Vector database index parameter self-optimization system and method based on multi-objective evolutionary algorithm

Abstract

The application discloses a vector database index parameter self-optimization method and system based on a multi-objective evolutionary algorithm. In view of the high super parameter optimization threshold and complex dimension of a vector database under different hardware resources and data scales, the application proposes an adaptive optimization mechanism based on an improved non-dominated sorting genetic algorithm (NSGA-II). The system constructs recall rate, query delay, memory occupation and hardware power consumption of vector retrieval as a multi-dimensional objective function in conflict with each other, and automatically explores and generates a Pareto optimal parameter set in different deployment environments through a designed hardware-aware mutation strategy, including memory topology mutation and calculation cycle mutation. The application significantly reduces the operation and maintenance complexity of a vector database cluster, and ensures the optimal balance between query performance and resource consumption under a data scale of 10 billion.

Description

Technical Field

[0001] This invention relates to the fields of artificial intelligence architecture, database adaptive optimization, and high-performance computing technology, specifically to a multi-objective self-optimization control system and method for index hyperparameters of large-scale vector databases. Background Technology

[0002] As a core infrastructure for large language models and artificial intelligence agents, vector databases' query performance is highly dependent on the hyperparameter configuration of the underlying indexing algorithm. Taking the mainstream hierarchical navigable small-world graph index as an example, its key hyperparameters include: efConstruction, which controls the size of the candidate set during graph construction; M, which controls the maximum number of connections between nodes; and efSearch, which controls the size of the candidate set during the query phase.

[0003] In production environments with hundreds of millions of users, due to the significant heterogeneity of the hardware environment (such as differences in processor cache topology, memory bandwidth, and support for single instruction multiple data stream instruction sets among different cloud instances) and the real-time shift in data distribution characteristics as business evolves, the single fixed parameter configuration provided by index vendors often cannot achieve the optimal performance balance in a specific deployment environment.

[0004] In existing technologies, hyperparameter tuning for vector indexes mainly relies on two methods: First, manual stress testing, where operations personnel manually modify parameters, run benchmark tests, and observe performance metrics through trial and error. This method is inefficient and heavily dependent on personal experience. Second, grid search or random search, which involves exhaustively searching or randomly sampling combinations within a preset parameter value range for evaluation. This method can only handle single-objective optimization problems and cannot effectively balance the nonlinear game relationship between multiple conflicting performance metrics such as query latency, recall, memory overhead, and processor power consumption. Under extreme system loads, suboptimal parameter configurations will lead to significant performance degradation.

[0005] Therefore, how to provide a vector index hyperparameter optimization method that can automatically perceive hardware environment and data distribution characteristics and automatically find the optimal balance point among multiple conflicting performance indicators is a technical problem that urgently needs to be solved in this field. Summary of the Invention

[0006] 3.1 Technical Issues This invention aims to solve the technical problem in the prior art that the hyperparameter tuning of vector database indexes relies on human experience and cannot take into account multiple conflicting performance indicators.

[0007] 3.2 Technical Solution This invention provides a multi-objective adaptive optimization system and method for vector index parameters based on an improved non-dominated sorting genetic algorithm. Its core idea is as follows: different combinations of index hyperparameters are encoded as individuals in the genetic algorithm population; during the evaluation phase, each individual undergoes benchmark testing in an isolated sandbox environment, collecting multi-dimensional performance indicators including query latency, memory usage, recall rate, and processor power consumption as optimization objectives; through fast non-dominated sorting and crowding distance calculation, the population is guided to evolve towards the Pareto optimal front; during the mutation phase, a hardware-aware strategic mutation operator is introduced to fine-tune the index structure parameters according to the host processor's hardware constraints.

[0008] Specifically, the technical solution of the present invention includes the following key mechanisms: (1) Individual chromosome encoding: The set of index hyperparameters to be optimized and the underlying memory alignment flags are encoded into fixed-length chromosomes, and each chromosome uniquely corresponds to a complete set of index microstructure control schemes.

[0009] (2) Sandbox benchmark test loop: The optimization engine periodically triggers micro-benchmark tests in an isolated sandbox environment, and collects telemetry data from real hardware feedback in real time as the basis for individual fitness scores.

[0010] (3) Multidimensional fitness evolution and Pareto optimal solution set search: Query latency, memory footprint, recall rate and processor energy consumption are taken as independent optimization objectives to construct a multidimensional objective space; through fast non-dominated sorting, the Pareto optimal frontier of multiple objectives mutually restraining each other is automatically searched, providing the system with diversified indexing strategy options.

