CN103366021A - Variable neighborhood search method and system on cloud computing platform - Google Patents
Variable neighborhood search method and system on cloud computing platform Download PDFInfo
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- CN103366021A CN103366021A CN2013103418927A CN201310341892A CN103366021A CN 103366021 A CN103366021 A CN 103366021A CN 2013103418927 A CN2013103418927 A CN 2013103418927A CN 201310341892 A CN201310341892 A CN 201310341892A CN 103366021 A CN103366021 A CN 103366021A
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Abstract
The invention discloses a variable neighborhood search method and system on a cloud computing platform. The which are variable neighborhood search method and system on the cloud computing platform used for overcoming the defects, of excessively-quick convergence and being weakened in the capacity of fleeing a local extreme value, existing in conducted variable neighborhood search based on a single living example. The method includes the steps of presetting a data collection and a plurality of initial solutions and storing the initial solutions to the data collection;, using a plurality of living examples and being based on at least one of the initial solutions to conduct neighborhood search on a solving space; for any living example, when a locally optimal solution which is obtained in a searching mode is superior to a worst solution in the data collection, using the locally optimal solution to update the worst solution, wherein the worst solution is the solution of a minimum value among initial solutions and/or locally optimal solution in the data collection. The living examples are adopted to simultaneously conduct search and results can be searched mutually among the living examples, therefore, solving space can be effectively expanded, the possibility of fleeing locally optimal possibility is improved, and furthermore a better overall optimal solution can be obtained.
Description
Technical field
The present invention relates to a kind of change neighborhood search technology, relate in particular to the change neighborhood search method and system on a kind of cloud computing platform.
Background technology
Become neighborhood search algorithm (Variable Neighborhood Search, be called for short VNS), it is a kind of path type meta-heuristic algorithm, it has comprised the neighbour structure of dynamic change, algorithm is more general, degree of freedom is large, can design multiple modification for specific question, is mainly used in finding the solution combinatorial optimization problem and Global Optimal Problem.
The method for solving of combinatorial optimization problem can be divided into two classes: a class is exact algorithm, and this class algorithm carries out complete search to solution space, can guarantee to find the on a small scale optimum solution of problem; Another kind of is heuritic approach, and this class algorithm has been abandoned the integrality to Searching Resolution Space, therefore can not guarantee the final Global Optimality of separating.
Owing to exist in a large number nondeterministic polynomial difficulty (NP-Hard) problem in the combinatorial optimization problem, so the precise search algorithm often is difficult to when problem scale is larger realize.Although and heuritic approach can not prove the optimality of solution, can find out preferably approximate optimal solution with rational calculation cost under many circumstances.
The basic thought that becomes the neighborhood search algorithm is in search procedure, based on the locally optimal solution that has found, systematically changes its neighbour structure, expands the hunting zone with this, and then finds another locally optimal solution.
Become the neighborhood search algorithm and comprised Local Search (Local Search), disturbance (Shaking) and three processes of neighborhood conversion, utilize Local Search to seek locally optimal solution, improve search precision, adopt perturbation process to jump out the scope of locally optimal solution, seek new locally optimal solution, so that locally optimal solution is drawn close to globally optimal solution, the neighborhood conversion provides a kind of iterative manner and stopping criterion.
But present change neighborhood search all is based on single instance carries out, limited to the search capability of solution room, exists convergence too fast and flee from the shortcomings such as the local extremum ability is weak.
Summary of the invention
Technical matters to be solved by this invention is that to overcome the change neighborhood search of carrying out based on single instance at present all be to exist convergence too fast and flee from the weak deficiency of local extremum ability.
In order to solve the problems of the technologies described above, the invention provides the change neighborhood search method on a kind of cloud computing platform, being applied to Multi-instance searches for a solution room, wherein, the method comprises: a default data acquisition and a plurality of initial solution, store described a plurality of initial solutions in the described data acquisition into; Adopt described Multi-instance based in described a plurality of initial solutions at least one, described solution room is carried out respectively neighborhood search; For arbitrary example, when the locally optimal solution that obtains of search is better than the poorest solutions in the described data acquisition, adopt described locally optimal solution renewal the poorest described solution; Wherein, the poorest described solution is the solution of minimum value in initial solution in the described data acquisition and/or the locally optimal solution.
