CN114154735A - Method and device for determining driving path, electronic equipment and storage medium - Google Patents

Method and device for determining driving path, electronic equipment and storage medium Download PDF

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CN114154735A
CN114154735A CN202111494738.4A CN202111494738A CN114154735A CN 114154735 A CN114154735 A CN 114154735A CN 202111494738 A CN202111494738 A CN 202111494738A CN 114154735 A CN114154735 A CN 114154735A
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李华峰
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China Construction Bank Corp
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Abstract

The disclosure provides a method for determining a driving path, which can be applied to the technical field of computers. The method for determining the travel path comprises the following steps: randomly encoding the user information to form an initial population set, wherein the initial population set comprises a plurality of individual information subsets, and each individual information subset comprises a plurality of user current information and corresponding path information; aiming at each individual information subset, calculating the fitness of the individual information subset according to the current information of the user and the corresponding path information to obtain a plurality of fitness; layering the plurality of individual information subsets according to the optimization parameters and the fitness; optimizing corresponding path information in a plurality of individual information subsets according to the levels of the individual information subsets to obtain optimized corresponding path information; and determining a driving path according to the optimized corresponding path information. The present disclosure also provides an apparatus, a device, a storage medium, and a program product for determining a travel path.

Description

Method and device for determining driving path, electronic equipment and storage medium
Technical Field
The present disclosure relates to the field of computer technologies, and more particularly, to a method, an apparatus, a sheet device, a storage medium, and a program product for determining a travel path.
Background
Currently, intelligent optimization algorithms for solving vehicle path problems include: genetic algorithm, particle swarm algorithm, simulated annealing algorithm, ant colony algorithm and the like. However, in the above intelligent optimization algorithm, only competition and cooperation among individuals are considered in the optimization process, and competition and cooperation among populations are not considered at the same time, so that the problems of low solution accuracy and low convergence speed are caused. The mechanism of division of labor and promotion of transmission of the pyramid evolution algorithm can consider competition and cooperation among populations, but neglects competition and cooperation among individuals, and can also cause the problems of low solving precision and low convergence speed.
Disclosure of Invention
In view of the above problems, the present disclosure provides a method, an apparatus, an electronic device, a storage medium, and a program product for determining a driving path, which can simultaneously consider competition and cooperation between groups and competition and cooperation between individuals when solving a vehicle path problem, thereby achieving the effects of high solution accuracy and fast convergence speed.
According to an aspect of the present disclosure, there is provided a method of determining a travel path, including: acquiring user information, and randomly encoding the user information to form an initial population set, wherein the initial population set comprises a plurality of individual information subsets, and each individual information subset comprises a plurality of user current information and corresponding path information; aiming at each individual information subset, calculating the fitness of the individual information subset according to the current information of the user and the corresponding path information to obtain a plurality of fitness; layering the plurality of individual information subsets according to the optimization parameters and the fitness; optimizing corresponding path information in a plurality of individual information subsets according to the levels of the individual information subsets to obtain optimized corresponding path information; and determining a driving path according to the optimized corresponding path information.
According to an embodiment of the disclosure, the method further comprises: and setting optimization parameters, wherein the optimization parameters comprise a layering proportion, a transmission proportion and iteration times.
According to an embodiment of the present disclosure, randomly encoding the user information to form an initial population set includes: setting at least one constraint function according to a preset driving condition; and obtaining a plurality of individual information subsets meeting the constraint function based on the constraint function and the user information.
According to the embodiment of the present disclosure, for each individual information subset, calculating the fitness of the individual information subset according to the current information of the user and the corresponding path information, and obtaining a plurality of fitness includes: decoding the current information of the user to obtain first user information, wherein the first user information comprises a user identifier and a user position coordinate; setting a fitness function based on the driving path optimization target; and calculating the fitness of each individual information subset based on each first user information in each individual information subset and a fitness function to obtain a plurality of fitness.
According to an embodiment of the present disclosure, layering the plurality of individual information subsets according to the optimization parameters and the fitness includes: ranking the fitness degrees according to a ranking function; dividing the sorted fitness into a plurality of layers according to the layering proportion; and dividing the plurality of individual information subsets corresponding to the fitness into a plurality of layers according to the layered fitness.
According to an embodiment of the present disclosure, the plurality of layers comprises, from bottom to top, an exploration layer, a first delivery layer, a second delivery layer and a mining layer, the exploration layer comprising a first hierarchal proportion of the subset of individual information, the first delivery layer comprising a second hierarchal proportion of the subset of individual information, the second delivery layer comprising a third hierarchal proportion of the subset of individual information and the mining layer comprising a fourth hierarchal proportion of the subset of individual information.
According to the embodiment of the present disclosure, according to the hierarchy where a plurality of individual information subsets are located, optimizing the corresponding path information in the individual information subsets, and obtaining the optimized corresponding path information includes: respectively optimizing the individual information subsets at each hierarchy according to optimization strategies respectively determined for the plurality of hierarchies to obtain a plurality of optimized individual information subsets; calculating the fitness of the optimized plurality of individual information subsets according to a fitness function; calculating the transmission probability of the optimized individual information subset according to the fitness, wherein the transmission probability is used for representing the probability of the optimized individual information subset being transmitted to the upper level; sorting the delivery probabilities; and performing cross-layer level transmission on the optimized individual information subsets of the layers according to the ordered transmission probability and transmission proportion to obtain optimized corresponding path information.
According to an embodiment of the present disclosure, the optimization strategy comprises at least one of: the first optimization strategy comprises the steps that a first optimization operator is adopted for the exploration layer and the first transmission layer, a second optimization operator is adopted for the second transmission layer and the exploitation layer, and the optimization amplitude of the first optimization operator to the corresponding path information in the optimization process is larger than that of the second optimization operator to the corresponding path information in the optimization process; and the second optimization strategy comprises the steps of aiming at the corresponding path information in a plurality of individual information subsets in the second transmission layer and the mining layer which execute the first optimization strategy, accelerating the corresponding path information in the excellent individual information subset by using the corresponding path information in the individual information subset corresponding to the first fitness, and optimizing the corresponding path information in the inferior individual information subset by using the insertion strategy, wherein the first fitness comprises the fitness positioned at the head after the ordering.
