CN106845907A - A kind of vehicle path planning method based on imperial competition algorithm - Google Patents

A kind of vehicle path planning method based on imperial competition algorithm Download PDF

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CN106845907A
CN106845907A CN201710073254.XA CN201710073254A CN106845907A CN 106845907 A CN106845907 A CN 106845907A CN 201710073254 A CN201710073254 A CN 201710073254A CN 106845907 A CN106845907 A CN 106845907A
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陈豪
王耀宗
张景欣
蔡品隆
张丹
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Quanzhou Institute of Equipment Manufacturing
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Abstract

A kind of vehicle path planning method based on imperial competition algorithm disclosed by the invention, including step：S1, regional environment communication chart, S2 are set up, initial empire, S3, the assimilation operation in empire is set up, S4, the revolution operation in empire, S5, is strengthened operation, the contention operation between S6, empire, S7, iteration judgement in empire.The present invention calculates an optimal travel route for meeting the composite request such as global optimization, calculating time, quality, the convergence rate of solution by the way that imperial competition algorithm is automatic, eliminate the energy consumption of driver or dispatcher oneself programme path, compared with original non-optimal path, optimizing the part reduced can directly save vehicle hour cost and fuel cost, the logistics distribution efficiency and service quality of bicycle are lifted, the operation cost of loglstics enterprise is reduced.

Description

A kind of vehicle path planning method based on imperial competition algorithm
Technical field
The invention belongs to the path planning field of communication and logistics, more particularly to a kind of car based on imperial competition algorithm Paths planning method.
Background technology
With the tide of Internet information technique, ecommerce, global positioning system, intelligent transportation system, geography information The technology of the instrument such as system and global system for mobile communications is further ripe, promotes developing rapidly for logistics.Logistics distribution is One key link of logistic industry, distribution vehicle most time is consumption in city is shuttled.The optimization energy of Distribution path The manpower and time cost in delivery process are directly reduced, the conevying efficiency and service quality of loglstics enterprise is obviously improved.So, The problem that vehicle path planning is mainly solved is：As how customer's dispatching demand is input, in operating range or elapsed time most Under few optimization aim, for distribution vehicle provides most rational travel route.
Because vehicle path planning is strong NP problems (problem for not being proved to be can solve the problem that with the presence or absence of multinomial algorithm), Thousands of dispatching points, directly carry out many car path plannings and are difficult to be obtained in available times to mass distribution point in city And feasible solution general precision is not high as a result,.In view of practicality, the common practices to extensive many car path plannings is, according to Avenue is laid out to distribution network subregion, and problem is converted into the bicycle path planning in small-scale region.
By region division, the dispatching point in small-scale region general at least tens is at most up to a hundred.In order to solve bicycle path Planning problem, typically there is exact algorithm, traditional heuritic approach and the class method of meta-heuristic algorithm three.Exact algorithm such as branch circle Determine method, dynamic programming, minimum K trees method, column generation method etc., it is impossible to avoid index exploding problem, be commonly available to very small rule The path planning problem of mould, is relatively difficult to application in practice.Structure type method, cleaning algorithm, local search algorithm, saving algrithm Deng traditional heuritic approach by certain intuitive judgment or exploration, the suboptimal solution of problem can be tried to achieve in acceptable time, it is this kind of Method has obtained good development over nearly 20 years, but lacks unified, complete theoretical system.With the increasing of Computing ability Long, first heuristic algorithm such as genetic algorithm, particle cluster algorithm, ant group algorithm, glowworm swarm algorithm, petal algorithm is increasingly becoming path rule The main stream approach drawn, this kind of algorithm has embedded different technology and concepts to evade local optimum, including random, evolution, memory Storage, guiding etc..Although current alternative is numerous, but still neither one generally acknowledges that perfect algorithm can solve path planning Np problem.Different paths planning methods can show one or more of following problems：It is poor for applicability, be absorbed in it is local most Excellent, excessive invalid computation, convergence rate be slow, it is solution it is of low quality, the problems such as stability is bad.Therefore for path planning side The research of method is generally intended to improve the defect of existing algorithm, or proposes the more preferable algorithm of general performance power.
The content of the invention
It is an object of the invention to provide a kind of vehicle path planning method based on imperial competition algorithm, can be direct It is applied in solution logistic industry, solves to dispense bicycle path planning problem a little during goods delivery more.
