CN114967689A - Multi-stage post-disaster rescue path optimization method and system based on improved ant colony algorithm - Google Patents
Multi-stage post-disaster rescue path optimization method and system based on improved ant colony algorithm Download PDFInfo
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Abstract
The invention provides a multi-stage post-disaster rescue path optimization method and system based on an improved ant colony algorithm, and belongs to the technical field of data mining. The method comprises the following steps: acquiring basic information of each disaster-affected point of each disaster relief stage on site; taking a medical center as an initial starting point, and calculating the transition probability of the vehicle from the current position to the next disaster-affected point according to a formula (1); generating a rescue path of the vehicle according to the transition probability; constructing a multi-stage rescue scheme according to the loading capacity of the vehicle; judging whether the number of the generated ants is larger than an ant number threshold value or not; under the condition that the number of the generated ants is judged to be less than or equal to the threshold value of the number of the ants, the step of taking the medical center as an initial starting point and calculating the transition probability of the vehicle from the current position to the next disaster-affected point according to the formula (1) is returned to be executed again; and under the condition that the number of the ants generated currently is judged to be larger than the ant number threshold value, determining the optimal multi-stage rescue scheme generated currently.
Description
Technical Field
The invention relates to the technical field of data mining, in particular to a multi-stage post-disaster rescue path optimization method and system based on an improved ant colony algorithm.
Background
The post-disaster rescue path optimization is an important component of emergency logistics system planning, and is mainly used for researching how to rapidly and effectively plan an optimal rescue route after a disaster occurs, organizing rescue vehicles, moving to each disaster-affected point to rescue wounded persons, and timely transporting the wounded persons to corresponding medical centers to receive treatment, so that the pain of the wounded persons is greatly reduced. Therefore, the emergency management department must make a fair and effective rescue scheme, reduce the damage of the disaster to people and timely rescue people suffering from the disaster.
In the current post-disaster rescue path optimization, most documents are researched in the aspects of time minimization or logistics cost minimization, and the pain degree of the wounded caused by the lack of medical supplies in the process of waiting for rescue, namely the lack cost of the wounded, is rarely considered. The first important meaning of rescue is 'people-oriented and life saving', so the optimization of the rescue path after disaster needs to be from the human perspective, the rescue efficiency is improved to the maximum extent, and the pain degree of the wounded is reduced. Meanwhile, due to the fact that the wounded persons are different in wounded conditions, classification processing is conducted on the wounded persons in the early stage of rescue, and the method is helpful for achieving fairness of rescue, however, the wounded persons with the same property are considered in the existing research, consideration on the wounded persons with the different property is lacked, and therefore the wounded persons with the same property may miss gold rescue periods, and overall rescue efficiency is reduced. In addition, regarding the problem of the rescue phase, the existing research mostly assumes that one disaster-stricken point can only be visited once, that is, in one cycle, rescue of all disaster-stricken points can be realized, however, due to the uncertainty of the number of wounded persons and the constraint of vehicle loading capacity, one disaster-stricken point may need to be rescued many times to complete the rescue work. Therefore, in the post-disaster rescue path optimization, the conventional research has certain defects in the aspects of reducing the emergency degree of injury of the wounded, improving the overall rescue efficiency and realizing the multi-stage rescue path optimization of the heterogeneous wounded.
Disclosure of Invention
The embodiment of the invention aims to provide a multi-stage post-disaster rescue path optimization method based on an improved ant colony algorithm, and the method and the system can improve the optimization efficiency of a rescue path scheme.