According to an embodiment of the present disclosure, accelerating the corresponding path information in the good subset of individual information using the corresponding path information in the subset of individual information corresponding to the first fitness includes: the second transfer layer uses the corresponding path information in the individual information subset corresponding to the current first applicability to perform acceleration operation, and the mining layer uses the corresponding path information in the individual information subset corresponding to the global first applicability to perform acceleration operation, where the acceleration operation includes: and optimizing the corresponding path information to be optimized by using an optimization operator according to the corresponding path information in the individual information subset corresponding to the first applicability.
According to the embodiment of the disclosure, optimizing the corresponding path information in the inferior-eliminated individual information subset by using the insertion strategy comprises: removing at least one element in the corresponding path information in the rejected first optimized individual information subset to obtain corresponding path information in the first individual information subset; for a removed element, inserting the removed element into different positions in corresponding path information in the first individual information subset respectively; respectively calculating the fitness corresponding to the corresponding path information in the first individual information subset after the insertion into different positions according to the fitness function; determining the insertion position corresponding to the first fitness as the insertion position of the element; and obtaining the corresponding path information in the optimized individual information subset until all the removed elements are inserted.
According to an embodiment of the present disclosure, determining a travel path according to the optimized corresponding path information includes: and in response to determining that the end condition is met, determining the corresponding path information in the individual information subset corresponding to the first fitness as the driving path, wherein the end condition comprises determining that the current iteration number is equal to the iteration number.
Another aspect of the present disclosure provides an apparatus for determining a travel path, including: the acquisition module is used for acquiring user information; the encoding module is used for randomly encoding the user information to form an initial population set, the initial population set comprises a plurality of individual information subsets, and each individual information subset comprises a plurality of user current information and corresponding path information; the calculation module is used for calculating the fitness of each individual information subset according to the current information of the user and the corresponding path information to obtain a plurality of fitness; the layering module is used for layering the plurality of individual information subsets according to the optimization parameters and the fitness; the optimization module is used for optimizing corresponding path information in the individual information subsets according to the levels of the individual information subsets to obtain optimized corresponding path information; and the determining module is used for determining a driving path according to the optimized corresponding path information.
Another aspect of the present disclosure provides an electronic device including: one or more processors; a memory for storing one or more programs, wherein the one or more programs, when executed by the one or more processors, cause the one or more processors to perform the above-described method of determining a travel path.
Another aspect of the present disclosure also provides a computer-readable storage medium having stored thereon executable instructions that, when executed by a processor, cause the processor to perform the above-described method of determining a travel path.
Another aspect of the present disclosure also provides a computer program product comprising a computer program which, when executed by a processor, implements the above-described method of determining a travel path.
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The foregoing and other objects, features and advantages of the disclosure will be apparent from the following description of embodiments of the disclosure, which proceeds with reference to the accompanying drawings, in which:
fig. 1 schematically illustrates an application scenario diagram of a method, an apparatus, an electronic device, a storage medium, and a program product for determining a travel path according to embodiments of the present disclosure;
FIG. 2 schematically illustrates a flow chart of a travel path method according to an embodiment of the disclosure;
FIG. 3 schematically illustrates a layering method employed in accordance with an embodiment of the present disclosure;
FIG. 4 schematically illustrates a flow chart of a travel path method according to another embodiment of the present disclosure;
FIG. 5 schematically illustrates a schematic diagram of an acceleration operation employed in accordance with an embodiment of the present disclosure;
FIG. 6 schematically illustrates an insertion strategy employed in accordance with an embodiment of the present disclosure;
fig. 7 is a block diagram schematically illustrating a structure of a device for determining a travel path according to an embodiment of the present disclosure; and
fig. 8 schematically shows a block diagram of an electronic device adapted to implement a method of determining a travel path according to an embodiment of the disclosure.
Detailed Description
Hereinafter, embodiments of the present disclosure will be described with reference to the accompanying drawings. It should be understood that the description is illustrative only and is not intended to limit the scope of the present disclosure. In the following detailed description, for purposes of explanation, numerous specific details are set forth in order to provide a thorough understanding of the embodiments of the disclosure. It may be evident, however, that one or more embodiments may be practiced without these specific details. Moreover, in the following description, descriptions of well-known structures and techniques are omitted so as to not unnecessarily obscure the concepts of the present disclosure.
The terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting of the disclosure. The terms "comprises," "comprising," and the like, as used herein, specify the presence of stated features, steps, operations, and/or components, but do not preclude the presence or addition of one or more other features, steps, operations, or components.
All terms (including technical and scientific terms) used herein have the same meaning as commonly understood by one of ordinary skill in the art unless otherwise defined. It is noted that the terms used herein should be interpreted as having a meaning that is consistent with the context of this specification and should not be interpreted in an idealized or overly formal sense.
Where a convention analogous to "at least one of A, B and C, etc." is used, in general such a construction is intended in the sense one having skill in the art would understand the convention (e.g., "a system having at least one of A, B and C" would include but not be limited to systems that have a alone, B alone, C alone, a and B together, a and C together, B and C together, and/or A, B, C together, etc.).
The embodiment of the disclosure provides a method for determining a driving path, which includes randomly encoding user information to form an initial population set, wherein the initial population set includes a plurality of individual information subsets, and each individual information subset includes a plurality of user current information and corresponding path information; aiming at each individual information subset, calculating the fitness of the individual information subset according to the current information of the user and the corresponding path information to obtain a plurality of fitness; layering the plurality of individual information subsets according to the optimization parameters and the fitness; optimizing corresponding path information in a plurality of individual information subsets according to the levels of the individual information subsets to obtain optimized corresponding path information; and determining a driving path according to the optimized corresponding path information.
Fig. 1 schematically illustrates an application scenario diagram for determining a travel path according to an embodiment of the present disclosure.
As shown in fig. 1, the application scenario 100 according to this embodiment may include terminal devices 101, 102, 103, a network 104 and a server 105. The network 104 serves as a medium for providing communication links between the terminal devices 101, 102, 103 and the server 105. Network 104 may include various connection types, such as wired, wireless communication links, or fiber optic cables, to name a few.
The user may use the terminal devices 101, 102, 103 to interact with the server 105 via the network 104 to receive or send messages or the like. The terminal devices 101, 102, 103 may have installed thereon various communication client applications, such as shopping-like applications, web browser applications, search-like applications, instant messaging tools, mailbox clients, social platform software, etc. (by way of example only).