To achieve these goals, the present invention is adopted the following technical scheme that：
A kind of vehicle path planning method based on imperial competition algorithm, comprises the following steps：
S1, regional environment communication chart is set up, calmodulin binding domain CaM Environmental Communication figure provides point set, the arc of logistics center and dispatching point Collection, and provide logistics center and the mutual distance value of each dispatching point；The point set refers to logistics center and dispatching point in region Position point set on Environmental Communication figure, the road set between Hu Jizhi logistics centers and dispatching point；
S2, initial empire is set up, N number of path is generated at random by out of order algorithm and according to the point set and arc collection of step S1, Total distance in each path is calculated, N number of country is defined, various countries are assessed with the relational expression of state power by the total distance in path State power, sorts then according to state power and All Countries is divided into colonialist power and colony, generation power maximum NimpIndividual colonialist power, i.e. NimpIndividual empire and Ncol=N-NimpIndividual colony, stores colonialist power and colonial power respectively Table；
Assimilation operation in S3, empire：Colonialist power is randomly replaced on the basis of own path according to certain assimilability With the part point set in change colony path；
Revolution operation in S4, empire：Colony changes the part place position of own path at random, and variation pattern is section The 0-1 of point is exchanged and 1-1 is exchanged, and reappraises colonialist power and colonial power, if the latter's power is more than the former, is replaced The former forms new empire；
Enhancing operation in S5, empire：According to colonialist power's power table, to power, minimum colonialist power strengthens, Enhancement method is：Partial dot collection in the colonialist power path is removed at random, then by the point set random rearrangement of removal and is sequentially inserted into In remaining place it is possible that position, then reappraise colonialist power enhancing before and after power, reserved power is big to colonize Country；
Contention operation between S6, empire：In units of empire, colonialist power and all colonial standards in empire are calculated Change weighting power and, by assessing all empire's power, Qiang empires are distributed into the most weak colony of Ruo empires, additionally, It is considered as empire's disappearance when not having any colony in certain empire, then the colonialist power of the empire is also allocated to Qiang empires As colony；
S7, iteration judge：Judge whether whether an empire reaches default iterations, if then stopping computing, output Power maximum colonialist power path as optimal solution, if it is not, then returning to S3.
The step S1 is specifically included：The point set of logistics center and dispatching point is given first, defines G=(V, E, D) to need The regional environment communication chart of goods delivery is wanted, wherein V={ 1,2 ..., M } is the set of logistics center and the dispatching point to be passed through, That is point set, if V1It is logistics center, E=(i, j) | and (i, j ∈ V), i ≠ j } it is the road between logistics center and each dispatching point Set, i.e. arc collection, Distance matrix D=[dij]M×MIn each element representation logistics center or dispatching point " i and logistics center or The distance of dispatching point j, and have dij＞ 0, dii=+∞, i, j ∈ V.
The step S2 is specifically included：
S21, initialization path and distance：Initial path solution is built, i.e., to except V1Point set { V in addition2,V3,…,VMEnter The out of order arrangement of row n times, the different path L={ L of generation N bars1,L2,…,LNAnd its path always apart from dist={ dist1, dist2,…,distN, wherein the initial point in all paths is logistics center V1, then total distance in vehicle delivery path For
S22, initialization country and power：Define N number of national C={ C1,C2,…,CNAnd its state power c={ c1, c2,…,cN, and correlation is always set up into path apart from dist with state power cWherein cn、distnDifference table Show n-th total distance of power and corresponding path of country, initialize current iteration number of times iter=1 and default iterations iter_final；
S23, the colony distribution of colonialist power：All Countries are ranked up from big to small according to state power, weighting The maximum N of powerimpUsed as colonialist power, their power is defined as c_imp for individual countryi, i=1,2 ..., Nimp, remaining Ncol= N-NimpIt is individual as colony, their power are defined as c_colj, j=1,2 ..., Ncol；Then colonialist power's power is standardized HaveThen according to distribution formula N_impi=round { p_impi·NcolBy institute There is colony to distribute to colonialist power, n-th colonialist power can get N_impiIndividual colony, and meetWherein round represents bracket function, so far, terminates NimpThe initialization procedure of individual empire.
