Method and device for obtaining business global optimal solution and electronic equipment

Abstract

The disclosure provides a method for obtaining a business global optimal solution, and relates to the technical field of data processing, in particular to the fields of big data, operational optimization, artificial intelligence and the like. The specific implementation scheme is: according to the description information of the target business, the target search space and the target function are determined; according to the target search space, an initial solution group is generated; based on the initial solution group, searching is performed in the target search space to obtain a reference solution group associated with the initial solution group; according to the initial solution group and the reference solution group, a new solution group is reconstructed; based on the new solution group, the operation of obtaining the reference solution group is executed until the optimal solution that makes the target function take the minimum value is obtained. Thus, by iterating the initial solution group, the reference solution group is obtained, and based on the initial solution group and the reference solution group, the new solution group is reconstructed, thereby increasing the dispersion of the solution group, and then the global optimal solution is solved based on the new solution group, thereby improving the reliability and accuracy of obtaining the global optimal solution.

Description

Technical Field

[0001] This disclosure relates to the field of data processing technology, particularly to the fields of big data, operations research and optimization, and artificial intelligence, specifically to methods, apparatus and electronic devices for obtaining the global optimal solution for a business. Background Technology

[0002] In business scenarios such as engineering system design, hybrid product manufacturing, and path planning, search algorithms can be used to find the globally optimal solution. However, real-world business problems are typically complex, non-linear, non-convex, and involve multiple variables with one or more objective functions. Therefore, obtaining the globally optimal solution is a pressing issue that needs to be addressed. Summary of the Invention

[0003] This disclosure provides a method and apparatus for obtaining the globally optimal solution for a business application.

[0004] According to one aspect of this disclosure, a method for obtaining a globally optimal solution for a business application is provided, comprising:

[0005] Based on the description of the target business, determine the target search space and objective function;

[0006] Based on the target search space, generate an initial solution population;

[0007] Based on the initial solution group, a search is performed in the target search space to obtain the reference solution group associated with the initial solution group;

[0008] Reconstruct a new solution group based on the initial solution group and the reference solution group;

[0009] Based on the new solution group, the operation of obtaining the reference solution group is performed until the optimal solution that minimizes the objective function is obtained.

[0010] According to another aspect of this disclosure, an apparatus for obtaining a globally optimal solution for a business is provided, comprising:

[0011] The determination module is used to determine the target search space and objective function based on the description information of the target business.

[0012] The generation module is used to generate an initial solution population based on the target search space;

[0013] The acquisition module is used to search the target search space based on the initial solution population to obtain the reference solution population associated with the initial solution population;

[0014] The reconstruction module is used to reconstruct a new solution group based on the initial solution group and the reference solution group;

[0015] The aforementioned acquisition module is also used to perform the operation of acquiring a reference solution group based on the new solution group, until the optimal solution that minimizes the objective function is acquired.

[0016] According to another aspect of this disclosure, an electronic device is provided, comprising:

[0017] At least one processor; and

[0018] A memory communicatively connected to the at least one processor; wherein,

[0019] The memory stores instructions that can be executed by the at least one processor, which, when executed by the at least one processor, enables the at least one processor to perform the method described in the above embodiments.

[0020] According to another aspect of this disclosure, a non-transitory computer-readable storage medium is provided storing computer instructions, wherein the computer instructions are used to cause the computer to perform the method described in the above embodiments.

[0021] According to another aspect of this disclosure, a computer program product is provided, including a computer program that, when executed by a processor, implements the steps of the method described in the above embodiments.

[0022] It should be understood that the description in this section is not intended to identify key or essential features of the embodiments of this disclosure, nor is it intended to limit the scope of this disclosure. Other features of this disclosure will become readily apparent from the following description. Attached Figure Description

[0023] The accompanying drawings are provided to better understand this solution and do not constitute a limitation of this disclosure. Wherein:

[0024] Figure 1 A flowchart illustrating a method for obtaining a globally optimal solution for a business application, as provided in this embodiment of the disclosure;

[0025] Figure 2 A flowchart illustrating another method for obtaining the globally optimal solution for a business application provided in this embodiment of the disclosure;

[0026] Figure 3 A schematic flowchart of another device for obtaining the global optimal solution for a business application provided in this embodiment of the present disclosure;

[0027] Figure 4 This is a block diagram of an electronic device used to implement the method for obtaining the global optimal solution of a business in the embodiments of this disclosure. Detailed Implementation

[0028] The exemplary embodiments of this disclosure are described below with reference to the accompanying drawings, including various details of the embodiments to aid understanding, and should be considered merely exemplary. Therefore, those skilled in the art will recognize that various changes and modifications can be made to the embodiments described herein without departing from the scope and spirit of this disclosure. Similarly, for clarity and brevity, descriptions of well-known functions and structures are omitted in the following description.