The terminal devices 101, 102, 103 may be various electronic devices having a display screen and supporting web browsing, including but not limited to smart phones, tablet computers, laptop portable computers, desktop computers, and the like.
The server 105 may be a server providing various services, such as a background management server (for example only) providing support for websites browsed by users using the terminal devices 101, 102, 103. The background management server may analyze and perform other processing on the received data such as the user request, and feed back a processing result (e.g., a webpage, information, or data obtained or generated according to the user request) to the terminal device.
It should be noted that the method for determining the travel path provided by the embodiment of the present disclosure may be generally executed by the server 105. Accordingly, the apparatus for determining a travel path provided by the embodiments of the present disclosure may be generally disposed in the server 105. The method for determining a driving path provided by the embodiment of the present disclosure may also be performed by a server or a server cluster which is different from the server 105 and can communicate with the terminal devices 101, 102, 103 and/or the server 105. Accordingly, the device for determining a driving path provided by the embodiment of the present disclosure may also be disposed in a server or a server cluster different from the server 105 and capable of communicating with the terminal devices 101, 102, 103 and/or the server 105.
It should be understood that the number of terminal devices, networks, and servers in fig. 1 is merely illustrative. There may be any number of terminal devices, networks, and servers, as desired for implementation.
The method of determining a travel path of the disclosed embodiment will be described in detail through fig. 2 to 6 based on the scenario described in fig. 1.
Fig. 2 schematically shows a flow chart of a method of determining a travel path according to an embodiment of the disclosure.
As shown in fig. 2, the method of determining a travel path of the embodiment includes operations S210 to S260.
In operation S210, user information is acquired.
According to the embodiment of the disclosure, the user information is determined according to the service type. For example, when applied to the problem of determining a travel path, the user information may include at least one of: the current location of the user, the service time window of the user, the cargo type of the user, and the cargo weight of the user.
In operation S220, the user information is randomly encoded to form an initial population set, where the initial population set includes a plurality of individual information subsets, and each individual information subset includes a plurality of user current information and corresponding path information.
According to the embodiment of the disclosure, all users are randomly coded, and each user corresponds to an integer identifier x after codingi，xiE (1, 2, 3, …, n), the user and user information is associated with the user's integer identification. Randomly generating a plurality of individual information subsets (X) for integer identification of a useri，Xi＝(x1，x2，x3，…，xn) Each subset of individual information includes a plurality of users and corresponding path information. For example, individual information subset Xi＝(x1，x2，x3) Indicating that the path includes three users (x)1、x2And x3) And corresponding path information (x)1→x2→x3). All randomly generated subsets of individual information form an initial population set (X, X ═ X1，X2，X3，…，Xn)). For example, in the example of 5 users, the users are randomly coded as 1, 2, 3, 4, and 5. Multiple subsets of individual information may be randomly generated for 5 users. For example, X may be1＝(1，2，3)、X2(5, 2, 3) and X3All randomly generated individual information subsets form an initial population set X ═ (X), and so on (1, 4, 3)1，X2，X3，…，Xn)。
In operation S230, for each individual information subset, the fitness of the individual information subset is calculated according to the current information of the user and the corresponding path information, so as to obtain a plurality of fitness.
According to the embodiment of the disclosure, the fitness is determined according to the optimization target of the service. The suitability degree is an index for evaluating the degree of goodness or badness of each individual information subset. For example, in solving the problem of the shortest travel path, the course may be selected as the fitness. And calculating the route corresponding to each individual information subset according to the current information of the user and the corresponding path information contained in each individual information subset, wherein the route is called the fitness of the individual information subset. The current information of the user may be the current location of the user.
In operation S240, a plurality of individual information subsets are layered according to the optimization parameters and the fitness.
According to the embodiment of the disclosure, a plurality of fitness degrees are ranked according to a ranking function; dividing the sorted fitness into a plurality of layers according to the layering proportion; and dividing a plurality of individual information subsets corresponding to the fitness into a plurality of layers according to the layered fitness.
And ranking the fitness calculated by each individual information subset according to a ranking function, wherein the ranking function comprises the following steps: ascending and descending. Dividing the sorted fitness into a plurality of layers according to the layering proportion; and then according to the layer with each fitness after layering, dividing the individual information subset corresponding to each fitness into the layer. The layering proportion comprises a plurality of different percentages, each percentage represents the proportion of the number of the individual information subsets contained in each layer to the number of the individual information subsets in the population set, and the sum of the percentages is 1. The number of the fraction containing percentage is the number of layers that the initial species are clustered into. For example, the hierarchy ratio is (50%, 25%, 15%, 10%), which includes four percentages, so that the individual information subset in the initial population set is divided into four layers, each layer accounting for 50%, 25%, 15%, and 10%, respectively.
In operation S250, the corresponding path information in the individual information subsets is optimized according to the hierarchy where the individual information subsets are located, so as to obtain optimized corresponding path information.
According to the embodiment of the disclosure, for each layer separated from the initial population set, an optimization operator for optimizing the individual information subset contained in each layer is respectively set. And optimizing the corresponding path information in the individual information subset contained in each layer according to the optimization operators respectively arranged on each layer to obtain the optimized corresponding path information.
And calculating the fitness of the optimized corresponding path information, and sequencing the calculated fitness. And selecting a plurality of optimized corresponding path information to be transmitted to the upper layer for optimization according to the transmission proportion and the sequenced fitness, and obtaining the optimized corresponding path information through optimization. The transfer ratio is the ratio of the number of the individual information subsets which can be transferred to the upper layer in each layer to the total number of the individual information subsets in the layer.
The optimization operator is a method for solving an optimal solution in a business, and specifically may include: a reversal operator, a random two-point transposition operator and the like. Taking the travel path problem as an example, the step of inverting operator optimization comprises: and randomly selecting two elements in the corresponding path information, carrying out reverse order arrangement (original order before optimization is arranged in a positive order) on the elements between the two selected elements, and exchanging the positions of the two selected elements to finally obtain the optimized corresponding path information. The random two-point transposition operator optimization method comprises the following steps: and randomly selecting two elements in the corresponding path information, and interchanging the positions of the two selected elements to obtain the optimized corresponding path information.
In operation S260, a travel path is determined according to the optimized corresponding path information.