The step S3 is specifically included：
S31, initialization assimilability：Random number between generation M [0,1], in M random number correspondence colonialist power path solution M sequence number；Assimilability ρ is defined, span is 0 ＜ ρ ＜ 1；
The local assimilation of S32, path solution：Random number is regarded as assimilating place at the sequence number less than or equal to ρ, and assimilation mode is to grow Location number of the man of the Republic of China at these sequence numbers directly as assimilation after location number of the colony at same sequence number；
The local holding of S33, path solution：Sequence number for random number more than ρ, if place of the colony at these sequence numbers Numbering did not occurred in locally assimilation, then be regarded as being kept, and hold mode is location number of the colony at these sequence numbers Directly as location number of the colony after holding at same sequence number；
The local rearrangement of S34, path solution：Sequence number for random number more than ρ, if place of the colony at these sequence numbers Numbering occurred in locally assimilation, then be regarded as being rearranged, and rearranged form is the place at these sequence numbers by colonialist power Numbering, inserts the optional position in colony path after local holding one by one, and takes the minimum position for increasing distance；
S35 is replaced：Original colony is replaced into colony after local rearrangement.
The step S4 is comprised the following specific steps that：
S41,0-1 are exchanged：A place in addition to initial place is randomly choosed in the path of colony, by the point successively Insertion other positions, and the minimum position for increasing distance is taken, if there is more excellent solution, former solution is replaced, if nothing, retain former solution；
S42,1-1 are exchanged：In the path of colony randomly choose a place in addition to initial place, with other positions according to Secondary exchange, and take the minimum position for increasing distance；If there is more excellent solution, former solution is replaced, if nothing, retain former solution；
S43, assessment and revolution：All colonies are carried out after 0-1 and 1-1 exchange, colonialist power in assessment empire and Colony power is simultaneously ranked up, if colonial supreme power is more than colonialist power, the colony and colonialist power exchange Identity.
The step S5 is comprised the following specific steps that：
S51, m-m are exchanged：According to colonialist power's power table, using the minimum colonialist power of power as enhancing object, from growing Remove m place in man of the Republic of China path at random, then by this m place be sequentially inserted into order it is all in remaining M-m place can The position of energy, every time insertion takes the minimum position for increasing distance, until m place is inserted and finished；
S52, assessment and enhancing：Colonialist power path solution before and after exchanging m-m is estimated, if there is more excellent solution, replaces Original solution, being considered as empire strengthens successfully, if nothing, retains former solution.
Comprised the following specific steps that in the S6：
S61, calculating empire power：In NimpIn individual empire, i-th empire is by 1 colonialist power and N_impiIndividual colony Composition, i-th power T of empireiBy colonialist power's power c_impiWith colony power c_coljWeighting composition, is calculated as follows：
Then to NimpIndividual empire's power has been standardized：
And meetTherefore there is empire's power vector
S62, colony are annexed：Define the random vector of dimension identical with TpIts element Obey and be uniformly distributed Rpi~U (0, (1+N_impi)/N), and define probability vector：
Dp=Tp-Rp={ Dp1,Dp2,…,Dpimp}
={ Tp1-Rp1,Tp2-Rp2,…,Tpimp-Rpimp}
Then the most strong and Ruo empires corresponding to max { Dp } and min { Dp } are found, and power is most in finding Ruo empires Small colony min { c_col1,c_col2..., Qiang empires are assigned them to, update the colony number of most strong and Ruo empires Amount.
S63, empire annex：Judge whether without colonial empire, if so, then the colonialist power of the empire distributes to Qiang empires work as colony, and empire and colonialist power's quantity are Nimp=Nimp-1。
Particular content is as follows in the S7：
Judge whether current empire's quantity is one or whether iterations is more than or equal to iter_final, if so, then stopping Only computing, the path of output power maximum colonialist power is used as optimal solution, if it is not, S3 is then returned, iter=iter+1.
After using such scheme, present invention has the advantages that：The present invention is calculated automatically by imperial competition algorithm Meet an optimal travel route of the composite request such as global optimization, calculating time, quality, the convergence rate of solution, eliminate and drive The energy consumption of the person of sailing or dispatcher oneself programme path, compared with original non-optimal path, optimizing the part reduced can be straight Saving vehicle hour cost and fuel cost are connect, the logistics distribution efficiency and service quality of bicycle is lifted, logistics enterprise is reduced The operation cost of industry.In addition, imperial competition algorithm is applied to vehicle path planning field by the present invention first, it is the field There is provided a new point of penetration and new approaches based on society's inspiration swarm intelligence algorithm.