[0029] Big data, or massive data, refers to information that is so large that it cannot be captured, managed, processed, and organized into a more proactive business decision-making process using current mainstream software tools within a reasonable timeframe.

[0030] Operations research, or optimization, uses mathematical methods to solve complex real-world problems with numerous decision variables, seeking optimal solutions. In modern business, operations research is widely applied across various industries to solve complex business problems, helping companies achieve intelligent decision-making.

[0031] Artificial intelligence (AI) is the study of using computers to simulate certain human thought processes and intelligent behaviors (such as learning, reasoning, thinking, and planning). It encompasses both hardware and software technologies. AI hardware technologies generally include sensors, dedicated AI chips, cloud computing, distributed storage, and big data processing. AI software technologies include computer vision, speech recognition, natural language processing, deep learning, big data processing, and knowledge graph technologies.

[0032] Typically, the low dispersion of the initial solution group can cause the search algorithm to easily get trapped in local optima. Therefore, this disclosure avoids the search process getting trapped in local optima by reconstructing a new solution group with higher dispersion and then restarting the new search process based on the reconstructed solution group, thereby improving the reliability and accuracy of the global optimal solution for the search business.

[0033] The method, apparatus, electronic device, and storage medium for obtaining the global optimal solution of a business according to embodiments of the present disclosure will now be described in detail with reference to the accompanying drawings.

[0034] Figure 1 This is a flowchart illustrating a method for obtaining a globally optimal solution for a business application, as provided in an embodiment of this disclosure.

[0035] like Figure 1 As shown, the method includes:

[0036] Step 101: Determine the target search space and objective function based on the description information of the target business.

[0037] The target business can be any business that requires finding the optimal solution, such as vehicle routing, hybrid product manufacturing, or engineering system design.

[0038] In this disclosure, the requirements of the target business can be determined based on the description information of the target business, and then the target search space and objective function can be determined based on the requirements of the target business.

[0039] The target search space can be the intervals of variables corresponding to the target business. Through iterative operations such as mutation, crossover, and movement of solutions within the solution group within the target search space, the solutions are made to converge towards the optimal solution. When convergence occurs, the optimal solution is obtained. The objective function can be used to indicate the direction of convergence, i.e., the optimal solution for the business; for example, the solution that minimizes the objective function value is the optimal solution.

[0040] For example, in vehicle routing planning, the objective function is to plan a vehicle route that minimizes the cost of delivering goods to the user's location.

[0041]

[0042] Where n is the user ID, g i Let K be the quantity of goods required by the i-th user, K be the number of available vehicles, k be the vehicle number, q be the capacity of each vehicle, and c be the quantity of goods required by the i-th user. ij Let y be the unit cost from user i to user j. ik This indicates that user i's goods are transported by vehicle k. When user i's goods are transported by vehicle k, y ik =1, otherwise y ik =0. x ijk This indicates that vehicle k travels from user i to user j. When vehicle k travels from user i to user j, x ijk =1, otherwise x ijk =0. R = (1+t) / 4, where t is the current iteration number.

[0043] In formula 1, This represents the penalty value of the objective function when the actual capacity limit of the vehicle is exceeded.

[0044] Furthermore, the target search space corresponding to this business can be represented by the following constraints:

[0045]

[0046]

[0047]

[0048]

[0049] In addition, since the solution in vehicle routing planning is the vehicle's driving path, the target search space can also include the possible routes that users can travel together in their respective areas.

[0050] Step 102: Generate an initial solution population based on the target search space.

[0051] The initial solution group can contain multiple solutions, each of which is a feasible solution to the business (i.e., the objective function). The feasible solution can be a multi-dimensional vector, with each dimension corresponding to a variable.

[0052] In this disclosure, multiple initial solutions can be determined by randomly generating a value for each dimension of the solution vector within the target search space, thereby generating an initial solution group. Alternatively, the initial solution group can be generated by encoding.

[0053] For example, in a vehicle routing scenario, suppose there are 6 users and 2 freight transport centers. Using encoding to generate an initial solution set, the freight transport centers can be encoded as 0. Therefore, the solution for the vehicle routing scenario can be an 8-dimensional vector, with each dimension corresponding to the user ID and the freight transport center.

[0054] Next, a random number can be generated for each user and cargo transportation center. Then, the users and cargo transportation centers can be sorted according to the size of the random number to generate a user code sequence, i.e., a solution.