According to the embodiment of the disclosure, the fitness of the optimized corresponding path information is calculated, and the calculated fitness is ranked. And determining the corresponding path information corresponding to the first fitness as a driving path. The first fitness is the fitness at the top of the ranking. For example, if the optimized target of the driving path is the shortest path and the fitness is arranged from small to large, the first fitness is the fitness (the smallest fitness) at the top after all the fitness is arranged.
FIG. 3 schematically illustrates a layering method according to an embodiment of the disclosure.
According to another embodiment, as shown in fig. 3, in a four-tier example, individual subsets of information within an initial population set are divided from bottom to top into an exploration tier 340, a first delivery tier 330, a second delivery tier 320, and a mining tier 310. Taking the hierarchical ratio (50%, 25%, 15%, 10%) as an example, the hierarchical ratio indicates that the exploration layer 340 contains 50% of the individual information subset of the initial population set, the first delivery layer 330 contains 25% of the individual information subset of the initial population set, and the second delivery layer 320 contains 15% of the individual information subset of the initial population set; the mining layer 310 contains a subset of the individual information that is 10% of the initial population set.
Assuming that smaller fitness values represent better paths, the fitness may be ranked from small to large. For the ranked fitness, dividing the fitness positioned at the last 50% into an exploration layer 340, and further dividing an individual information subset corresponding to the fitness positioned at the last 50% into the exploration layer 340; dividing the fitness from the first 25% to the first 50% into the first transmission layer 330, and further dividing the individual information subset corresponding to the fitness from the first 25% to the first 50% into the first transmission layer 330; dividing the fitness from the first 10% to the first 25% into the second transmission layer 320, and further dividing the individual information subset corresponding to the fitness from the first 10% to the first 25% into the second transmission layer 320; the fitness in the top 10% is divided into the mining layers 310, and then the individual information subset corresponding to the fitness in the top 10% is also divided into the mining layers 310.
It will be appreciated by those skilled in the art that the foregoing examples are merely illustrative and that the number of layers in which the particular layering method of the present disclosure is based is not limited thereto.
According to another embodiment, the randomly encoding the user information to form the initial population set may include the following operations.
Setting at least one constraint function according to a preset driving condition; and obtaining a plurality of individual information subsets meeting the constraint function based on the constraint function and the user information.
The running condition includes a constraint condition to which the vehicle is subjected during running. For example, the constraints of the driving conditions during driving of the vehicle may include the longest distance the vehicle travels, the maximum speed of the vehicle during driving, the service time window requirements of the user, and the maximum weight the vehicle may be equipped with, among other things. Constraint functions are respectively set according to the driving conditions. And respectively verifying whether each randomly generated individual information subset meets the driving condition by using a constraint function, wherein the individual information subsets meeting the driving condition form an initial population set.
According to another embodiment, the calculating the fitness of each individual information subset according to the current information of the user and the corresponding path information may include the following operations.
And decoding the current information of the user to obtain first user information, wherein the first user information comprises a user identifier and a user position coordinate. Setting a fitness function based on an optimization target of the driving path; and calculating the fitness of each individual information subset based on each first user information in each individual information subset and the fitness function to obtain a plurality of fitness. For example, taking the travel route optimization target as the shortest travel route as an example, the route is selected as the fitness. Setting a vehicle starting position as a coordinate origin, determining the position coordinate of the user according to the relation between the real-time position in each user information and the vehicle starting position, and associating the coordinate with an integer identifier formed after the user codes to ensure that the integer identifier of one user corresponds to one coordinate. And determining a function for calculating the route according to the user coordinates and the corresponding route information, wherein the function is called a fitness function. The fitness of each individual information subset is calculated based on a fitness function.
By combining the pyramid evolution algorithm and the intelligent optimization algorithm to optimize the corresponding path information in the individual information subset consisting of the users, the problem that competition and cooperation between individuals and between groups cannot be considered simultaneously when the driving path is solved, and therefore the effects of high solving precision and high convergence speed are achieved.
Fig. 4 schematically shows a flow chart for determining a travel path according to another embodiment of the present disclosure.
As shown in fig. 4, the method of determining a travel path of the embodiment includes operations S410 to S480.
In operation S410, optimization parameters including a layering ratio, a transfer ratio, and an iteration number are set.
According to the embodiment of the disclosure, before determining the driving path, the optimization parameters are set. The optimization parameters may include the size of the initial population set, the stratification ratio, the transfer ratio, the maximum load of the vehicle, the vehicle speed, the number of iterations, and the like.
In operation S420, the user information is randomly encoded to form an initial population set, where the initial population set includes a plurality of individual information subsets, and each individual information subset includes a plurality of user current information and corresponding path information.
This operation S420 may perform random encoding to form an initial population set by a method similar to the method described in the previous operation S220. And will not be described in detail herein.
In operation S430, for each individual information subset, the fitness of the individual information subset is calculated according to the current information of the user and the corresponding path information, so as to obtain a plurality of fitness.
This operation S430 may calculate the fitness of the individual information subset by a method similar to the method described in the foregoing operation S230. And will not be described in detail herein.
In operation S440, a plurality of individual information subsets are layered according to the optimization parameters and the fitness.
This operation S440 may layer the plurality of individual information subsets by a method similar to the method described in the foregoing operation S240. And will not be described in detail herein.
In operation S450, the individual information subsets at each level are optimized according to the optimization strategies respectively determined for the plurality of levels, so as to obtain a plurality of optimized individual information subsets.
In accordance with an embodiment of the present disclosure, in a four-tier example, optimization strategies are formulated separately for an exploration tier, a first delivery tier, a second delivery tier, and a production tier. And optimizing the corresponding path information in the individual information subsets in the four layers according to an optimization strategy to obtain the optimized corresponding path information after optimization. The optimization strategy includes at least one of a first optimization strategy and a second optimization strategy.
According to another embodiment, the first optimization strategy includes optimizing the exploration layer and the first transport layer using a first optimization operator, and optimizing the second transport layer and the exploitation layer using a second optimization operator. It should be noted that, when the first optimization strategy is set, the first disturbance amplitude is larger than the second disturbance amplitude. The first disturbance amplitude is the optimized amplitude of the first optimization operator to the corresponding path information in the process of executing the optimization operation, and the second disturbance amplitude is the optimized amplitude of the second optimization operator to the corresponding path information in the process of executing the optimization operation. For example, the exploration layer and the first transfer layer are optimized by adopting an inversion operator, and the second transfer layer and the exploitation layer are optimized by adopting a random two-point transposition operator.