Preferably, the method comprises: for the outer arbitrary example of example of operation globally optimal solution in the described Multi-instance, if the iterative search of preset times does not upgrade the locally optimal solution of oneself, then from described data acquisition, choose at random an initial solution or locally optimal solution and carry out iterative search.
Preferably, described a plurality of initial solutions are stored in the described data acquisition, comprising: described a plurality of initial solutions are sorted according to size.
Preferably, the method comprises: after adopting described locally optimal solution to upgrade the poorest described solution, the initial solution in the described data acquisition and/or locally optimal solution are sorted according to size.
The application's embodiment also provides the system of the change neighborhood search on a kind of cloud computing platform, being applied to Multi-instance searches for a solution room, wherein, this system comprises: presetting module, be set to a default data acquisition and a plurality of initial solution, described a plurality of initial solutions are stored in the described data acquisition; Search module, be set to adopt described Multi-instance based in described a plurality of initial solutions at least one, described solution room is carried out respectively neighborhood search; Update module is set to for arbitrary example, when the locally optimal solution that obtains of search is better than the poorest solutions in the described data acquisition, adopts described locally optimal solution renewal the poorest described solution; Wherein, the poorest described solution is the solution of minimum value in initial solution in the described data acquisition and/or the locally optimal solution.
Preferably, this system comprises: replacement module, link to each other with described presetting module and search module, be set to the outer arbitrary example of example for operation globally optimal solution in the described Multi-instance, if the iterative search of preset times does not upgrade the locally optimal solution of oneself, then from described data acquisition, choose at random an initial solution or locally optimal solution and carry out iterative search.
Preferably, this system comprises: order module, link to each other with described presetting module, and be set to described a plurality of initial solutions are sorted according to size.
Preferably, after described order module is set to described update module and adopts described locally optimal solution to upgrade the poorest described solution, the initial solution in the described data acquisition and/or locally optimal solution are sorted according to size.
Compared with prior art, the application's embodiment adopts many examples to carry out concurrent search, and can be between each example the interactive searching result, can effectively expand solution room, increase and flee from the possibility of local optimum, and then can obtain better globally optimal solution.
Other features and advantages of the present invention will be set forth in the following description, and, partly from instructions, become apparent, perhaps understand by implementing the present invention.Purpose of the present invention and other advantages can realize and obtain by specifically noted structure in instructions, claims and accompanying drawing.
Description of drawings
Accompanying drawing is used to provide the further understanding to technical solution of the present invention, and consists of the part of instructions, is used from the application's embodiment one and explains technical scheme of the present invention, does not consist of the restriction to technical solution of the present invention.
Fig. 1 is the schematic flow sheet of the change neighborhood search method on the cloud computing platform of the embodiment of the present application.
Fig. 2 is the organigram of the change neighborhood search system on the cloud computing platform of the embodiment of the present application.
Embodiment
Describe embodiments of the present invention in detail below with reference to drawings and Examples, how the application technology means solve technical matters to the present invention whereby, and the implementation procedure of reaching technique effect can fully understand and implements according to this.Each feature among the embodiment of the present application and the embodiment mutually combining under the prerequisite of not conflicting mutually is all within protection scope of the present invention.
In addition, can in the computer system such as one group of computer executable instructions, carry out in the step shown in the process flow diagram of accompanying drawing.And, although there is shown logical order in flow process, in some cases, can carry out step shown or that describe with the order that is different from herein.
Change neighborhood search method on the cloud computing platform of the embodiment of the present application is mainly used in Multi-instance one solution room is carried out neighborhood search.As shown in Figure 1, the method mainly comprises following content.
Step S110, a default data acquisition.This data acquisition is mainly used to store Multi-instance and carries out the solution that neighborhood search obtains.
Step S120, default a plurality of initial solutions, each example can carry out respectively neighborhood search to the search volume based at least one initial solution wherein.The quantity of initial solution can be more than or equal to the quantity of example.
Step S130 stores each initial solution in the default data acquisition into.The application's embodiment can sort according to size to the initial solution in the data set.
Step S140 adopts Multi-instance based on each self-corresponding initial solution described solution room to be carried out neighborhood search.The each time neighborhood search of each example can be carried out based on a current solution, obtains locally optimal solution by search in the neighborhood set of this current solution; Wherein primary neighborhood search is based on initial solution and carries out.