[0055] For example, if the random numbers generated for users are 5.4, 2.7, 4.6, 3.5, 1.8, and 3.9, and the random numbers generated for the two cargo transportation centers are 2.9 and 4.1, then the user IDs and cargo transportation centers are sorted in ascending order of the random numbers, and the determined user code sequence is: 5, 2, 0, 4, 6, 0, 3, 1.

[0056] Therefore, the vehicle's path can be determined based on the user coding sequence. For example, if the user coding sequence is 5, 2, 0, 4, 6, 0, 3, 1, and the vehicle must depart from and return to the freight transport center, the vehicle's path is 0520460310, meaning vehicle 1's path is 0520, vehicle 2's path is 0460, and vehicle 3's path is 0310.

[0057] Step 103: Based on the initial solution group, search the target search space to obtain the reference solution group associated with the initial solution group.

[0058] In this disclosure, a sequential quadratic programming approach can be applied to the initial solution group, or the initial solution group can be input into a search algorithm to iterate within the target search space. When the number of iterations reaches a first preset number of iterations, the iteration of the solution group can be stopped, and the solution group after the first preset number of iterations is determined as the reference solution group.

[0059] For example, the first preset number of iterations is 1000. After 1000 iterations on the initial solution group, the iteration can be stopped, and the solution group determined after 1000 iterations can be determined as the reference solution group.

[0060] Therefore, by performing preliminary iterations on the initial solution group to determine the reference solution group, the effectiveness of the reference solution group can be improved.

[0061] Step 104: Reconstruct a new solution group based on the initial solution group and the reference solution group.

[0062] In this disclosure, a new solution corresponding to each initial solution is reconstructed based on the first distance between each initial solution in the initial solution group and the corresponding reference solution in the reference solution group.

[0063] For example, the Eulerian distance between solution i in the initial solution group and solution i in the reference solution group can be calculated, and the weighted sum of this Eulerian distance can be used to determine the new solution corresponding to solution i in the initial solution group. The values ​​of each dimension in the new solution are shown in the following formula:

[0064]

[0065] Where i,j=1,2,…,N (N is the number of solutions in the initial solution group), w is (0,1), Let j be the value of the j-th dimension of the i-th reference solution in the reference solution group. Let be the value of the j-th dimension of the i-th initial solution in the initial solution group.

[0066] It should be noted that each new solution in the new solution group actually corresponds to a point on the line connecting the corresponding solution in the initial solution group and the corresponding solution in the reference solution group. Therefore, the new solutions are more dispersed than the solutions in the initial solution group.

[0067] Step 105: Based on the new solution group, perform the operation of obtaining the reference solution group until the optimal solution that minimizes the objective function is obtained.

[0068] In this disclosure, the operation of obtaining a reference solution group can be performed based on a new solution group until a target solution group corresponding to a second preset number of iterations is obtained. The solution in the target solution group that minimizes the objective function value can be determined as the optimal solution, thereby improving the reliability of obtaining the optimal solution. The second preset number of iterations can be the number of iterations required to obtain the optimal solution from the solution group.

[0069] For example, the second preset number of iterations is 10,000, and the first preset number of iterations is 1,000. After performing 1,000 iterations on the initial solution group to obtain a reference solution group, and determining a new solution group based on the reference solution group and the initial solution group, another 10,000 iterations can be performed based on the new solution group. The solution group after 10,000 iterations is then determined as the target solution group. Therefore, the solution in the target solution group that minimizes the objective function is the globally optimal solution for that business operation.

[0070] Optionally, the operation of obtaining a reference solution group can be performed based on the new solution group. Each time the iteration count reaches a first preset number of iterations, the solution group after the first preset number of iterations can be used to replace the reference solution group determined after the previous first preset number of iterations, serving as the new reference group. Then, a new solution can be reconstructed based on this new reference group and the previously reconstructed solution group, and the operation of obtaining a reference solution group can be performed again based on the new solution group, until the second preset number of iterations is completed. Finally, the solution group after the second preset number of iterations can be determined as the target solution group, and the solution in the target solution group that minimizes the objective function value is determined as the optimal solution.

[0071] For example, the second preset number of iterations is 10,000, and the first preset number of iterations is 1,000. After performing 1,000 iterations on the initial solution group to obtain a reference solution group, and determining a new solution group based on this reference solution group and the initial solution group, the next 9,000 iterations can be performed based on the new solution group. When another 1,000 iterations are performed on the new solution group (i.e., a total of 2,000 iterations have been completed), the solution group after 2,000 iterations can be determined as the new reference solution group. Based on this reference solution group after 2,000 iterations and the solution group reconstructed after 1,000 iterations, a new solution group after 2,000 iterations is reconstructed, and the next 8,000 iterations are performed based on this new solution group. This process is repeated until 10,000 iterations are completed, and the solution group after 10,000 iterations is determined as the target solution group. The solution in the target solution group that minimizes the objective function value is determined as the optimal solution.