The present invention is described further below in conjunction with the accompanying drawings.
Brief description of the drawings
Fig. 1 is flow chart of the present invention based on imperial competition algorithm vehicle path planning method；
Fig. 2 is the schematic diagram that step S2 sets up initial empire；
Fig. 3 is the schematic diagram of step S3 empires inside assimilation operation；
Fig. 4 is the schematic diagram of step S4 empires inside revolution operation；
Fig. 5 is the schematic diagram of step S5 empires inside enhancing operation；
Fig. 6 is the schematic diagram of contention operation between step S6 empires.
Specific embodiment
This specific embodiment is illustrated with reference to Fig. 1, one kind that the embodiment of the present invention is disclosed is based on imperial competition algorithm Vehicle path planning method, carry out according to the following steps：
S1, regional environment communication chart is set up, calmodulin binding domain CaM Environmental Communication figure provides point set, the arc of logistics center and dispatching point Collection, and provide logistics center and the mutual distance value of each dispatching point；The point set refers to logistics center and dispatching point in region Position point set on Environmental Communication figure, the road set between Hu Jizhi logistics centers and dispatching point；Specifically：
The point set of logistics center and dispatching point is given first, and it is the regional environment for needing goods delivery to define G=(V, E, D) Communication chart, wherein V={ 1,2 ..., M } are the set of logistics center and the dispatching point to be passed through, i.e. point set, if V1For in logistics The heart, E=(i, j) | and (i, j ∈ V), i ≠ j } it is the road set between logistics center and each dispatching point, i.e. arc collection, distance matrix D=[dij]M×MIn each element representation dispatching point or logistics center i and dispatching point or the distance of logistics center j, and have dij＞ 0,dii=+∞, i, j ∈ V.
S2, initial empire is set up, N number of path is generated at random by out of order algorithm and according to the point set and arc collection of step S1, Total distance in each path is calculated, N number of country is defined, is distributed to various countries with the relational expression of state power by the total distance in path State power, sorts then according to state power and All Countries is divided into colonialist power and colony, generation power maximum NimpIndividual colonialist power, i.e. NimpIndividual empire and Ncol=N-NimpIndividual colony, stores colonialist power and colonial power respectively Table；
Referring to Fig. 2, according to history social fact, a complete empire is by a colonialist power and several colony groups Into.Additionally, path planning generally includes structure initial path solution, path optimization two walking greatly, therefore implement imperial competition During algorithm, initial empire should be first set up, being divided into following three steps is carried out：
S21, initialization path and distance：Initial path solution is built, i.e., to except V1Point set { V in addition2,V3,…,VMEnter The out of order arrangement of row n times, the different path L={ L of generation N bars1,L2,…,LNAnd its path always apart from dist={ dist1, dist2,…,distN, wherein the initial point in all paths is logistics center V1, then total distance in vehicle delivery path ForThe target of path planning is to make total distance minimum, is had：
s.t.
To the proper subclass S of any V
Constraint 1 and 2 represents that for any one arc only a line is entered to go out with a line, and constraint 3 is used to avoid producing Path solution containing sub-loop, | S | is the place number containing figure G in set S in constraint 4, and constraint 5 represents arc E(i,j)By as car Optimal path.
S22, initialization country and power：Define N number of national C={ C1,C2,…,CNAnd its state power c={ c1, c2,…,cN, and correlation is always set up into path apart from dist with state power cWherein cn、distnRespectively N-th total distance of power and corresponding path of country is represented, current iteration number of times iter=1 and default iterations is initialized iter_final；
S23, the colony distribution of colonialist power：All Countries are ranked up from big to small according to state power, weighting The maximum N of powerimpUsed as colonialist power, their power is defined as c_imp for individual countryi, i=1,2 ..., Nimp, remaining Ncol= N-NimpIt is individual as colony, their power are defined as c_colj, j=1,2 ..., Ncol；For the accuracy that subsequently calculates and just Profit, has to the standardization of colonialist power's powerThen according to distribution formula N_impi= round{p_impi·NcolColonialist power is distributed into all colonies, n-th colonialist power can get N_impiIt is individual to colonize Ground, and meetWherein round represents bracket function, so far, terminates NimpThe initialization of individual empire Journey.
