CN-121998358-A - Resource allocation method, device and system based on multi-index grouping collaborative search

CN121998358ACN 121998358 ACN121998358 ACN 121998358ACN-121998358-A

Abstract

The invention discloses a resource allocation method, device and system based on multi-index grouping collaborative search, which comprises the steps of performing multi-index calculation on a resource object; and carrying out differential searching on different groups, and generating a resource allocation scheme meeting constraint conditions through a coordination control mechanism. By adopting the technical scheme of the invention, under the condition of limited computing resources, the structural optimization of the constrained resource allocation problem is realized, and the searching efficiency, the solution stability and the scheme feasibility are improved.

Inventors

CAI RONGGUI
LIU SUPING
GONG SHU
LI SHICAN
LI MINGCONG
LIU XUN
XIONG ZIHAN
Liao jiaxin

Assignees

广东科技学院

Dates

Publication Date: 20260508
Application Date: 20260128

Claims (7)

1. A resource allocation method based on multi-index grouping collaborative search is characterized by comprising the following steps: Step S1, acquiring a resource object set to be configured; step S2, calculating a plurality of evaluation indexes for describing the current matching state and potential contribution capacity of each resource object; S3, performing collaborative clustering on the resource objects according to a preset threshold or a clustering rule based on the real-time matching fingers and the value contribution index; Step S4, aiming at different groups, performing differentiated search operation, and uniformly adjusting search results through a coordination control mechanism; and S5, when the preset termination condition is reached, terminating the searching process, and outputting a final resource allocation scheme as an optimization result.
2. The resource allocation method based on multi-index clustering collaborative searching according to claim 1, wherein in step S2, a real-time matching index (RMI) is defined for measuring the matching degree between the current configuration state of the resource object and the target demand, and the calculation method is as shown in formula (1): ; Wherein, the Represent the first The number of resource objects that have been currently matched, Representing a corresponding target demand; Meanwhile, a Value Contribution Index (VCI) is defined for measuring potential contribution capability of a resource object in the whole configuration, and the calculation mode is shown as a formula (2): ; Wherein, the Represent the first The combined contribution value of the individual resource objects, Representing the total number of resource objects.
3. The method for resource allocation based on multi-index clustered collaborative search according to claim 2, wherein in step S4, The overall optimization objective function can be expressed as equation (3): ; Wherein, the Representing resource objects The utility function in the current configuration, Is a weight coefficient; The optimization process needs to meet the resource constraint and the adjustment constraint at the same time, and the constraint condition is shown in a formula (4): ; Wherein, the Represents the total budget upper limit for the resource, Representing the configuration values of the resource object in the previous iteration, Representing the maximum allowable adjustment amplitude; Through coordination control of different clustering search results, the final resource allocation scheme optimizes the objective function value while meeting the constraint condition of the formula (4) 。
4. A resource allocation device based on multi-index clustering collaborative search, comprising: The first processing module is used for acquiring a resource object set to be configured; The second processing module is used for calculating a plurality of evaluation indexes for describing the current matching state and potential contribution capacity of each resource object; The third processing module is used for carrying out cooperative grouping on the resource objects according to a preset threshold or grouping rule based on the real-time matching fingers and the value contribution index; The fourth processing module is used for executing differentiated search operation aiming at different groups and uniformly adjusting search results through a coordination control mechanism; And the fifth processing module is used for terminating the searching process when the preset termination condition is reached, and outputting the final resource configuration scheme as an optimization result.
5. The resource allocation device based on multi-index clustered collaborative search according to claim 4 wherein the second processing module is configured to define a real-time matching index (RMI) for measuring a degree of matching between a current configuration state of a resource object and a target requirement, wherein the method is as follows: ; Wherein, the Represent the first The number of resource objects that have been currently matched, Representing a corresponding target demand; The second processing module is configured to define a Value Contribution Index (VCI) for measuring potential contribution capability of the resource object in the overall configuration, where the calculation mode is as shown in formula (2): ; Wherein, the Represent the first The combined contribution value of the individual resource objects, Representing the total number of resource objects.
6. The resource allocation device based on multi-index clustered collaborative search according to claim 5 wherein the fourth processing module is configured to optimize an objective function expressed as equation (3): ; Wherein, the Representing resource objects The utility function in the current configuration, Is a weight coefficient; The optimization process needs to meet the resource constraint and the adjustment constraint at the same time, and the constraint condition is shown in a formula (4): ; Wherein, the Represents the total budget upper limit for the resource, Representing the configuration values of the resource object in the previous iteration, Representing the maximum allowable adjustment amplitude; Through coordination control of different clustering search results, the final resource allocation scheme optimizes the objective function value while meeting the constraint condition of the formula (4) 。
7. A resource allocation system based on multi-index clustered co-search, comprising a memory and a processor, the memory having stored thereon a computer program for execution by the processor, the computer program when executed by the processor performing the multi-index clustered co-search based resource allocation method of any of claims 1-3.