[0072] In this disclosure, after determining the target search space and objective function based on the description information of the target business, an initial solution group can be generated based on the target search space. Then, a search is performed within the target search space based on the initial solution group to obtain a reference solution group associated with the initial solution group. Subsequently, a new solution group can be reconstructed based on the initial solution group and the reference solution group, and the operation of obtaining the reference solution group is repeated based on the new solution group until the optimal solution that minimizes the objective function is obtained. Therefore, by iterating through the initial solution group to obtain the reference solution group, and reconstructing a new solution group based on the initial solution group and the reference solution group, the dispersion of the solution group is increased. This allows for the search of the global optimal solution based on the new solution group, improving the reliability and accuracy of obtaining the global optimal solution.

[0073] Figure 2 This is a flowchart illustrating a method for obtaining a globally optimal solution for a business application, as provided in an embodiment of this disclosure.

[0074] like Figure 2 As shown, the method includes:

[0075] Step 201: Determine the target search space and objective function based on the description information of the target business.

[0076] Step 202: Generate an initial solution population based on the target search space.

[0077] The specific implementation process of steps 201-202 in this disclosure can be found in the detailed description of any embodiment of this disclosure, and will not be repeated here.

[0078] Step 203: Based on the initial solution population, search in the target search space until the minimum value of the objective function corresponding to the continuously obtained solution population remains unchanged, and determine that a reference solution population has been obtained.

[0079] In this disclosure, a sequential quadratic programming approach can be applied to the initial solution group, or the initial solution group can be input into a search algorithm to iterate within the target search space. During the iteration process, the values ​​of the objective function obtained based on each solution can be recorded, and the minimum value of the objective function corresponding to the solution group obtained in each iteration can be determined. When the minimum value of the objective function corresponding to the solution group obtained in multiple consecutive iterations does not change, the search may have fallen into a local optimum. At this point, a new solution group can be reconstructed, and the search can be performed again based on the new solution group to allow the search to escape the local optimum and obtain the globally optimal solution for the business application.

[0080] In this disclosure, when iterating the initial solution group in the target search space, if the minimum value of the objective function corresponding to the continuously obtained solution groups remains unchanged, the solution group that most recently kept the minimum value of the objective function unchanged can be determined as the reference solution group.

[0081] For example, when iterating over the initial solution group, if the minimum value of the objective function corresponding to the solution group determined from the 90th to the 100th iteration has not changed, the solution group after the 100th iteration can be determined as the reference solution group.

[0082] Therefore, by performing preliminary iterations on the initial solution group, when the minimum value of the objective function corresponding to the continuously obtained solution groups remains unchanged, the solution group that most recently kept the minimum value of the objective function unchanged can be determined as the reference solution group, thereby increasing the distance between the reference solution group and the initial solution group.

[0083] Step 204: Based on the second distance between the i-th initial solution in the initial solution group and the j-th reference solution in the reference solution group, reconstruct the new solutions corresponding to the i-th initial solution and the j-th reference solution, where i and j are distinct positive integers less than N, and N is the number of solutions contained in the initial solution group and the reference solution group, and N is an integer greater than 1.

[0084] In this disclosure, in order to enhance the dispersion of the solution group, the new solutions corresponding to the i-th initial solution and the j-th reference solution can be reconstructed based on the second distance between the i-th initial solution in the initial solution group and the j-th reference solution in the reference solution group.

[0085] For example, the Eulerian distance between solution i in the initial solution group and solution j in the reference solution group can be calculated, and the weighted sum of this Eulerian distance can be used to determine the new solution corresponding to solution i in the initial solution group. The values ​​of each dimension in the new solution are shown in the following formula:

[0086]

[0087] Where i,j=1,2,…,N, w is (0,1), Let j be the value of the j-th dimension of the j-th reference solution in the reference solution group. Let be the value of the j-th dimension of the i-th initial solution in the initial solution group.

[0088] It is understandable that the j-th reference solution in the reference solution group corresponding to the i-th initial solution in the initial solution group is N-1, so the i-th initial solution can generate N-1 new solutions, and the reconstructed new solution group can contain N(N-1) new solutions. Furthermore, each new solution actually corresponds to a point on the line connecting the corresponding solution in the initial solution group and the corresponding solution in the reference solution group; therefore, the new solutions are more dispersed than the solutions in the initial solution group.