Assimilation operation in S3, empire：Colonialist power is randomly replaced on the basis of own path according to certain assimilability With the part point set in change colony path；
Referring to Fig. 3, empire's assimilation is local convergence step of the imperial competition algorithm in optimization process, shows and grows Man of the Republic of China assimilates the colonial path solution of its subordinate on the basis of own path solution, and guides majorization of solutions direction, makes scattered Solution is converged to around more excellent solution, and empire's assimilation process is divided into following five steps to be carried out：
S31, initialization assimilability：Random number between generation M [0,1], in M random number correspondence colonialist power path solution M sequence number；Assimilability ρ is defined, to prevent preconvergence too fast, assimilability ρ is unsuitable excessive, and span is 0 ＜ ρ ＜ 1；
The local assimilation of S32, path solution：Random number is regarded as assimilating place at the sequence number less than or equal to ρ, and assimilation mode is to grow Location number of the man of the Republic of China at these sequence numbers directly as assimilation after location number of the colony at same sequence number；With table 1 As a example by, provide colonialist power and colonial path.2. 4. assimilability ρ=0.5 is taken, in colonialist power path, it is assumed that sequence number 6. the random number 7. 10. located is less than or equal to ρ, and their location number is used in assimilation, by the location number 3 at these sequence numbers, 7, 9th, 2,5 directly as location number of the colony at same sequence number after local assimilation.
The local optimum of the path of table 1 solution
The local holding of S33, path solution：Sequence number for random number more than ρ, if place of the colony at these sequence numbers Numbering did not occurred in locally assimilation, then be regarded as being kept, and hold mode is location number of the colony at these sequence numbers Directly as location number of the colony after holding at same sequence number；3. 5. by taking table 2 as an example, in colonialist power path, sequence number 8. the random number 9. located is more than ρ, wherein in the path of colony only sequence number 3. 5. 8. in location number 8,10,6 not upper (colony after part assimilation) occurs in one step result, then the place that 8. 5. 3. the colony renewal sequence number after local holding located is compiled Numbers 8,10,6.
The local holding of the path of table 2 solution
The local rearrangement of S34, path solution：Sequence number for random number more than ρ, if place of the colony at these sequence numbers Numbering occurred in locally assimilation, then be regarded as being rearranged, and rearranged form is the place at these sequence numbers by colonialist power Numbering, inserts the optional position in colony path after local holding one by one, and takes the minimum position for increasing distance；By taking table 3 as an example, In colonialist power path, 3. 5. 8. 9., wherein sequence number location number 3. 5. 8. is in previous step for serial number of the random number more than ρ Local holding is carried out, the location number 4 that 9. remaining sequence number is located is untreated, now will be (local to protect in 4 one by one insertion previous step result Hold rear colony), it is assumed that 3. insertion sequence number locates to make the total distance in path minimum, then can obtain final path 1,3,4,8,7,10, 9、2、6、5。
The local rearrangement of the path of table 3 solution
S35 is replaced：Original colony is replaced into colony after local rearrangement.
Revolution operation in S4, empire：Colony changes the part place position of own path at random, and variation pattern is section The 0-1 of point is exchanged and 1-1 is exchanged, and reappraises colonialist power and colonial power, if the latter's power is more than the former, is replaced The former forms new empire；
Referring to Fig. 4, empire's revolution is Local Search step of the imperial competition algorithm in optimization process, shows and grows The random part place position for changing own path in people ground, trial is found and is always solved apart from shorter path, the assimilation in empire After operation, all colonial solutions can gradually tend to the solution of colonialist power, therefore empire's revolution can increase the various of colony solution Property, prevent local convergence too fast.In addition, colonial solution is centered around near the solution of colonialist power, therefore it is a class Local Search. Empire's revolution process is divided into following three steps to be carried out：
S41,0-1 are exchanged：A place in addition to initial place is randomly choosed in the path of colony, by the point successively Insertion other positions, and the minimum position for increasing distance is taken, if there is more excellent solution, former solution is replaced, if nothing, retain former solution；
S42,1-1 are exchanged：In the path of colony randomly choose a place in addition to initial place, with other positions according to Secondary exchange, and take the minimum position for increasing distance；If there is more excellent solution, former solution is replaced, if nothing, retain former solution；
S43, assessment and revolution：All colonies are carried out after 0-1 and 1-1 exchange, colonialist power in assessment empire and Colony power is simultaneously ranked up, if colonial supreme power is more than colonialist power, the colony and colonialist power exchange Identity.