Including some the neighborhood transform methods to current solution in the neighborhood set, thereby change the structure of current solution, and then can obtain different adaptive value (fitness), also is aforesaid locally optimal solution.
Step S150, for arbitrary example, when the locally optimal solution that neighborhood search obtains is better than the poorest solution in the described data acquisition, the locally optimal solution that adopts described arbitrary example neighborhood search to obtain upgrades the poorest described solution, then initial solution and/or locally optimal solution in the data set is sorted according to size.
The poorest solution wherein is the solution of minimum value in the initial solution in the data acquisition and/or the locally optimal solution.
After iterative search was finished, the optimum solution in the data acquisition was globally optimal solution.
Among the application's the embodiment, the outer arbitrary example of example for operation globally optimal solution in the Multi-instance, if the iterative search of preset times does not upgrade the locally optimal solution of oneself, then from data acquisition, choose at random an initial solution or locally optimal solution as the current solution of oneself, and utilize this current solution of choosing at random to carry out follow-up iterative search.
The application's embodiment when using, can carry out according to following algorithm flow.
Step1 sets initial parameter: M (example number), K
Max(neighbour structure number), T
Max(the total iterations of algorithm) and neighbour structure set N
k(k=1 ..., K
Max); For example m (m=1 ..., M) set an initial solution
And record its current optimum solution:
Current solution
k
m=1, t
m=1, i
m=1.Initialization data set D, Iter (maximum iteration time that algorithm allows optimum solution not upgrade).
Step2 for each example m (m=1 ..., M), work as t
m>T
MaxThe time, example m stops computing; Otherwise,
K
mNeighborhood
In choose at random solution
And to separating
Carry out Local Search, obtain locally optimal solution
If
Then
And the poorest solution that will gather among the D is updated to
k
m=1, i
m=1; Otherwise, if i
m>Iter then selects an optimum solution at random from set D, give
k
m=1, i
m=1; Otherwise, k=kmod K
Max+ 1, i
m=i
m+ 1.T=t+1, repeating step Step2.
Change neighborhood search system on the cloud computing platform of the embodiment of the present application is applied to Multi-instance one solution room is searched for.As shown in Figure 2, this system mainly comprises presetting module 210, search module 220 and update module 230.
Presetting module 210 is set to a default data acquisition and a plurality of initial solution, and a plurality of initial solutions are stored in the data acquisition.
Search module 220 links to each other with presetting module 210, be set to adopt Multi-instance based in a plurality of initial solutions at least one, solution room is carried out respectively neighborhood search, obtain separately locally optimal solution corresponding to this search.
Update module 230 links to each other with presetting module 210 and search module 220, is set to for arbitrary example, and when the locally optimal solution that obtains of search is better than the poorest solutions in the data acquisition, the poorest solution of locally optimal solution renewal that adopts this arbitrary example search to obtain.Wherein, the poorest solution is the solution of minimum value in initial solution in the data acquisition and/or the locally optimal solution.
As shown in Figure 2, change neighborhood search system on the cloud computing platform of the embodiment of the present application, can also comprise a replacement module 240, it links to each other with presetting module 210 and search module 220, be set to the outer arbitrary example of example for operation globally optimal solution in the Multi-instance, if the iterative search of preset times does not upgrade the locally optimal solution of oneself, then from data acquisition, choose at random an initial solution or locally optimal solution as the current solution of oneself, and utilize current solution to carry out follow-up iterative search.
As shown in Figure 2, the change neighborhood search system on the cloud computing platform of the embodiment of the present application can also comprise an order module 250, and it links to each other with presetting module 210, is set to a plurality of initial solutions are sorted according to size.
Among the application's the embodiment, order module 250 sorts according to size to initial solution and/or locally optimal solution in the data set after being set to the poorest solution of update module 230 employing locally optimal solutions renewals.
When the application's embodiment is applied on cloud computing platform, many computing nodes can be built into a cluster, and create cluster file system at shared storage device.At each computing node carry cluster file system, shared resource is carried out Concurrency Access.In cluster file system, create a plurality of virtual machine image, and by the virtualization process of bottom virtual machine is operated on the different computing nodes.In each virtual machine, create an application, be used for example solution room is carried out neighborhood search.Create a shared file in cluster file system, this shared file is used for the globally optimal solution that all examples of record find, and the simultaneously application in each virtual machine all can be accessed this shared file, to carry out the information interaction between example.