In order to achieve the above object, an embodiment of the present invention provides a multi-stage post-disaster rescue path optimization method based on an improved ant colony algorithm, including:
acquiring basic information of each disaster-affected point of each disaster relief stage on site;
taking a medical center as an initial starting point, calculating the transition probability of the vehicle from the current position to the next disaster-affected point according to a formula (1),
wherein,
the transfer probability from the ith disaster-stricken point or the medical center to the jth disaster-stricken point for the r-th ant of the NC generation,
is the pheromone concentration between the ith disaster-affected point and the jth disaster-affected point of the NC generation, alpha is the pheromone importance degree factor,
η ij is the heuristic information of the distance between the ith disaster-affected point and the jth disaster-affected point, beta is a distance importance factor, gamma j Heuristic information of the expected deficit value generated for the victim set of the jth disaster-stricken point, lambda is an expected deficit value importance factor,
the r-th ant of the NC generation starts from the ith disaster-stricken point or the collection of disaster-stricken points which can go to the medical center,
is an index sequence number;
generating a rescue path of the vehicle according to the transition probability;
constructing a multi-stage rescue scheme according to the loading capacity of the vehicle;
judging whether the number of the ants generated currently is larger than an ant number threshold value or not;
under the condition that the number of the ants generated at present is judged to be less than or equal to the threshold value of the number of the ants, the step of calculating the transition probability of the vehicle from the current position to the next disaster point according to the formula (1) is executed again;
determining the currently generated optimal multi-stage rescue scheme under the condition that the number of the currently generated ants is judged to be larger than the ant number threshold;
updating the pheromone concentration;
judging whether the current iteration times are larger than a preset value or not;
under the condition that the iteration number is judged to be less than or equal to the preset value, the step of taking the medical center as an initial starting point and calculating the transition probability of the vehicle from the current position to the next disaster point according to the formula (1) is returned to be executed again;
and outputting an optimal multi-stage rescue scheme under the condition that the iteration times are judged to be larger than the preset value.
Optionally, generating a rescue path of the vehicle according to the transition probability comprises:
calculating the cumulative probability of the next disaster-affected point according to the formula (2),
wherein,
the cumulative probability of the e th disaster-affected point of the NC generation, wherein N is the set of the disaster-affected points;
randomly generating a random number between 0 and 1;
traversing each cumulative probability in a sequence from small to large, and judging whether the cumulative probability is larger than the random number;
under the condition that the cumulative probability is judged to be larger than the random number, selecting a disaster-affected point corresponding to the cumulative probability as a next disaster-affected point;
judging whether the disaster-stricken points which are not selected exist at present;
under the condition that the disaster-stricken point which is not selected is judged to exist at present, the current disaster-stricken point is updated, and the step of calculating the transition probability of the vehicle from the current position to the next disaster-stricken point according to the formula (1) is returned to be executed;
and outputting the rescue path under the condition that the current unselected disaster-stricken point does not exist.
Optionally, constructing the multi-stage rescue protocol according to the load of the vehicle comprises:
selecting a disaster-suffering point from the set of disaster-suffering points according to the sequence of the rescue path;
judging whether the remaining loadable quantity of the current vehicle is greater than or equal to the number of wounded persons at the selected disaster-stricken point;
adding the disaster-affected point into the running path of the current vehicle under the condition that the remaining loadable quantity of the current vehicle is judged to be more than or equal to the number of the wounded persons at the selected disaster-affected point;
judging whether unselected disaster-stricken points exist at present;
under the condition that the current disaster-stricken point which is not selected still exists, returning to execute the step of selecting one disaster-stricken point from the set of disaster-stricken points according to the sequence of the rescue path;
under the condition that the current disaster-stricken point does not exist, outputting the multi-stage rescue scheme;
and under the condition that the remaining loadable quantity of the current vehicle is judged to be smaller than the number of the wounded at the selected disaster-affected point, adding the selected disaster-affected point into the running path of the current vehicle, updating the number of the wounded at the selected disaster-affected point to be the difference between the number of the wounded at the selected disaster-affected point and the loadable quantity, adding a medical center in the running path of the current vehicle as a terminal point, and selecting a new vehicle.
Optionally, updating the pheromone concentration comprises:
updating the pheromone concentration according to formula (3) and formula (4),
wherein,
the pheromone concentration between the ith disaster-affected point and the jth disaster-affected point of the r-th ant of the NC +1 generation,
the pheromone concentration between the ith disaster-affected point and the jth disaster-affected point of the r-th ant of the NC generation is 0-1, and ro is an offset value;
wherein Q is a preset total pheromone release amount,
expected scarcity value of the r-th ant of the NC generation on the disaster relief stage set T, b ij Is shown as the r-th ant in the NC generation
Path of travel
Middle ith disaster-affected point d i And the jth disaster-affected point d j The position of the arc (i, j) in between.
Optionally, updating the pheromone concentration comprises:
updating the pheromone concentration according to formula (5),
wherein,
the pheromone concentration between the ith disaster-affected point and the jth disaster-affected point of the r th ant of the updated NC +1 generation is tau min Is the lower limit of the pheromone concentration, τ min Is the upper limit of the pheromone concentration.