The second optimization strategy comprises accelerating the corresponding path information in the second transfer layer which has executed the first optimization strategy by using the corresponding path information in the individual information subset corresponding to the current first applicability; accelerating the corresponding path information in the mining layer which has executed the first optimization strategy by using the corresponding path information in the individual information subset corresponding to the global first applicability; and optimizing corresponding path information in the rejected individual information subset by using an insertion strategy. The first fitness comprises the fitness at the head of the ranking. For example, if the optimized target of the driving path is the shortest path and the fitness is arranged from small to large, the first fitness is the fitness (the smallest fitness) at the top after all the fitness is arranged.
In operation S460, cross-layer level transmission is performed on the optimized individual information subsets of the multiple layers according to the transmission probability and the transmission ratio, so as to obtain optimized corresponding path information.
According to the embodiment of the present disclosure, the fitness of the optimized plurality of individual information subsets obtained in step S450 is calculated. And calculating the transmission probability of the optimized individual information subset according to the calculated fitness. The delivery probability represents the probability of the optimized subset of individual information being delivered to the upper level. For example, the calculation formula of the transmission probability is a quotient of a first value and a second value, where the first value is the fitness corresponding to the current individual information subset, and the second value is the sum of the fitness of the hierarchy where the current individual information subset is located. And solving the transmission probability of the optimized individual information subsets according to the transmission probability calculation formula.
And sequencing the plurality of calculated transfer probabilities. And carrying out cross-layer level transmission on a plurality of individual information subsets of a plurality of layers according to the ordered transmission probability and transmission proportion to obtain optimized corresponding path information. For example, in the example of four layers, the transfer proportion is (50%, 20%, 10%), the transfer proportion represents that 50% of the individual information subsets selected in the exploration layer are transferred to the first transfer layer, and the selected 50% of the individual information subsets are corresponding individual information subsets with the transfer probability ordering being located at the top 50%; transmitting 20% of the individual information subsets selected in the first transmission layer to the second transmission layer, wherein the selected 20% of the individual information subsets are combined into corresponding individual information subsets with the transmission probability sequence of 50% at the top; and transmitting 10% of the individual information subsets selected in the second transmission layer to the mining layer, wherein the selected 100% of the individual information subsets are combined into corresponding individual information subsets with the transmission probability ordering positioned in the top 50%. And optimizing the individual information subset in the new hierarchy formed after the cross-hierarchy transmission according to the optimization strategy of the hierarchy, and obtaining optimized corresponding path information through optimization.
In operation S470, it is determined whether an end condition is satisfied.
If the end condition is not satisfied, the process returns to operation S430.
If the end condition is satisfied, operation S480 is performed to determine a driving route according to the optimized corresponding route information.
According to the embodiment of the present disclosure, the ending condition may be at least one of the following, the current iteration number is equal to the iteration number set in the optimization parameter, and the optimization result at this time converges. For example, taking the current iteration number equal to the iteration number set in the optimization parameter as an example, the iteration number set in the optimization parameter is 500, if the currently executed iteration number is less than 500, the step returns to the step S430 to continue the iteration process, and if the currently executed iteration number is 500, the operation S480 is executed.
And when the end condition is met, determining the corresponding path information in the individual information subset corresponding to the first fitness as the driving path. The first fitness is the fitness at the top of the ranking. For example, if the optimized target of the driving path is the shortest path and the fitness is arranged from small to large, the first fitness is the fitness (the smallest fitness) at the top after all the fitness is arranged.
FIG. 5 schematically illustrates a schematic diagram of an acceleration operation employed in accordance with an embodiment of the present disclosure.
According to another embodiment, as shown in fig. 5, 510 represents the corresponding path information in the subset of individual information to be optimized, and 520 represents the corresponding path information in the subset of individual information to which the first fitness corresponds. For simplicity, the corresponding path information in the individual information subset is hereinafter represented by path information. For example, the start bit of the optimization is the position of the element 1 of the path information 510 to be optimized, that is, the position of the element 2 in the optimized path information corresponding to the first fitness. The optimization end bit is the position of element 3 of the path information 510 to be optimized, that is, the position of element 1 in the path information corresponding to the first fitness.
And starting optimization operation from the start bit, and exchanging the start bit element of the path information to be optimized with the start bit element of the path information corresponding to the first fitness. For example, element 1 of the path information 510 to be optimized is exchanged with element 2 of the optimized path information 520 corresponding to the first fitness, the path information to be optimized after the element exchange becomes (2, 2, 3, 4, 5), and the path information corresponding to the first fitness becomes (5, 1, 1, 4, 3). And determining the same element originally existing in the path information as the swapped-in element, and swapping the element for the element used for swapping by the path information. For example, the original element 2 in the path information 510 to be optimized is changed to element 1, the path information 510 to be optimized after the element change is changed to the first optimized path information 530(1, 2, 3, 4, 5), and the path information 520 corresponding to the first fitness is changed to the path information 540(5, 1, 2, 4, 3) corresponding to the first fitness of the first optimization.
The remaining elements in the path information to be optimized 510 are optimized using the same operations as the start bit element operations above. After the optimization, the first optimized path information 530 becomes second optimized path information 550(1, 3, 2, 4, 5), and the path information 540 corresponding to the first fitness of the first optimization becomes path information 560(5, 1, 3, 4, 2) corresponding to the first fitness of the second optimization. Similarly, after re-optimization, the second optimized path information 550 becomes optimized path information 570(1, 3, 2, 4, 5).
Those skilled in the art will appreciate that the above embodiments are exemplary only, and that the particular path accelerated by the present disclosure is not limited thereto.
Fig. 6 schematically illustrates an insertion strategy employed in accordance with an embodiment of the present disclosure.
According to another embodiment, the insertion policy comprises: removing at least one element in the corresponding path information in the rejected first optimized individual information subset to obtain corresponding path information in the first individual information subset; aiming at the removed element, respectively inserting the element into different positions in the corresponding path information in the first individual information subset; respectively calculating the fitness corresponding to the corresponding path information in the first individual information subset after the individual path information is inserted into different positions according to a fitness function; determining an insertion position corresponding to the first fitness as an insertion position of the element; and obtaining the corresponding path information in the optimized individual information subset until all the removed elements are inserted.