[0089] Step 205: Based on the new solution group, perform the operation of obtaining a reference solution group until the minimum value of the objective function corresponding to the continuously obtained solution groups remains unchanged. The solution that minimizes the objective function value is determined as the optimal solution.

[0090] In this disclosure, the operation of obtaining a reference solution group can be performed based on the new solution group. When the minimum value of the objective function corresponding to the continuously obtained solution group remains unchanged, it means that the optimal solution for the business has been found. At this time, the iteration can be stopped, and the solution that makes the objective function take the minimum value is determined as the optimal solution, thereby improving the reliability of obtaining the optimal solution.

[0091] Optionally, the operation of obtaining a reference solution group can be performed based on the new solution group. When the minimum value of the objective function corresponding to the continuously obtained solution groups remains unchanged, the solution group that most recently kept the minimum value of the objective function unchanged can replace the previously determined reference solution group as the new reference group. Then, a new solution group can be reconstructed based on this new reference group and the previously reconstructed solution group, and the operation of obtaining a reference solution group can be performed again based on the new solution group until the second preset number of iterations is completed. Then, the solution group after the second preset number of iterations can be determined as the target solution group, and the solution with the minimum objective function value in the target solution group is determined as the optimal solution.

[0092] For example, the second preset number of iterations is 10,000. When the minimum value of the objective function corresponding to the solution group determined by the 90th to 100th iterations remains unchanged, the solution group after the 100th iteration can be determined as the reference solution group. After determining a new solution group based on this reference solution group and the initial solution group, the next 9,900 iterations can be performed based on the new solution group. When the minimum value of the objective function corresponding to the solution group determined by the 2,000th to 2,100th iterations again remains unchanged, the solution group after the 2,100th iteration can be determined as the new reference solution group. Based on this reference solution group after the 2,100th iteration and the new solution group reconstructed after 100 iterations, a new solution group after the 2,100th iteration is determined. The next 7,900 iterations are performed based on this new solution group, and this process is repeated until 10,000 iterations are completed. The solution group after the 10,000th iteration is then determined as the target solution group.

[0093] In this disclosure, after determining the target search space and objective function based on the description information of the target business, an initial solution group can be generated based on the target search space. A search is then performed within the target search space based on this initial solution group until the minimum value of the objective function corresponding to the continuously acquired solution groups remains unchanged, thus identifying a reference solution group. Subsequently, based on the second distance between the i-th initial solution in the initial solution group and the j-th reference solution in the reference solution group, a new solution corresponding to the i-th initial solution and the j-th reference solution is reconstructed. The operation of obtaining a reference solution group is then repeated based on the new solution group until the minimum value of the objective function corresponding to the continuously acquired solution groups remains unchanged. The solution that minimizes the objective function value is determined as the optimal solution. Therefore, by iterating through the initial solution group to obtain a reference solution group, and reconstructing a new solution group based on the initial and reference solution groups, the dispersion of the initial solution group is increased. This, in turn, allows for the solving of the global optimal solution based on the new solution group, improving the reliability and accuracy of obtaining the global optimal solution.

[0094] To implement the above embodiments, this disclosure also proposes a device for obtaining the global optimal solution for a business application. Figure 3 This is a schematic diagram of a device for obtaining the global optimal solution for a business application, provided in an embodiment of this disclosure.

[0095] like Figure 3 As shown, the device 300 for obtaining the global optimal solution of the business includes: a determination module 310, a generation module 320, an acquisition module 330, and a reconstruction module 340.

[0096] The determination module 310 is used to determine the target search space and objective function based on the description information of the target business;

[0097] The generation module 320 is used to generate an initial solution group based on the target search space;

[0098] The acquisition module 330 is used to search the target search space based on the initial solution group to obtain the reference solution group associated with the initial solution group;

[0099] Reconstruction module 340 is used to reconstruct a new solution group based on the initial solution group and the reference solution group;

[0100] The acquisition module 330 is further configured to perform the operation of acquiring the reference solution group based on the new solution group, until the optimal solution that minimizes the objective function is acquired.

[0101] Optionally, the aforementioned refactoring module 340 is used for:

[0102] Based on the first distance between each initial solution in the initial solution group and the corresponding reference solution in the reference solution group, a new solution corresponding to each initial solution is reconstructed.

[0103] Optionally, the aforementioned refactoring module 340 is used for:

[0104] Based on the second distance between the i-th initial solution in the initial solution group and the j-th reference solution in the reference solution group, reconstruct the new solutions corresponding to the i-th initial solution and the j-th reference solution, where i and j are mutually unequal positive integers less than N, and N is the number of solutions contained in the initial solution group and the reference solution group, and N is an integer greater than 1.