Optionally, the method further comprises:
calculating the expected starvation value according to equation (6),
wherein S is the injury state set, S is the serial number index,
the amount to be rescued of the wounded member set in the s-th wounded condition state from the jth disaster-affected point in the tth rescue phase s Is a weight coefficient, f 1 、f 2 Is a preset constant.
Optionally, constructing the multi-stage rescue protocol according to the load of the vehicle comprises:
screening the multi-stage rescue scenario according to equations (7) to (9),
wherein,
for the t-th rescue stepAmount to be rescued of wounded person set of s-th wounded state of i-th disaster-affected point of section, N it The amount to be rescued of the wounded set of all the wounded states of the ith disaster-affected point in the tth rescue phase, o is the set of the wounded states, T is the set of the rescue phase, N is the set of the disaster-affected points,
transferring the wounded quantity of the s th wounded condition state of the ith disaster-affected point for K vehicles in the t-th rescue phase, wherein K is a vehicle set, and c is the total quantity of the vehicles.
Optionally, outputting the optimal multi-stage rescue protocol comprises:
selecting a multi-stage rescue scheme corresponding to the minimum expected deficit value as the optimal multi-stage rescue scheme.
In another aspect, the present invention also provides a multi-stage post-disaster rescue path optimization system based on an improved ant colony algorithm, the system including a processor configured to execute any one of the methods described above.
In yet another aspect, the invention also provides a computer readable storage medium having stored thereon instructions for reading by a machine to cause the machine to perform a method as described in any one of the above.
Through the technical scheme, the multi-stage post-disaster rescue path optimization method and system based on the improved ant colony algorithm optimize the rescue path by adopting the improved ant colony algorithm according to the basic information of the disaster-affected point. Compared with the prior art, the improved algorithm in the method and the system combines various information of the wounded at the disaster-affected point, so that the optimized rescue path has higher efficiency.
Additional features and advantages of embodiments of the invention will be set forth in the detailed description which follows.
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The accompanying drawings, which are included to provide a further understanding of the embodiments of the invention and are incorporated in and constitute a part of this specification, illustrate embodiments of the invention and together with the description serve to explain the embodiments of the invention without limiting the embodiments of the invention. In the drawings:
fig. 1 is a flow diagram of a multi-stage post-disaster rescue path optimization method based on an improved ant colony algorithm, according to an embodiment of the invention;
FIG. 2 is a flow diagram of a method of selecting a next disaster point according to one embodiment of the present invention;
FIG. 3 is an exemplary diagram of a generated rescue path according to one embodiment of the present invention;
fig. 4 is a flow chart of a method of generating a multi-stage rescue protocol according to one embodiment of the invention.
Detailed Description
The following detailed description of embodiments of the invention refers to the accompanying drawings. It should be understood that the detailed description and specific examples, while indicating embodiments of the invention, are given by way of illustration and explanation only, not limitation.
Fig. 1 is a flowchart illustrating a multi-stage post-disaster rescue path optimization method based on an improved ant colony algorithm according to an embodiment of the invention. In this fig. 1, the method may include:
in step S10, basic information of each disaster-stricken point in each disaster relief stage on the site is acquired;
in step S11, the transition probability of the vehicle from the current location to the next disaster point is calculated according to formula (1) with the medical center as the initial starting point,
wherein,
the transfer probability from the ith disaster-stricken point or the medical center to the jth disaster-stricken point for the r-th ant of the NC generation,
is the pheromone concentration between the ith disaster-affected point and the jth disaster-affected point of the NC generation, alpha is the pheromone importance degree factor,
η ij is the heuristic information of the distance between the ith disaster-affected point and the jth disaster-affected point, beta is a distance importance factor, gamma j Heuristic information of the expected deficit value generated for the victim set of the jth disaster-stricken point, lambda is an expected deficit value importance factor,
the r-th ant of the NC generation starts from the ith disaster-stricken point or the collection of disaster-stricken points which can go to the medical center,
is an index sequence number;
in step S12, a rescue path of the vehicle is generated according to the transition probability;
in step S13, a multi-stage rescue scheme is constructed according to the load of the vehicle;
in step S14, it is determined whether the number of ants currently generated is greater than the threshold number of ants;
under the condition that the number of the ants generated at present is judged to be less than or equal to the threshold value of the number of the ants, the step of taking the medical center as an initial starting point and calculating the transition probability of the vehicle from the current position to the next disaster-affected point according to the formula (1) is returned to be executed again;
in step S15, in a case where it is determined that the number of ants currently generated is greater than the threshold value of the number of ants, determining an optimal multi-stage rescue scheme that has been currently generated;
in step S16, the pheromone concentration is updated;
in step S17, it is determined whether the current iteration count is greater than a preset value;
under the condition that the iteration number is judged to be less than or equal to the preset value, the step of taking the medical center as an initial starting point and calculating the transition probability of the vehicle from the current position to the next disaster point according to the formula (1) is returned to be executed again;
in step S18, in the case where it is determined that the number of iterations is greater than the preset value, an optimal multi-stage rescue plan is output.