Enhancing operation in S5, empire：According to colonialist power's power table, to power, minimum colonialist power strengthens, Enhancement method is：Partial dot collection in the colonialist power path is removed at random, then by the point set random rearrangement of removal and is sequentially inserted into In remaining place it is possible that position, then reappraise colonialist power enhancing before and after power, reserved power is big to colonize Country；
Referring to Fig. 5, empire's enhancing is global search step of the imperial competition algorithm in optimization process, shows right In the most weak colonialist power of power, the part place position in its own path is changed at random, trial is found always apart from shorter road Footpath solves.Empire's revolution is Local Search, and empire's enhancing is global search, it is desirable to have stronger change dynamics, including following two steps：
S51, m-m are exchanged：According to colonialist power's power table, using the minimum colonialist power of power as enhancing object, from growing Remove m place in man of the Republic of China path at random, then by this m place be sequentially inserted into order it is all in remaining M-m place can The position of energy, every time insertion takes the minimum position for increasing distance, until m place is inserted and finished；
S52, assessment and enhancing：Colonialist power path solution before and after exchanging m-m is estimated, if there is more excellent solution, replaces Original solution, being considered as empire strengthens successfully, if nothing, retains former solution.
Contention operation between S6, empire：In units of empire, colonialist power and all colonial standards in empire are calculated Change weighting power and, by assessing all empire's power, Qiang empires are distributed into the most weak colony of Ruo empires, additionally, It is considered as empire's disappearance when not having any colony in certain empire, then the colonialist power of the empire is also allocated to Qiang empires As colony；
Referring to Fig. 6, empire's competition is global convergence step of the imperial competition algorithm in optimization process, is shown most Strong empire gradually absorbs the colony of Ruo empires, multiple small empires is gradually annexed into a Ge great empires, and multiple is completed with this Path solution is to a convergence process for globally optimal solution.Empire's contention operation includes three steps：
S61, calculating empire power：In NimpIn individual empire, i-th empire is by 1 colonialist power and N_impiIndividual colony Composition, i-th power T of empireiBy colonialist power's power c_impiWith colony power c_coljWeighting composition, is calculated as follows：
Then to NimpIndividual empire's power has been standardized：
And meetTherefore there is empire's power vector
S62, colony are annexed：Define the random vector of dimension identical with TpIts element Obey and be uniformly distributed Rpi~U (0, (1+N_impi)/N), and define probability vector：
Dp=Tp-Rp={ Dp1,Dp2,…,Dpimp}
={ Tp1-Rp1,Tp2-Rp2,…,Tpimp-Rpimp}
Then the most strong and Ruo empires corresponding to max { Dp } and min { Dp } are found, and power is most in finding Ruo empires Small colony min { c_col1,c_col2..., Qiang empires are assigned them to, update the colony number of most strong and Ruo empires Amount.
S63, empire annex：Judge whether without colonial empire, if so, then the colonialist power of the empire distributes to Qiang empires work as colony, and empire and colonialist power's quantity are Nimp=Nimp-1。
S7, iteration judge：Judge whether whether an empire reaches default iterations, if then stopping computing, output Power maximum colonialist power path as optimal solution, if it is not, then returning to S3.
S7 is specifically：Judge whether current empire's quantity is one or whether iterations is more than or equal to iter_final, If so, then stopping computing, the path of output power maximum colonialist power is used as optimal solution, if it is not, S3 is then returned, iter=iter +1。
Described above has shown and described the preferred embodiments of the present invention, it should be understood that the present invention is not limited to this paper institutes The form of disclosure, is not to be taken as the exclusion to other embodiment, and can be used for various other combinations, modification and environment, and energy Enough in invention contemplated scope herein, it is modified by the technology or knowledge of above-mentioned teaching or association area.And people from this area The change and change that member is carried out do not depart from the spirit and scope of the present invention, then all should be in the protection of appended claims of the present invention In the range of.