[0105] Optionally, the aforementioned acquisition module 330 is used for:

[0106] Based on the initial solution group, a search is performed in the target search space until a reference solution group corresponding to a first preset number of iterations is obtained.

[0107] Optionally, the aforementioned acquisition module 330 is used for:

[0108] Based on the initial solution group, a search is performed in the target search space until the minimum value of the objective function corresponding to the continuously obtained solution groups remains unchanged, and the reference solution group is determined to be obtained.

[0109] Optionally, the aforementioned acquisition module 330 is used for:

[0110] Based on the new solution group, the operation of obtaining the reference solution group is repeated until the target solution group corresponding to the second preset number of iterations is obtained;

[0111] The solution with the smallest corresponding objective function value in the target solution group is determined as the optimal solution.

[0112] Optionally, the aforementioned acquisition module 330 is used for:

[0113] Based on the new solution group, the operation of obtaining the reference solution group is repeated until the minimum value of the objective function corresponding to the continuously obtained solution group remains unchanged. The solution that minimizes the objective function value is determined as the optimal solution.

[0114] It should be noted that the explanation of the aforementioned method for determining search term weights also applies to the apparatus of this embodiment, and therefore will not be repeated here.

[0115] In this disclosure, after determining the target search space and objective function based on the description information of the target business, an initial solution group can be generated based on the target search space. Then, a search is performed within the target search space based on the initial solution group to obtain a reference solution group associated with the initial solution group. Subsequently, a new solution group can be reconstructed based on the initial solution group and the reference solution group, and the operation of obtaining the reference solution group is repeated based on the new solution group until the optimal solution that minimizes the objective function is obtained. Therefore, by iterating through the initial solution group to obtain the reference solution group, and reconstructing a new solution group based on the initial solution group and the reference solution group, the dispersion of the solution group is increased. This allows for the search of the global optimal solution based on the new solution group, improving the reliability and accuracy of obtaining the global optimal solution.

[0116] According to embodiments of this disclosure, this disclosure also provides an electronic device, a readable storage medium, and a computer program product.

[0117] Figure 4 A schematic block diagram of an example electronic device 400 that can be used to implement embodiments of the present disclosure is shown. The electronic device is intended to represent various forms of digital computers, such as laptop computers, desktop computers, workstations, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. The electronic device may also represent various forms of mobile devices, such as personal digital processors, cellular phones, smartphones, wearable devices, and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely illustrative and are not intended to limit the implementation of the present disclosure described and / or claimed herein.

[0118] like Figure 4 As shown, device 400 includes a computing unit 401, which can perform various appropriate actions and processes based on a computer program stored in ROM (Read-Only Memory) 402 or a computer program loaded from storage unit 408 into RAM (Random Access Memory) 403. RAM 403 may also store various programs and data required for the operation of device 400. The computing unit 401, ROM 402, and RAM 403 are interconnected via bus 404. I / O (Input / Output) interface 405 is also connected to bus 404.

[0119] Multiple components in device 400 are connected to I / O interface 405, including: input unit 406, such as keyboard, mouse, etc.; output unit 407, such as various types of monitors, speakers, etc.; storage unit 408, such as disk, optical disk, etc.; and communication unit 409, such as network card, modem, wireless transceiver, etc. Communication unit 409 allows device 400 to exchange information / data with other devices through computer networks such as the Internet and / or various telecommunications networks.

[0120] The computing unit 401 can be various general-purpose and / or special-purpose processing components with processing and computing capabilities. Some examples of the computing unit 401 include, but are not limited to, CPUs (Central Processing Units), GPUs (Graphics Processing Units), various special-purpose AI (Artificial Intelligence) computing chips, various computing units running machine learning model algorithms, DSPs (Digital Signal Processors), and any suitable processor, controller, microcontroller, etc. The computing unit 401 performs the various methods and processes described above, such as the method for obtaining the global optimal solution for a business. For example, in some embodiments, the method for obtaining the global optimal solution for a business may be implemented as a computer software program, which is tangibly contained in a machine-readable medium, such as storage unit 408. In some embodiments, part or all of the computer program may be loaded and / or installed on device 400 via ROM 402 and / or communication unit 409. When the computer program is loaded into RAM 403 and executed by the computing unit 401, one or more steps of the method for obtaining the global optimal solution for a business described above may be performed. Alternatively, in other embodiments, the computing unit 401 may be configured, by any other suitable means (e.g., by means of firmware), to perform a method for obtaining the global optimal solution for the business.