In the method as shown in fig. 1, step S10 is used to obtain basic information of each disaster-stricken point of each disaster-relief stage of the site. The basic information can be a set of rescue phases, a set of disaster points, a set of wounded states, a set of wounded, a set of rescue vehicles and a medical center of a rescue scene.
In the method, a plurality of rescue vehicles need to go to each disaster-affected point according to information such as wounded condition states, the number of wounded and the like from a medical center, so that a rescue task is completed. The steps S11 and S12 may be used to generate a rescue path for rescuing each disaster-affected point in sequence, and the step S13 is to generate a complete multi-stage rescue scheme according to the generated rescue path and the loading capacity of the vehicle itself.
Specifically, the step S11 may be a method of calculating the transition probability according to formula (1), the greater the transition probability, the greater the probability that the vehicle selects the disaster-stricken point in step S12, and determining the rescue path according to step S12, although there may be various methods known to those skilled in the art. However, in consideration of the operation efficiency of the algorithm in the subsequent iteration process, in a preferred example of the present invention, step S12 may be a method including the steps as shown in fig. 2. In this fig. 2, the method may include:
in step S20, the cumulative probability of the next disaster-affected point being selected is calculated according to formula (2),
wherein,
the cumulative probability of the e th disaster-affected point of the NC generation, wherein N is a set of disaster-affected points;
in step S21, a random number between 0 and 1 is randomly generated;
in step S22, traversing each cumulative probability in order from small to large, and determining whether the cumulative probability is greater than a random number;
in step S23, when it is determined that the cumulative probability is greater than the random number, selecting a disaster-affected point corresponding to the cumulative probability as a next disaster-affected point;
in step S24, it is determined whether there is a disaster-stricken point that has not been selected at present;
in step S25, when it is determined that there is a disaster-stricken point that has not been selected, the current disaster-stricken point is updated, and the step of calculating the transition probability of the vehicle from the current location to the next disaster-stricken point according to the formula (1) is performed;
in step S26, when it is determined that there is no disaster-stricken point that has not been selected at present, a rescue route is output.
After the rescue path is generated at step S12, step S13 may construct a multi-stage rescue scheme according to the load of the vehicle. Specifically, this step S13 may include steps as shown in fig. 3. In this fig. 3, step S13 may include:
in step S30, one disaster-stricken point is selected from the set of disaster-stricken points in the order of the rescue path;
in step S31, determining whether the remaining available load of the current vehicle is greater than or equal to the number of wounded persons at the selected disaster-stricken point;
in step S32, adding the disaster-stricken point to the current driving route of the vehicle when it is determined that the remaining available load of the current vehicle is greater than or equal to the number of the wounded persons at the selected disaster-stricken point;
in step S33, it is determined whether there is a disaster-stricken point that has not been selected currently;
in step S34, when it is determined that there is an unselected disaster-stricken point, the method returns to the step of selecting one disaster-stricken point from the set of disaster-stricken points in the order of the rescue path;
in step S35, in a case where it is determined that there is no unselected disaster-stricken point at present, outputting a multi-stage rescue plan;
and under the condition that the remaining loadable quantity of the current vehicle is judged to be smaller than the number of the wounded at the selected disaster-affected point, adding the selected disaster-affected point into the running path of the current vehicle, updating the number of the wounded at the selected disaster-affected point to be the difference between the number of the wounded at the selected disaster-affected point and the loadable quantity, adding a medical center into the running path of the current vehicle as a terminal point, and selecting a new vehicle.