[0011] (4) Pareto front dynamic reconstruction: The system periodically re-evaluates the existing solution set based on changes in data distribution; if a shift in the current semantic distribution is detected, the evolution logic is automatically restarted to correct the Pareto curve and ensure that the system always runs in the optimal performance range.

[0012] (5) Automatic dispatch of decision operators: The system can dynamically select the most suitable parameter configuration from the Pareto optimal solution set according to the current service level agreement requirements.

[0013] 3.3 Beneficial Effects Compared with the prior art, the present invention has the following beneficial effects: (1) High degree of automation: No human intervention is required. The system can automatically sense the operating environment and complete the entire process optimization, which greatly reduces the threshold for deployment and maintenance of vector databases.

[0014] (2) Multi-objective trade-off: Output a set of Pareto optimal solutions instead of a single optimal solution, allowing the system to choose between recall, latency, memory and energy consumption as needed to meet the differentiated needs of different business scenarios.

[0015] (3) Hardware awareness: The strategic mutation operator can sense and utilize the characteristics of the underlying hardware, and the optimization results are deeply adapted to the hardware environment, achieving significant performance improvement in different cloud instances or physical machine environments.

[0016] (4) Dynamic Adaptation: Through the dynamic reconstruction mechanism of Pareto front, the system can automatically correct the optimization direction when the data distribution shifts, ensuring continuous operation in the optimal performance range. Attached Figure Description

[0017] Figure 1 The flowchart below shows the overall process of a multi-objective adaptive optimization method for vector index parameters based on an improved non-dominated sorting genetic algorithm, as provided in an embodiment of the present invention.

[0018] Figure 2 This is a schematic diagram of the chromosome coding structure and Pareto optimal frontier distribution in one embodiment of the present invention. Detailed Implementation

[0019] To make the objectives, technical solutions, and advantages of the present invention clearer, the present invention will be further described in detail below with reference to the accompanying drawings and specific embodiments.

[0020] 5.1 System Architecture and Operating Environment The multi-objective adaptive optimization system provided by this invention serves as the optimization engine implementation of a vector database kernel. In this embodiment, the VecminDB vector database implemented in Rust is used as an example for illustration. The core optimization logic of this invention is implemented in a closed loop within the optimization kernel module src / algorithm / optimizer / , which mainly includes: a population management submodule, a fitness evaluation and scheduling submodule, a sorting and selection submodule, and a strategic mutation submodule.

[0021] The system mainly includes the following functional components: Chromosome encoding module: responsible for encoding index hyperparameters and memory alignment flags into fixed-length chromosomes.

[0022] Sandbox Benchmarking Module: Responsible for running micro-benchmarks in an isolated environment and collecting real hardware telemetry data.

[0023] Fitness assessment module: responsible for calculating the multidimensional fitness score of an individual based on telemetry data.

[0024] Pareto sorting and selection module: responsible for performing fast non-dominated sorting and crowding distance calculation to select superior individuals.

[0025] Strategic mutation module: Responsible for performing targeted mutation operations based on hardware constraints.

[0026] Dynamic refactoring trigger: Responsible for monitoring data distribution shifts and restarting the evolution process when necessary.

[0027] 5.2 Individual Chromosome Encoding In this embodiment, the hyperparameters of the hierarchical navigable small world graph index and the underlying memory alignment flags are encoded as logical chromosomes. A chromosome contains at least the following gene loci: efConstruction: Integer, value range [100, 2000] M: Integer, value range [4, 64] cache_alignment_flag: Boolean, indicating whether to enable line alignment optimization for the processor's L3 cache. The combination of the aforementioned gene loci uniquely determines a complete index microstructure control scheme. By incorporating memory alignment flags into the encoding, this invention enables precise control over the underlying storage layout of the index, thereby sensing and adapting to the cache topology characteristics of different processors.

[0028] 5.3 Sandbox Benchmark Test Circuit The optimization engine periodically triggers micro-benchmark tests in an isolated sandbox environment. The sandbox environment shares the same hardware resources as the online service but uses independent test datasets and query loads to ensure that the testing process does not affect the normal operation of the online service.

[0029] The micro-benchmark simulates a mixed read / write load in a real-world business scenario, including point queries, range queries, and batch insert operations. During test execution, the system collects the following telemetry data from real hardware feedback in real time: Query latency: Calculated as the 99th percentile latency value, in milliseconds. Memory footprint: The total memory usage of the index structure and its metadata, in megabytes. Recall rate: The percentage of true nearest neighbor recalls compared to brute-force search results, expressed as a percentage. Processor power consumption: Processor package power consumption value read from the average power limit interface. After normalization, the above telemetry data constitutes the basis for the multidimensional fitness score of an individual.