[0121] Various implementations of the systems and techniques described above herein can be implemented in digital electronic circuit systems, integrated circuit systems, FPGAs (Field Programmable Gate Arrays), ASICs (Application-Specific Integrated Circuits), ASSPs (Application-Specific Standard Products), SOCs (System-on-Chips), CPLDs (Complex Programmable Logic Devices), computer hardware, firmware, software, and / or combinations thereof. These various implementations may include implementations in one or more computer programs that can be executed and / or interpreted on a programmable system including at least one programmable processor, which may be a dedicated or general-purpose programmable processor, capable of receiving data and instructions from a storage system, at least one input device, and at least one output device, and transmitting data and instructions to the storage system, the at least one input device, and the at least one output device.

[0122] The program code used to implement the methods of this disclosure may be written in any combination of one or more programming languages. This program code may be provided to a processor or controller of a general-purpose computer, special-purpose computer, or other programmable data processing apparatus, such that when executed by the processor or controller, the program code causes the functions / operations specified in the flowcharts and / or block diagrams to be implemented. The program code may be executed entirely on a machine, partially on a machine, as a standalone software package partially on a machine and partially on a remote machine, or entirely on a remote machine or server.

[0123] In the context of this disclosure, a machine-readable medium can be a tangible medium that may contain or store a program for use by or in conjunction with an instruction execution system, apparatus, or device. A machine-readable medium can be a machine-readable signal medium or a machine-readable storage medium. A machine-readable medium can be, but is not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, apparatus, or devices, or any suitable combination of the foregoing. More specific examples of machine-readable storage media include electrical connections based on one or more wires, portable computer disks, hard disks, RAM, ROM, EPROM (Electrically Programmable Read-Only Memory) or flash memory, optical fiber, CD-ROM (Compact Disc Read-Only Memory), optical storage devices, magnetic storage devices, or any suitable combination of the foregoing.

[0124] To provide interaction with a user, the systems and techniques described herein can be implemented on a computer having: a display device for displaying information to the user (e.g., a CRT (Cathode-Ray Tube) or LCD (Liquid Crystal Display) monitor); and a keyboard and pointing device (e.g., a mouse or trackball) through which the user provides input to the computer. Other types of devices can also be used to provide interaction with the user; for example, feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user can be received in any form (including sound input, voice input, or tactile input).

[0125] The systems and technologies described herein can be implemented in computing systems that include backend components (e.g., as data servers), or middleware components (e.g., application servers), or frontend components (e.g., user computers with graphical user interfaces or web browsers through which users can interact with implementations of the systems and technologies described herein), or any combination of such backend, middleware, or frontend components. The components of the system can be interconnected via digital data communication (e.g., communication networks) of any form or medium. Examples of communication networks include LANs (Local Area Networks), WANs (Wide Area Networks), the Internet, and blockchain networks.

[0126] Computer systems can include clients and servers. Clients and servers are generally geographically separated and typically interact via communication networks. The client-server relationship is created by computer programs running on the respective computers and having a client-server relationship with each other. A server can be a cloud server, also known as a cloud computing server or cloud host, a hosting product within the cloud computing service system that addresses the shortcomings of traditional physical hosts and VPS (Virtual Private Server) services, such as high management difficulty and weak business scalability. Servers can also be servers for distributed systems or servers integrated with blockchain technology.

[0127] According to embodiments of this disclosure, this disclosure also provides a computer program product that, when an instruction processor in the computer program product is executed, performs the method for obtaining the globally optimal solution of a business as proposed in the above embodiments of this disclosure.

[0128] It should be understood that the various forms of processes shown above can be used to rearrange, add, or delete steps. For example, the steps described in this disclosure can be executed in parallel, sequentially, or in different orders, as long as the desired result of the technical solution disclosed in this disclosure can be achieved, and this is not limited herein.

[0129] The specific embodiments described above do not constitute a limitation on the scope of protection of this disclosure. Those skilled in the art should understand that various modifications, combinations, sub-combinations, and substitutions can be made according to design requirements and other factors. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of this disclosure should be included within the scope of protection of this disclosure.