In the method as illustrated in fig. 3, step S30 may sequentially select disaster-stricken points to be added to the traveling path of the currently selected vehicle according to the order of the rescue paths. Step S31 is to determine whether the currently selected vehicle can take on the rescue task of the selected disaster-stricken point. If the vehicle can bear the load, that is, if it is determined yes in step S31, the disaster-stricken point may be directly added to the travel route, and a new disaster-stricken point may be selected again. And when the vehicle cannot complete the rescue task of the disaster-stricken point, on one hand, the disaster-stricken point can be added into the driving path, so that the vehicle can move to the disaster-stricken point to complete the rescue amount which can be completed by the vehicle, and meanwhile, the actual number of wounded persons at the selected disaster-stricken point is updated. Step S33 may be used to determine whether there is no disaster-stricken point that can be selected currently, i.e., whether the multi-stage rescue plan has been generated.
Step S14 may determine whether the number of ants currently generated is greater than the ant number threshold. The number of ants may represent the number of the currently generated multi-stage rescue scenario, and the ant number threshold may represent the number of the multi-stage rescue scenario that needs to be generated. Step S15 may be used to determine the optimal multi-phase rescue protocol that has been currently generated, thereby facilitating subsequent updates of pheromone concentrations. In particular, in this embodiment, the method of determining the optimal multi-phase rescue scheme may be to first calculate the expected starvation value according to (6),
wherein S is the injury state setAnd s is the index of the sequence number,
the amount to be rescued of the wounded member set in the s-th wounded condition state from the jth disaster-affected point in the tth rescue phase s Is a weight coefficient, f 1 、f 2 Is a preset constant.
And selecting the multi-stage rescue scheme with the minimum expected shortage value as the optimal scheme through comparison.
Further, in this embodiment, as for the method of updating the pheromone concentration, although there may be various ways known to those skilled in the art, in a preferred example of the present invention, the method of updating the pheromone concentration may be updating the pheromone concentration according to formula (3), formula (4) and formula (5),
wherein,
the pheromone concentration between the ith disaster-affected point and the jth disaster-affected point of the r-th ant of the NC +1 generation,
the pheromone concentration between the ith disaster-affected point and the jth disaster-affected point of the r-th ant of the NC generation is 0-1, and ro is an offset value;
wherein Q is a preset total pheromone release amount,
expected scarcity value of the r-th ant of the NC generation on the disaster relief stage set T, b ij Is shown as the r-th ant in the NC generation
Path of travel
Middle ith disaster-affected point d i And the jth disaster-affected point d j The position of the arc (i, j) in between;
wherein,
the pheromone concentration between the ith disaster-affected point and the jth disaster-affected point of the r th ant of the updated NC +1 generation is tau min Is the lower limit of the pheromone concentration, τ min Is the upper limit of the pheromone concentration.
In this embodiment, in order to reduce the complexity of the algorithm, the multi-stage rescue scheme may be filtered through equations (7) to (9) when generating the multi-stage rescue scheme,
wherein,
the amount to be rescued of the wounded member set in the s th injury state of the ith disaster-affected point in the t th rescue phase, N it To-be-rescued of wounded personnel set in all wounded conditions of ith disaster-affected point of tth rescue phaseThe rescue amount, o is the set of the wounded condition, T is the set of the rescue phase, N is the set of the disaster-affected points,
transferring the wounded quantity of the s th wounded condition state of the ith disaster-affected point for K vehicles in the t-th rescue phase, wherein K is a vehicle set, and c is the total quantity of the vehicles.
In another aspect, the present invention further provides a multi-stage post-disaster rescue path optimization system based on an improved ant colony algorithm, the system including a processor configured to perform any one of the methods described above.
In yet another aspect, the invention also provides a computer readable storage medium having stored thereon instructions for reading by a machine to cause the machine to perform a method as described in any one of the above.
Through the technical scheme, the multi-stage post-disaster rescue path optimization method and system based on the improved ant colony algorithm optimize the rescue path by adopting the improved ant colony algorithm according to the basic information of the disaster-affected points. Compared with the prior art, the improved algorithm in the method and the system combines various information of the wounded at the disaster-affected point, so that the optimized rescue path has higher efficiency.
