multi-ant colony algorithm virtual machine under cloud platform |
| CN106971245A (en) * | 2017-03-30 | 2017-07-21 | 广东工业大学 | A kind of determining method of path and system based on improvement ant group algorithm |
| CN111582582A (en) * | 2020-05-08 | 2020-08-25 | 西安建筑科技大学 | Warehouse picking path optimization method based on improved GA-PAC |
| CN112053010B (en) * | 2020-10-09 | 2022-02-08 | 腾讯科技（深圳）有限公司 | Riding path determining method and device, computer equipment and storage medium |
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