Claims

1. A method for obtaining the globally optimal solution for a business application, comprising: Based on the description information of the target service, determine the target search space and objective function, wherein the target service includes vehicle routing. Based on the target search space, an initial solution group is generated, wherein the target search space includes the traversable paths in the area where each user is located. Based on the initial solution group, a search is performed in the target search space to obtain the reference solution group associated with the initial solution group; Based on the initial solution group and the reference solution group, a new solution group is reconstructed; Based on the new solution group, the operation of obtaining the reference solution group is repeated until the optimal solution that minimizes the objective function is obtained; Wherein, the step of reconstructing a new solution group based on the initial solution group and the reference solution group includes: Based on the second distance between the i-th initial solution in the initial solution group and the j-th reference solution in the reference solution group, reconstruct the new solutions corresponding to the i-th initial solution and the j-th reference solution. Each new solution corresponds to a point on the line connecting the corresponding solution in the initial solution group and the corresponding solution in the reference solution group. Here, i and j are mutually exclusive positive integers less than N, and N is the number of solutions contained in the initial solution group and the reference solution group, and N is an integer greater than 1.

2. The method of claim 1, wherein, The step of searching the target search space based on the initial solution group to obtain the reference solution group associated with the initial solution group includes: Based on the initial solution group, a search is performed in the target search space until a reference solution group corresponding to a first preset number of iterations is obtained.

3. The method of claim 1, wherein, The step of searching the target search space based on the initial solution group to obtain the reference solution group associated with the initial solution group includes: Based on the initial solution group, a search is performed in the target search space until the minimum value of the objective function corresponding to the continuously obtained solution groups remains unchanged, and the reference solution group is determined to be obtained.

4. The method as described in any one of claims 1-3, wherein, The step of returning and performing the operation of obtaining the reference solution group based on the new solution group until the optimal solution that minimizes the objective function is obtained includes: Based on the new solution group, the operation of obtaining the reference solution group is repeated until the target solution group corresponding to the second preset number of iterations is obtained; The solution with the smallest corresponding objective function value in the target solution group is determined as the optimal solution.

5. The method as described in any one of claims 1-3, wherein, The step of returning and performing the operation of obtaining the reference solution group based on the new solution group until the optimal solution that minimizes the objective function is obtained includes: Based on the new solution group, the operation of obtaining the reference solution group is repeated until the minimum value of the objective function corresponding to the continuously obtained solution group remains unchanged. The solution that minimizes the objective function value is determined as the optimal solution.

6. A device for obtaining a globally optimal solution for a business application, comprising: The determination module is used to determine the target search space and objective function based on the description information of the target service, wherein the target service includes vehicle route planning; The generation module is used to generate an initial solution group based on the target search space, wherein the target search space includes the traversable paths of each user's region; The acquisition module is used to search the target search space based on the initial solution group to obtain the reference solution group associated with the initial solution group; The reconstruction module is used to reconstruct a new solution group based on the initial solution group and the reference solution group; The acquisition module is further configured to perform the operation of acquiring the reference solution group based on the new solution group until the optimal solution that minimizes the objective function is acquired. The reconstruction module is used for: Based on the second distance between the i-th initial solution in the initial solution group and the j-th reference solution in the reference solution group, reconstruct the new solutions corresponding to the i-th initial solution and the j-th reference solution. Each new solution corresponds to a point on the line connecting the corresponding solution in the initial solution group and the corresponding solution in the reference solution group. Here, i and j are mutually exclusive positive integers less than N, and N is the number of solutions contained in the initial solution group and the reference solution group, and N is an integer greater than 1.

7. The apparatus of claim 6, wherein, The acquisition module is used for: Based on the initial solution group, a search is performed in the target search space until a reference solution group corresponding to a first preset number of iterations is obtained.

8. The apparatus of claim 6, wherein, The acquisition module is used for: Based on the initial solution group, a search is performed in the target search space until the minimum value of the objective function corresponding to the continuously obtained solution groups remains unchanged, and the reference solution group is determined to be obtained.

9. The apparatus according to any one of claims 6-8, wherein, The acquisition module is used for: Based on the new solution group, the operation of obtaining the reference solution group is repeated until the target solution group corresponding to the second preset number of iterations is obtained; The solution with the smallest corresponding objective function value in the target solution group is determined as the optimal solution.

10. The apparatus according to any one of claims 6-8, wherein, The acquisition module is used for: Based on the new solution group, the operation of obtaining the reference solution group is repeated until the minimum value of the objective function corresponding to the continuously obtained solution group remains unchanged. The solution that minimizes the objective function value is determined as the optimal solution.

11. An electronic device, comprising: At least one processor; as well as A memory communicatively connected to the at least one processor; wherein, The memory stores instructions that can be executed by the at least one processor to enable the at least one processor to perform the method of any one of claims 1-5.

12. A non-transitory computer-readable storage medium storing computer instructions, wherein, The computer instructions are used to cause the computer to perform the method according to any one of claims 1-5.

13. A computer program product comprising a computer program that, when executed by a processor, implements the steps of the method according to any one of claims 1-5.

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