As will be appreciated by one skilled in the art, embodiments of the present application may be provided as a method, system, or computer program product. Accordingly, the present application may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present application may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein.
The present application is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the application. It will be understood that each flow and/or block of the flow diagrams and/or block diagrams, and combinations of flows and/or blocks in the flow diagrams and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
In a typical configuration, a computing device includes one or more processors (CPUs), input/output interfaces, network interfaces, and memory.
The memory may include forms of volatile memory in a computer readable medium, Random Access Memory (RAM) and/or non-volatile memory, such as Read Only Memory (ROM) or flash memory (flash RAM). The memory is an example of a computer-readable medium.
Computer-readable media, including both non-transitory and non-transitory, removable and non-removable media, may implement information storage by any method or technology. The information may be computer readable instructions, data structures, modules of a program, or other data. Examples of computer storage media include, but are not limited to, phase change memory (PRAM), Static Random Access Memory (SRAM), Dynamic Random Access Memory (DRAM), other types of Random Access Memory (RAM), Read Only Memory (ROM), Electrically Erasable Programmable Read Only Memory (EEPROM), flash memory or other memory technology, compact disc read only memory (CD-ROM), Digital Versatile Discs (DVD) or other optical storage, magnetic cassettes, magnetic tape magnetic disk storage or other magnetic storage devices, or any other non-transmission medium that can be used to store information that can be accessed by a computing device. As defined herein, a computer readable medium does not include a transitory computer readable medium such as a modulated data signal and a carrier wave.
It should also be noted that the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising an … …" does not exclude the presence of other identical elements in the process, method, article, or apparatus that comprises the element.
The above are merely examples of the present application and are not intended to limit the present application. Various modifications and changes may occur to those skilled in the art. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present application should be included in the scope of the claims of the present application.
Claims (10)
1. A multi-stage post-disaster rescue path optimization method based on an improved ant colony algorithm is characterized by comprising the following steps:
acquiring basic information of each disaster-affected point of each disaster relief stage on site;
taking a medical center as an initial starting point, calculating the transition probability of the vehicle from the current position to the next disaster-affected point according to a formula (1),
wherein,
the transfer probability from the ith disaster-stricken point or the medical center to the jth disaster-stricken point for the r-th ant of the NC generation,
is the pheromone concentration between the ith disaster-affected point and the jth disaster-affected point of the NC generation, alpha is the pheromone importance degree factor,
η ij is heuristic information of the distance between the ith disaster-affected point and the jth disaster-affected point, beta is a distance importance factor, gamma j Heuristic information of the expected deficit value generated for the victim set of the jth disaster-stricken point, lambda is an expected deficit value importance factor,
the r-th ant of the NC generation starts from the ith disaster-stricken point or the collection of disaster-stricken points which can go to the medical center,
is an index sequence number;
generating a rescue path of the vehicle according to the transition probability;
constructing a multi-stage rescue scheme according to the loading capacity of the vehicle;
judging whether the number of the ants generated currently is larger than an ant number threshold value or not;
under the condition that the number of the ants generated at present is judged to be less than or equal to the threshold value of the number of the ants, the step of taking the medical center as an initial starting point and calculating the transition probability of the vehicle from the current position to the next disaster-affected point according to the formula (1) is returned to be executed again;
determining the currently generated optimal multi-stage rescue scheme under the condition that the number of the currently generated ants is judged to be larger than the ant number threshold;
updating the pheromone concentration;
judging whether the current iteration times are larger than a preset value or not;
under the condition that the iteration number is judged to be less than or equal to the preset value, the step of taking the medical center as an initial starting point and calculating the transition probability of the vehicle from the current position to the next disaster point according to the formula (1) is returned to be executed again;
and outputting an optimal multi-stage rescue scheme under the condition that the iteration times are judged to be larger than the preset value.
2. The method of claim 1, wherein generating a rescue path for a vehicle according to the transition probability comprises:
calculating the cumulative probability of the next disaster-affected point according to the formula (2),
wherein,
the cumulative probability of the e th disaster-affected point of the NC generation, wherein N is the set of the disaster-affected points;
randomly generating a random number between 0 and 1;
traversing each cumulative probability in a sequence from small to large, and judging whether the cumulative probability is larger than the random number;
under the condition that the cumulative probability is judged to be larger than the random number, selecting a disaster-affected point corresponding to the cumulative probability as a next disaster-affected point;
judging whether the disaster-stricken points which are not selected exist at present;
under the condition that the disaster-stricken point which is not selected is judged to exist at present, the current disaster-stricken point is updated, and the step of calculating the transition probability of the vehicle from the current position to the next disaster-stricken point according to the formula (1) is returned to be executed;
and outputting the rescue path under the condition that the current unselected disaster-stricken point does not exist.