Claims (8)

1. a kind of vehicle path planning method based on imperial competition algorithm, it is characterised in that comprise the following steps：
S1, regional environment communication chart is set up, calmodulin binding domain CaM Environmental Communication figure provides point set, the arc collection of logistics center and dispatching point, And provide logistics center and the mutual distance value of each dispatching point；The point set refers to logistics center and dispatching point in regional environment Position point set on communication chart, the road set between Hu Jizhi logistics centers and dispatching point；
S2, initial empire is set up, N number of path is generated at random by out of order algorithm and according to the point set and arc collection of step S1, calculated Total distance in each path, defines N number of country, and the country of various countries is assessed with the relational expression of state power by the total distance in path Power, sorts then according to state power and All Countries is divided into colonialist power and colony, the maximum N of generation powerimpIt is individual Colonialist power, i.e. NimpIndividual empire and Ncol=N-NimpIndividual colony, stores colonialist power and colonial power table respectively；
Assimilation operation in S3, empire：Colonialist power is randomly replaced and changed on the basis of own path according to certain assimilability Become the part point set in the path of colony；
Revolution operation in S4, empire：Colony changes the part place position of own path at random, and variation pattern is node 0-1 is exchanged and 1-1 is exchanged, and reappraises colonialist power and colonial power, if the latter's power is more than the former, replaces the former Form new empire；
Enhancing operation in S5, empire：According to colonialist power's power table, to power, minimum colonialist power strengthens, and strengthens Mode is：Partial dot collection in the colonialist power path is removed at random, then by the point set random rearrangement of removal and is sequentially inserted into residue In place it is possible that position, then reappraise colonialist power enhancing before and after power, the big colonialist power of reserved power；
Contention operation between S6, empire：In units of empire, calculate colonialist power and all colonial standardization in empire and add Weigh power and by assessing all empire's power, Qiang empires are distributed into the most weak colony of Ruo empires, additionally, working as certain Do not have to be considered as empire's disappearance during any colony in individual empire, then the colonialist power of the empire is also allocated to the conduct of Qiang empires Colony；
S7, iteration judge：Judge whether whether an empire reaches default iterations, if then stopping computing, export power The path of maximum colonialist power is used as optimal solution, if it is not, then returning to S3.
2. a kind of vehicle path planning method based on imperial competition algorithm as claimed in claim 1, it is characterised in that The step S1 is specifically included：The point set of logistics center and dispatching point is given first, defines G=(V, E, D) to need goods to match somebody with somebody The regional environment communication chart for sending, the set that wherein V={ 1,2 ..., M } puts for logistics center and the dispatching to be passed through, i.e. point set, If V1It is logistics center, E=(i, j) | and (i, j ∈ V), i ≠ j } be the road set between logistics center and each dispatching point, i.e., Arc collection, Distance matrix D=[dij]M×MIn each element representation logistics center or dispatching point i and logistics center or dispense point j's Distance, and have dij＞ 0, dii=+∞, i, j ∈ V.
3. a kind of vehicle path planning method based on imperial competition algorithm as claimed in claim 2, it is characterised in that The step S2 is specifically included：
S21, initialization path and distance：Initial path solution is built, i.e., to except V1Point set { V in addition2,V3,…,VMCarry out n times Out of order arrangement, the different path L={ L of generation N bars1,L2,…,LNAnd its path always apart from dist={ dist1,dist2,…, distN, wherein the initial point in all paths is logistics center V1, then total distance in a vehicle delivery path be
S22, initialization country and power：Define N number of national C={ C1,C2,…,CNAnd its state power c={ c1,c2,…, cN, and correlation is always set up into path apart from dist with state power cWherein cn、distnN-th is represented respectively The total distance of power and corresponding path of individual country, initializes current iteration number of times iter=1 and default iterations iter_ final；
S23, the colony distribution of colonialist power：All Countries are ranked up from big to small according to state power, weighting power is most Big NimpUsed as colonialist power, their power is defined as c_imp for individual countryi, i=1,2 ..., Nimp, remaining Ncol=N-Nimp It is individual as colony, their power are defined as c_colj, j=1,2 ..., Ncol；Then have to the standardization of colonialist power's powerThen according to distribution formula N_impi=round { p_impi·NcolWill be all Colonialist power is distributed in colony, and n-th colonialist power can get N_impiIndividual colony, and meet Wherein round represents bracket function, so far, terminates NimpThe initialization procedure of individual empire.