As shown in fig. 6, 610 indicates bad-eliminated path information, and element 5 and element 6 in the path information 610 are removed, and the path information 610 becomes path information 620(3, 2, 1).
First, for element 5, element 5 is inserted into different positions in path information 620(3, 2, 1), that is, element 5 is inserted into each virtual frame position in path information 630 before and after element 5, and the fitness of the path information formed after element 5 is inserted into each position is calculated, that is, each virtual frame position in path information 630 forms new path information, and the fitness of all the formed new path information is calculated. If the fitness calculated is the first fitness when element 5 is inserted between element 3 and element 2 (e.g., path information 640), then determination 640 represents the first optimized path information at this point. Similarly, the element 6 is inserted into the first optimized path information 640 according to the above operation, and the optimized path information 650(3, 5, 2, 6, 1) is obtained.
Those skilled in the art will appreciate that the above embodiments are merely examples, and that the specific path of the present disclosure using an insertion strategy is not limited thereto.
Based on the method for determining the driving path, the disclosure also provides a device for determining the driving path. The apparatus will be described in detail below with reference to fig. 7.
Fig. 7 schematically shows a block diagram of a device for determining a travel path according to an embodiment of the present disclosure.
As shown in fig. 7, the apparatus 700 for determining a performance path of the embodiment includes an obtaining module 710, an encoding module 720, a calculating module 730, a layering module 740, an optimizing module 750, and a determining module 760.
The obtaining module 710 is used for obtaining the user information. In an embodiment, the obtaining module 710 may be configured to perform the operation S210 described above, which is not described herein again.
According to an embodiment of the present disclosure, determining the travel path 700 further includes configuring an optimization parameter module, where the optimization parameter includes a layering ratio, a transfer ratio, and an iteration number.
The encoding module 720 is configured to randomly encode the user information to form an initial population set, where the initial population set includes a plurality of individual information subsets, and each individual information subset includes a plurality of user current information and corresponding path information. In an embodiment, the encoding module 720 may be configured to perform the operation S220 described above, which is not described herein again.
According to an embodiment of the present disclosure, the current determined travel path encoding module 720 is further configured to: setting at least one constraint function according to a preset driving condition; and obtaining a plurality of individual information subsets meeting the constraint function based on the constraint function and the user information.
The calculating module 730 is configured to calculate, for each individual information subset, the fitness of the individual information subset according to the current information of the user and the corresponding path information, so as to obtain a plurality of fitness. In an embodiment, the calculating module 730 can be configured to perform the operation S230 described above, which is not described herein again.
According to an embodiment of the present disclosure, the currently determined travel path calculation module 730 is further configured to: decoding current information of a user to obtain first user information, wherein the first user information comprises a user identifier and a user position coordinate; setting a fitness function based on the driving path optimization target; and calculating the fitness of each individual information subset based on each first user information in each individual information subset and the fitness function to obtain a plurality of fitness.
The layering module 740 is configured to layer the plurality of individual information subsets according to the optimization parameters and the fitness. In an embodiment, the layering module 740 may be configured to perform the operation S240 described above, which is not described herein again.
According to an embodiment of the present disclosure, the current determination of travel path tier module 740 is further configured to: sorting the fitness degrees according to a sorting function; dividing the sorted fitness into a plurality of layers according to the layering proportion; and dividing a plurality of individual information subsets corresponding to the fitness into a plurality of layers according to the layered fitness.
The optimizing module 750 is configured to optimize the corresponding path information in the individual information subsets according to the hierarchy where the individual information subsets are located, so as to obtain optimized corresponding path information, and in an embodiment, the optimizing module 750 may be configured to perform the operation S250 described above, which is not described herein again.
According to an embodiment of the present disclosure, the current determined travel path optimization module 750 is further configured to: respectively optimizing the individual information subsets at each hierarchy according to optimization strategies respectively determined for a plurality of hierarchies to obtain a plurality of optimized individual information subsets; calculating the fitness of the optimized individual information subsets according to a fitness function; calculating the transmission probability of the optimized individual information subset according to the fitness, wherein the transmission probability is used for representing the probability of the optimized individual information subset being transmitted to the upper level; sequencing the transmission probabilities; and performing cross-layer transmission on a plurality of optimized individual information subsets of a plurality of layers according to the ordered transmission probability and transmission proportion to obtain optimized corresponding path information.
The determining module 760 is configured to determine a driving route according to the corresponding route information. In an embodiment, the determining module 760 may be configured to perform the operation S260 described above, which is not described herein again.
According to an embodiment of the present disclosure, the current determined travel path determination module 760 is further configured to: and in response to determining that the end condition is met, determining the corresponding path information in the individual information subset corresponding to the first fitness as the driving path, wherein the end condition comprises determining that the current iteration number is equal to the iteration number.
According to an embodiment of the present disclosure, any plurality of the obtaining module 710, the encoding module 720, the calculating module 730, the layering module 740, the optimizing module 750, and the determining module 760 may be combined into one module to be implemented, or any one of the modules may be split into a plurality of modules. Alternatively, at least part of the functionality of one or more of these modules may be combined with at least part of the functionality of the other modules and implemented in one module. According to an embodiment of the disclosure, at least one of the obtaining module 710, the encoding module 720, the calculating module 730, the layering module 740, the optimizing module 750, and the determining module 760 may be implemented at least in part as a hardware circuit, such as a Field Programmable Gate Array (FPGA), a Programmable Logic Array (PLA), a system on a chip, a system on a substrate, a system on a package, an Application Specific Integrated Circuit (ASIC), or may be implemented by hardware or firmware in any other reasonable manner of integrating or packaging a circuit, or implemented by any one of three implementations of software, hardware, and firmware, or any suitable combination of any of them. Alternatively, at least one of the obtaining module 710, the encoding module 720, the calculating module 730, the layering module 740, the optimizing module 750, and the determining module 760 may be at least partially implemented as a computer program module, which when executed, may perform a corresponding function.
Fig. 8 schematically shows a block diagram of an electronic device adapted to implement a method of determining a travel path according to an embodiment of the disclosure.