Description

Resource allocation method, device and system based on multi-index grouping collaborative search Technical Field The invention belongs to the technical field of resource allocation, and particularly relates to a resource allocation method, device and system based on multi-index grouping collaborative search. Background With the development of informatization and intellectualization technologies, resource allocation problems are widely modeled as optimization problems that can be handled by computers and solved by heuristic or group intelligent algorithms. In constrained resource configuration scenarios, the prior art generally employs a rule-based decision method or a general-based population intelligent optimization method in order to obtain a viable solution under complex constraint conditions. Among them, a Greedy Algorithm (Greedy Algorithm) is a typical heuristic method for gradually constructing a solution by selecting a current optimal local decision at each step. The method has higher calculation efficiency and easy realization, but due to the lack of global searching capability, local optimization is easy to fall into, high-quality solutions are difficult to obtain in the problems of multi-constraint and multi-target resource allocation, and particularly, when object heterogeneity is strong or constraint conditions are complex, the optimization effect is obviously insufficient. In order to overcome the limitations of the greedy method, general population intelligent Optimization methods such as genetic algorithm (Genetic Algorithm, GA) and Particle Swarm Optimization (PSO) are widely adopted in the prior art. The method performs global search in the solution space through a group evolution or group cooperation mechanism, has a certain ability of jumping out of local optimization, and is applied to various resource allocation and combination optimization problems. However, the existing general group intelligent algorithm generally regards decision variables as homogeneous objects, and adopts a unified search strategy, so that the variability of different resource objects in actual application and the different contributions of the different resource objects to the whole target cannot be fully considered. In addition, in the actual resource configuration scenario, there are often constraints such as limited computational budget, limited adjustment range, and higher stability requirement of the solution. The intelligent group algorithm in the prior art focuses on searching the optimal solution in the solution space, and is less specially designed for the stability, progressive adjustment characteristics and strategy implementation of the solution. This results in either insufficient search efficiency in cases of limited computational resources, or the resulting solution is difficult to implement smoothly in practical applications, thus reducing the practical value of the algorithm. Thus, the prior art has the following deficiencies in the constrained resource allocation problem: (1) The rule-type or greedy method has insufficient global optimization capability, and is difficult to obtain a high-quality solution; (2) The general group intelligent algorithm lacks of structural utilization of the aberration of resources, and has limited searching efficiency; (3) The quality, stability and feasibility of solutions cannot be considered under a limited computational budget. For the above reasons, it is necessary to provide a resource allocation method and system capable of grouping resource objects by combining multiple indexes under the condition of limited computing resources and improving the optimization efficiency and solution stability by a collaborative search mechanism, so as to overcome the defects in the prior art. Disclosure of Invention In order to solve the problems in the prior art, the invention provides a resource allocation method, a device and a system based on multi-index grouping collaborative search, which can improve the solving efficiency, the stability and the practical feasibility of the resource allocation problem under the condition of limited computing resources. In order to achieve the above object, the present invention provides the following solutions: a resource allocation method based on multi-index grouping collaborative search comprises the following steps: Step S1, acquiring a resource object set to be configured; step S2, calculating a plurality of evaluation indexes for describing the current matching state and potential contribution capacity of each resource object; S3, performing collaborative clustering on the resource objects according to a preset threshold or a clustering rule based on the real-time matching fingers and the value contribution index; Step S4, aiming at different groups, performing differentiated search operation, and uniformly adjusting search results through a coordination control mechanism; and S5, when the preset termination condition is reached,