4. a kind of vehicle path planning method based on imperial competition algorithm as claimed in claim 1, it is characterised in that The step S3 is specifically included：
S31, initialization assimilability：Random number between generation M [0,1], the M in M random number correspondence colonialist power path solution Individual sequence number；Assimilability ρ is defined, span is 0 ＜ ρ ＜ 1；
The local assimilation of S32, path solution：Random number is regarded as assimilating place at the sequence number less than or equal to ρ, and assimilation mode is the state that colonizes Location number of the family at these sequence numbers directly as assimilation after location number of the colony at same sequence number；
The local holding of S33, path solution：Sequence number for random number more than ρ, if location number of the colony at these sequence numbers Do not occurred in locally assimilation, be then regarded as being kept, hold mode was that location number of the colony at these sequence numbers is direct As location number of the colony after holding at same sequence number；
The local rearrangement of S34, path solution：Sequence number for random number more than ρ, if location number of the colony at these sequence numbers Occurred in locally assimilation, be then regarded as being rearranged, rearranged form was the location number at these sequence numbers by colonialist power, The optional position in colony path after part keeps is inserted one by one, and takes the minimum position for increasing distance；
S35 is replaced：Original colony is replaced into colony after local rearrangement.
5. a kind of vehicle path planning method based on imperial competition algorithm as claimed in claim 4, it is characterised in that The step S4 is comprised the following specific steps that：
S41,0-1 are exchanged：A place in addition to initial place is randomly choosed in the path of colony, the point is sequentially inserted into Other positions, and the minimum position for increasing distance is taken, if there is more excellent solution, former solution is replaced, if nothing, retain former solution；
S42,1-1 are exchanged：A place in addition to initial place is randomly choosed in the path of colony, is handed over successively with other positions Change, and take the minimum position for increasing distance；If there is more excellent solution, former solution is replaced, if nothing, retain former solution；
S43, assessment and revolution：After all colonies are carried out with 0-1 and 1-1 exchanges, assess the colonialist power in empire and colonize Ground power is simultaneously sorted, if colonial supreme power is more than colonialist power, the colony and colonialist power exchange identity.
6. a kind of vehicle path planning method based on imperial competition algorithm as claimed in claim 4, it is characterised in that The step S5 is comprised the following specific steps that：
S51, m-m are exchanged：According to colonialist power's power table, using the minimum colonialist power of power as enhancing object, from the state that colonizes M place is removed in family path at random, then this m place is sequentially inserted into order all possible in remaining M-m place Position, every time insertion takes the minimum position for increasing distance, until m place insertion is finished；
S52, assessment and enhancing：Colonialist power path solution before and after exchanging m-m is estimated, if there is more excellent solution, replaces former solution, Being considered as empire strengthens successfully, if nothing, retains former solution.
7. a kind of vehicle path planning method based on imperial competition algorithm as claimed in claim 4, it is characterised in that Comprised the following specific steps that in the S6：
S61, calculating empire power：In NimpIn individual empire, i-th empire is by 1 colonialist power and N_impiIndividual colony composition, I-th power T of empireiBy colonialist power's power c_impiWith colony power c_coljWeighting composition, is calculated as follows：
Tc i = c _ imp i + δ N _ imp i Σ j = 1 N _ imp i c _ col j
Then to NimpIndividual empire's power has been standardized：
Tp i = Tc i - m i n { Tc k } Σ l = 1 N i m p ( Tc l - min { Tc k } ) , k ≤ N i m p
And meetTherefore there is empire's power vector
S62, colony are annexed：Define the random vector of dimension identical with TpIts element takes From being uniformly distributed Rpi~U (0, (1+N_impi)/N), and define probability vector：
Dp=Tp-Rp={ Dp1,Dp2,…,Dpimp}
={ Tp1-Rp1,Tp2-Rp2,…,Tpimp-Rpimp}
Then the most strong and Ruo empires corresponding to max { Dp } and min { Dp } are found, and power minimum is grown in finding Ruo empires People ground min { c_col1,c_col2..., Qiang empires are assigned them to, update the colony quantity of most strong and Ruo empires.
S63, empire annex：Judge whether without colonial empire, if so, then the colonialist power of the empire distribute to it is most strong Empire works as colony, and empire and colonialist power's quantity are Nimp=Nimp-1。
8. a kind of vehicle path planning method based on imperial competition algorithm as claimed in claim 4, it is characterised in that Particular content is as follows in the S7：
Judge whether current empire's quantity is one or whether iterations is more than or equal to iter_final, if so, then stopping fortune Calculate, the path of output power maximum colonialist power is used as optimal solution, if it is not, S3 is then returned, iter=iter+1.
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