Among the application's the embodiment, Multi-instance can carry out neighborhood search to solution room simultaneously, has effectively enlarged solution room, and can use efficiently limited computational resource, improves processing power and efficient.The application's embodiment can be as required, between each example, carry out information interaction, share historical optimum solution separately, can effectively avoid the Premature Convergence of iterative search, improve search and fled from the possibility of local extremum, and then can obtain better globally optimal solution.
It is apparent to those skilled in the art that each ingredient of the system that above-mentioned the embodiment of the present application provides, and each step in the method, they can concentrate on the single calculation element, perhaps are distributed on the network that a plurality of calculation elements form.Alternatively, they can be realized with the executable program code of calculation element.Thereby, they can be stored in the memory storage and be carried out by calculation element, perhaps they are made into respectively each integrated circuit modules, perhaps a plurality of modules in them or step are made into the single integrated circuit module and realize.Like this, the present invention is not restricted to any specific hardware and software combination.
Although the disclosed embodiment of the present invention as above, the embodiment that described content only adopts for ease of understanding the present invention is not to limit the present invention.Those of skill in the art under any the present invention; under the prerequisite that does not break away from the disclosed spirit and scope of the present invention; can carry out any modification and variation in form and the details implemented; but scope of patent protection of the present invention still must be as the criterion with the scope that appending claims was defined.
Claims (8)
1. the change neighborhood search method on the cloud computing platform is applied to Multi-instance one solution room is searched for, and wherein, the method comprises:
A default data acquisition and a plurality of initial solution store described a plurality of initial solutions in the described data acquisition into;
Adopt described Multi-instance based in described a plurality of initial solutions at least one, described solution room is carried out respectively neighborhood search;
For arbitrary example, when the locally optimal solution that obtains of search is better than the poorest solutions in the described data acquisition, adopt described locally optimal solution renewal the poorest described solution;
Wherein, the poorest described solution is the solution of minimum value in initial solution in the described data acquisition and/or the locally optimal solution.
2. method according to claim 1, wherein, the method comprises:
The outer arbitrary example of example for operation globally optimal solution in the described Multi-instance, if the iterative search of preset times does not upgrade the locally optimal solution of oneself, then from described data acquisition, choose at random an initial solution or locally optimal solution and carry out iterative search.
3. method according to claim 1 wherein, stores described a plurality of initial solutions in the described data acquisition into, comprising:
Described a plurality of initial solutions are sorted according to size.
4. method according to claim 3, wherein, the method comprises:
After adopting described locally optimal solution to upgrade the poorest described solution, the initial solution in the described data acquisition and/or locally optimal solution are sorted according to size.
5. the change neighborhood search system on the cloud computing platform is applied to Multi-instance one solution room is searched for, and wherein, this system comprises:
Presetting module is set to a default data acquisition and a plurality of initial solution, and described a plurality of initial solutions are stored in the described data acquisition;
Search module, be set to adopt described Multi-instance based in described a plurality of initial solutions at least one, described solution room is carried out respectively neighborhood search;
Update module is set to for arbitrary example, when the locally optimal solution that obtains of search is better than the poorest solutions in the described data acquisition, adopts described locally optimal solution renewal the poorest described solution;
Wherein, the poorest described solution is the solution of minimum value in initial solution in the described data acquisition and/or the locally optimal solution.
6. system according to claim 5, wherein, this system comprises:
Replacement module, link to each other with described presetting module and search module, be set to the outer arbitrary example of example for operation globally optimal solution in the described Multi-instance, if the iterative search of preset times does not upgrade the locally optimal solution of oneself, then from described data acquisition, choose at random an initial solution or locally optimal solution and carry out iterative search.
7. system according to claim 5, wherein, this system comprises:
Order module links to each other with described presetting module, is set to described a plurality of initial solutions are sorted according to size.
8. system according to claim 7, wherein:
After described order module is set to described update module and adopts described locally optimal solution to upgrade the poorest described solution, the initial solution in the described data acquisition and/or locally optimal solution are sorted according to size.
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