As shown in fig. 8, an electronic device 800 according to an embodiment of the present disclosure includes a processor 801 that can perform various appropriate actions and processes according to a program stored in a Read Only Memory (ROM)802 or a program loaded from a storage section 808 into a Random Access Memory (RAM) 803. The processor 801 may include, for example, a general purpose microprocessor (e.g., CPU), an instruction set processor and/or associated chipset, and/or a special purpose microprocessor (e.g., Application Specific Integrated Circuit (ASIC)), among others. The processor 801 may also include onboard memory for caching purposes. The processor 801 may include a single processing unit or multiple processing units for performing different actions of the method flows according to embodiments of the present disclosure.
In the RAM 803, various programs and data necessary for the operation of the electronic apparatus 800 are stored. The processor 801, the ROM 802, and the RAM 803 are connected to each other by a bus 804. The processor 801 performs various operations of the method flows according to the embodiments of the present disclosure by executing programs in the ROM 802 and/or RAM 803. Note that the programs may also be stored in one or more memories other than the ROM 802 and RAM 803. The processor 801 may also perform various operations of method flows according to embodiments of the present disclosure by executing programs stored in the one or more memories.
Electronic device 800 may also include input/output (I/O) interface 805, input/output (I/O) interface 805 also connected to bus 804, according to an embodiment of the present disclosure. Electronic device 800 may also include one or more of the following components connected to I/O interface 805: an input portion 806 including a keyboard, a mouse, and the like; an output section 807 including a signal such as a Cathode Ray Tube (CRT), a Liquid Crystal Display (LCD), and the like, and a speaker; a storage portion 808 including a hard disk and the like; and a communication section 809 including a network interface card such as a LAN card, a modem, or the like. The communication section 809 performs communication processing via a network such as the internet. A drive 810 is also connected to the I/O interface 805 as necessary. A removable medium 811 such as a magnetic disk, an optical disk, a magneto-optical disk, a semiconductor memory, or the like is mounted on the drive 810 as necessary, so that a computer program read out therefrom is mounted on the storage section 808 as necessary.
The present disclosure also provides a computer-readable storage medium, which may be contained in the apparatus/device/system described in the above embodiments; or may exist separately and not be assembled into the device/apparatus/system. The computer-readable storage medium carries one or more programs which, when executed, implement the method according to an embodiment of the disclosure.
According to embodiments of the present disclosure, the computer-readable storage medium may be a non-volatile computer-readable storage medium, which may include, for example but is not limited to: a portable computer diskette, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing. In the present disclosure, a computer readable storage medium may be any tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, apparatus, or device. For example, according to embodiments of the present disclosure, a computer-readable storage medium may include the ROM 802 and/or RAM 803 described above and/or one or more memories other than the ROM 802 and RAM 803.
Embodiments of the present disclosure also include a computer program product comprising a computer program containing program code for performing the method illustrated in the flow chart. When the computer program product runs in a computer system, the program code is used for causing the computer system to realize the item recommendation method provided by the embodiment of the disclosure.
The computer program performs the above-described functions defined in the system/apparatus of the embodiments of the present disclosure when executed by the processor 801. The systems, apparatuses, modules, units, etc. described above may be implemented by computer program modules according to embodiments of the present disclosure.
In one embodiment, the computer program may be hosted on a tangible storage medium such as an optical storage device, a magnetic storage device, or the like. In another embodiment, the computer program may also be transmitted in the form of a signal on a network medium, distributed, downloaded and installed via communication section 809, and/or installed from removable media 811. The computer program containing program code may be transmitted using any suitable network medium, including but not limited to: wireless, wired, etc., or any suitable combination of the foregoing.
In such an embodiment, the computer program can be downloaded and installed from a network through the communication section 809 and/or installed from the removable medium 811. The computer program, when executed by the processor 801, performs the above-described functions defined in the system of the embodiments of the present disclosure. The systems, devices, apparatuses, modules, units, etc. described above may be implemented by computer program modules according to embodiments of the present disclosure.
In accordance with embodiments of the present disclosure, program code for executing computer programs provided by embodiments of the present disclosure may be written in any combination of one or more programming languages, and in particular, these computer programs may be implemented using high level procedural and/or object oriented programming languages, and/or assembly/machine languages. The programming language includes, but is not limited to, programming languages such as Java, C + +, python, the "C" language, or the like. The program code may execute entirely on the user computing device, partly on the user device, partly on a remote computing device, or entirely on the remote computing device or server. In the case of a remote computing device, the remote computing device may be connected to the user computing device through any kind of network, including a Local Area Network (LAN) or a Wide Area Network (WAN), or may be connected to an external computing device (e.g., through the internet using an internet service provider).
The flowchart and block diagrams in the figures illustrate the architecture, functionality, and operation of possible implementations of systems, methods and computer program products according to various embodiments of the present disclosure. In this regard, each block in the flowchart or block diagrams may represent a module, segment, or portion of code, which comprises one or more executable instructions for implementing the specified logical function(s). It should also be noted that, in some alternative implementations, the functions noted in the block may occur out of the order noted in the figures. For example, two blocks shown in succession may, in fact, be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending upon the functionality involved. It will also be noted that each block of the block diagrams or flowchart illustration, and combinations of blocks in the block diagrams or flowchart illustration, can be implemented by special purpose hardware-based systems which perform the specified functions or acts, or combinations of special purpose hardware and computer instructions.
Those skilled in the art will appreciate that various combinations and/or combinations of features recited in the various embodiments and/or claims of the present disclosure can be made, even if such combinations or combinations are not expressly recited in the present disclosure. In particular, various combinations and/or combinations of the features recited in the various embodiments and/or claims of the present disclosure may be made without departing from the spirit or teaching of the present disclosure. All such combinations and/or associations are within the scope of the present disclosure.
The embodiments of the present disclosure have been described above. However, these examples are for illustrative purposes only and are not intended to limit the scope of the present disclosure. Although the embodiments are described separately above, this does not mean that the measures in the embodiments cannot be used in advantageous combination. The scope of the disclosure is defined by the appended claims and equivalents thereof. Various alternatives and modifications can be devised by those skilled in the art without departing from the scope of the present disclosure, and such alternatives and modifications are intended to be within the scope of the present disclosure.