3. The method of claim 1, wherein constructing a multi-stage rescue protocol based on the loading of the vehicle comprises:
selecting a disaster-suffering point from the set of disaster-suffering points according to the sequence of the rescue path;
judging whether the remaining loadable quantity of the current vehicle is greater than or equal to the number of wounded persons at the selected disaster-stricken point;
adding the disaster-affected point into the running path of the current vehicle under the condition that the remaining loadable quantity of the current vehicle is judged to be more than or equal to the number of the wounded persons at the selected disaster-affected point;
judging whether the unselected disaster-affected points exist at present;
under the condition that the disaster-stricken points which are not selected currently exist, returning to execute the step of selecting one disaster-stricken point from the set of disaster-stricken points according to the sequence of the rescue path;
under the condition that the current disaster-stricken point does not exist, outputting the multi-stage rescue scheme;
and under the condition that the remaining loadable quantity of the current vehicle is judged to be smaller than the number of the wounded at the selected disaster-affected point, adding the selected disaster-affected point into the running path of the current vehicle, updating the number of the wounded at the selected disaster-affected point to be the difference between the number of the wounded at the selected disaster-affected point and the loadable quantity, adding a medical center in the running path of the current vehicle as a terminal point, and selecting a new vehicle.
4. The method of claim 1, wherein updating the pheromone concentration comprises:
updating the pheromone concentration according to formula (3) and formula (4),
wherein,
the pheromone concentration between the ith disaster-affected point and the jth disaster-affected point of the r-th ant of the NC +1 generation,
the pheromone concentration between the ith disaster-affected point and the jth disaster-affected point of the r-th ant of the NC generation is 0-1, and ro is an offset value;
wherein Q is a preset total pheromone release amount,
expected scarcity value of the r-th ant of the NC generation on the disaster relief stage set T, b ij Is shown as the r-th ant in the NC generation
Path of travel
Middle ith disaster-affected point d i And the jth disaster-affected point d j The position of the arc (i, j) in between.
5. The method of claim 4, wherein updating the pheromone concentration comprises:
updating the pheromone concentration according to formula (5),
wherein,
the pheromone concentration between the ith disaster-affected point and the jth disaster-affected point of the r th ant of the updated NC +1 generation is tau min Is the lower limit of the pheromone concentration, τ min Is the upper limit of the pheromone concentration.
6. The method of claim 1, further comprising:
calculating the expected starvation value according to equation (6),
wherein S is the injury state set, S is the index of sequence number,
the amount to be rescued of the wounded member set in the s-th wounded condition state from the jth disaster-affected point in the tth rescue phase s Is a weight coefficient, f 1 、f 2 Is a preset constant.
7. The method of claim 1, wherein constructing a multi-stage rescue protocol based on the loading of the vehicle comprises:
screening the multi-stage rescue scenario according to equations (7) through (9),
wherein,
the amount to be rescued of the wounded member set in the s th injury state of the ith disaster-affected point in the t th rescue phase, N it The amount to be rescued of the wounded set of all the wounded states of the ith disaster-affected point in the tth rescue phase, o is the set of the wounded states, T is the set of the rescue phase, N is the set of the disaster-affected points,
transferring the wounded quantity of the s th wounded condition state of the ith disaster-affected point for K vehicles in the t-th rescue phase, wherein K is a vehicle set, and c is the total quantity of the vehicles.
8. The method of claim 1, wherein outputting an optimal multi-stage rescue protocol comprises:
selecting a multi-stage rescue scheme corresponding to the minimum expected deficit value as the optimal multi-stage rescue scheme.
9. A multi-stage post-disaster rescue path optimization system based on an improved ant colony algorithm, characterized in that the system comprises a processor for performing the method according to any one of claims 1 to 8.
10. A computer-readable storage medium having stored thereon instructions for reading by a machine to cause the machine to perform the method of any one of claims 1 to 8.
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