Claims (15)

1. A method of determining a travel path, comprising: acquiring user information;
randomly encoding the user information to form an initial population set, wherein the initial population set comprises a plurality of individual information subsets, and each individual information subset comprises a plurality of user current information and corresponding path information;
aiming at each individual information subset, calculating the fitness of the individual information subset according to the current information of the user and the corresponding path information to obtain a plurality of fitness;
layering the plurality of individual information subsets according to the optimization parameters and the fitness;
optimizing corresponding path information in a plurality of individual information subsets according to the levels of the individual information subsets to obtain optimized corresponding path information; and
and determining a driving path according to the optimized corresponding path information.
2. The method of claim 1, further comprising:
and setting optimization parameters, wherein the optimization parameters comprise a layering proportion, a transmission proportion and iteration times.
3. The method of claim 1, wherein the randomly encoding the user information to form an initial population set comprises:
setting at least one constraint function according to a preset driving condition;
and obtaining a plurality of individual information subsets meeting the constraint function based on the constraint function and the user information.
4. The method of claim 1, wherein the calculating, for each subset of individual information, a fitness of the subset of individual information according to the current information of the user and the corresponding path information, and obtaining a plurality of fitness includes:
decoding the current information of the user to obtain first user information, wherein the first user information comprises a user identifier and a user position coordinate;
setting a fitness function based on the driving path optimization target;
and calculating the fitness of each individual information subset based on each first user information in each individual information subset and a fitness function to obtain a plurality of fitness.
5. The method of claim 2, wherein said layering said plurality of subsets of individual information according to optimization parameters and fitness comprises:
ranking the fitness degrees according to a ranking function;
dividing the sorted fitness into a plurality of layers according to the layering proportion;
and dividing the plurality of individual information subsets corresponding to the fitness into a plurality of layers according to the layered fitness.
6. The method of claim 5, wherein the plurality of tiers comprise, from bottom to top, an exploration tier comprising a first tiered proportion of the subset of individual information, a first delivery tier comprising a second tiered proportion of the subset of individual information, a second delivery tier comprising a third tiered proportion of the subset of individual information, and a mining tier comprising a fourth tiered proportion of the subset of individual information.
7. The method according to one of claims 1 to 6, wherein the optimizing the corresponding path information in the individual information subsets according to the hierarchy of the individual information subsets includes:
respectively optimizing the individual information subsets at each hierarchy according to optimization strategies respectively determined for the plurality of hierarchies to obtain a plurality of optimized individual information subsets;
calculating the fitness of the optimized plurality of individual information subsets according to a fitness function;
calculating the transmission probability of the optimized individual information subset according to the fitness, wherein the transmission probability is used for representing the probability of the optimized individual information subset being transmitted to the upper level;
sorting the delivery probabilities;
and performing cross-layer level transmission on the optimized individual information subsets of the layers according to the ordered transmission probability and transmission proportion to obtain optimized corresponding path information.
8. The method of claim 7, wherein the optimization strategy comprises at least one of:
the first optimization strategy comprises the steps that a first optimization operator is adopted for the exploration layer and the first transmission layer, a second optimization operator is adopted for the second transmission layer and the exploitation layer, and the optimization amplitude of the first optimization operator to the corresponding path information in the optimization process is larger than that of the second optimization operator to the corresponding path information in the optimization process;
and the second optimization strategy comprises the steps of aiming at the corresponding path information in a plurality of individual information subsets in the second transmission layer and the mining layer which execute the first optimization strategy, accelerating the corresponding path information in the excellent individual information subset by using the corresponding path information in the individual information subset corresponding to the first fitness, and optimizing the corresponding path information in the inferior individual information subset by using the insertion strategy, wherein the first fitness comprises the fitness positioned at the head after the ordering.
9. The method of claim 8, wherein the accelerating the corresponding path information in the good subset of individual information using the corresponding path information in the subset of individual information to which the first fitness corresponds comprises:
the second transfer layer uses the corresponding path information in the individual information subset corresponding to the current first applicability to carry out acceleration operation, the mining layer uses the corresponding path information in the individual information subset corresponding to the global first applicability to carry out acceleration operation,
wherein the accelerating operation comprises: and optimizing the corresponding path information to be optimized by using an optimization operator according to the corresponding path information in the individual information subset corresponding to the first applicability.
10. The method of claim 8, wherein the optimizing corresponding path information in the inferior-out individual information subset using an insertion policy comprises:
removing at least one element in the corresponding path information in the rejected first optimized individual information subset to obtain corresponding path information in the first individual information subset;
for a removed element, inserting the removed element into different positions in corresponding path information in the first individual information subset respectively;
respectively calculating the fitness corresponding to the corresponding path information in the first individual information subset after the insertion into different positions according to the fitness function;
determining the insertion position corresponding to the first fitness as the insertion position of the element;
and obtaining the corresponding path information in the optimized individual information subset until all the removed elements are inserted.
11. The method of claim 2, wherein said determining a travel path from said optimized corresponding path information comprises:
and in response to determining that the end condition is met, determining the corresponding path information in the individual information subset corresponding to the first fitness as the driving path, wherein the end condition comprises determining that the current iteration number is equal to the iteration number.
12. An apparatus for determining a travel path, comprising:
the acquisition module is used for acquiring user information;
the encoding module is used for randomly encoding the user information to form an initial population set, the initial population set comprises a plurality of individual information subsets, and each individual information subset comprises a plurality of user current information and corresponding path information;
the calculation module is used for calculating the fitness of each individual information subset according to the current information of the user and the corresponding path information to obtain a plurality of fitness;
the layering module is used for layering the plurality of individual information subsets according to the optimization parameters and the fitness;
the optimization module is used for optimizing corresponding path information in the individual information subsets according to the levels of the individual information subsets to obtain optimized corresponding path information; and
and the determining module is used for determining a driving path according to the optimized corresponding path information.
13. An electronic device, comprising:
one or more processors;
a storage device for storing one or more programs,
wherein the one or more programs, when executed by the one or more processors, cause the one or more processors to perform the method of any of claims 1-11.
14. A computer readable storage medium having stored thereon executable instructions which, when executed by a processor, cause the processor to perform the method of any one of claims 1 to 11.
15. A computer program product comprising a computer program which, when executed by a processor, implements a method according to any one of claims 1 to 11.
CN202111494738.4A 2021-12-07 2021-12-07 Method and device for determining driving path, electronic equipment and storage medium Pending CN114154735A (en)

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