[0030] 5.4 Dynamic Reconstruction of the Pareto Front The system periodically re-evaluates the existing Pareto optimal solution set based on changes in data distribution. Specifically, the system continuously monitors the feature distribution of the query load and the clustering characteristics of the vector data. If a significant shift in the current semantic distribution relative to the baseline distribution during the previous evolution is detected, the evolutionary logic is automatically restarted.

[0031] After restarting, the system re-executes the complete evolutionary process, including population initialization, fitness evaluation, non-dominated sorting, and strategic mutation, based on the current hardware environment and data distribution, generating a new Pareto optimal frontier. This dynamic reconfiguration mechanism ensures that the system always operates within its optimal performance range in a production environment where the data distribution continues to evolve.

[0032] 5.5 Automatic Dispatch of Decision Operators When the system needs to adjust the index configuration according to the current service level agreement requirements, the decision operator dynamically selects the most suitable parameter combination from the Pareto optimal solution set.

[0033] For example, during periods of low system load and insensitivity to query latency, the decision operator can switch to a high energy efficiency configuration to reduce processor power consumption and memory usage; during peak business periods, the decision operator can switch to an extremely low latency configuration to ensure user experience. Because the Pareto optimal solution set provides configuration options with different trade-offs among multiple objectives, the system can flexibly switch according to real-time needs without re-executing the time-consuming parameter search process.

[0034] 5.6 Industrial Application Validation The technical solution of this invention has been fully implemented and deployed in the VecminDB vector database kernel. Comparative tests on various heterogeneous cloud instances show that, compared with the default parameter configuration provided by index vendors, the overall resource utilization of this invention is improved by more than 30%, and the P99 query latency is reduced by approximately 25%. This technology significantly lowers the deployment and maintenance threshold for large-scale vector intelligence applications and is a key underlying infrastructure for realizing the "environment-adaptive memory" of artificial intelligence agents.

[0035] The above description is merely a preferred embodiment of the present invention and does not limit the patent scope of the present invention. Any equivalent structural or procedural modifications made based on the description and drawings of the present invention, or direct or indirect applications in other related technical fields, are similarly included within the patent protection scope of the present invention.

Claims

1. A vector database index parameter self-optimization system based on a multi-objective evolutionary algorithm, characterized in that, include: The initial population generation unit is used to generate a population of candidate individuals with different combinations of configuration parameters for a specific vector database index. The multidimensional fitness evaluation unit is used to measure and output evaluation scores for each candidate individual in a real or simulated computing environment, including at least multiple conflict objectives such as retrieval recall, response latency, and system resource consumption. The Pareto front-end analysis unit is configured to perform non-dominated ranking based on the evaluation scores of the plurality of conflict targets, dividing the candidate individual population into different levels of non-dominated fronts; The genetic evolution scheduling unit is used to select for survival based on the non-dominance level and crowding distance of individuals, and to perform hardware-policy-aware mutation and crossover operations to generate a new generation of population.

2. The system according to claim 1, characterized in that, The multidimensional fitness evaluation unit integrates a hardware heatmap monitor, which is used to provide real-time feedback on processor cycle consumption and memory bus usage, and normalize the processor cycle consumption and memory bus usage into an energy expenditure target.

3. The system according to claim 1, characterized in that, When performing mutation operations, the genetic evolution scheduling unit adopts a memory topology alignment strategy, that is, it uses the memory mapping alignment method of the inode as the mutation gene to optimize the processor's L3 cache hit rate.

4. The system according to claim 1, characterized in that, The genetic evolution scheduling unit uses a crowding distance threshold to remove overly dense individuals in the target space in order to force the dispersion of the Pareto front.

5. A vector index optimization method using the system described in any one of claims 1 to 4, characterized in that, Includes the following steps: Through periodic background evolution cycles, the parameters of active indexes are dynamically shifted and smoothly switched online without interrupting online services, thereby ensuring that index performance consistently meets the standards.

6. The method according to claim 5, characterized in that, The online drift and smooth switching includes: After the background evolutionary loop converges, a Pareto optimal configuration list is generated. Receive user-selected preference tags; Smoothly replace the hyperparameters of the currently active index with the target configuration, while maintaining the online queryability of the index during the replacement process.

7. The method according to claim 5, characterized in that, The background evolution loop includes: Generate a population of candidate individuals containing different combinations of index hyperparameters; Benchmark tests were run on each candidate in a sandbox environment, and multi-dimensional performance metrics, including recall, query latency, and memory usage, were collected. Based on the aforementioned multidimensional performance indicators, a fast non-dominated sort is performed to divide the population into Pareto fronts of different levels. Selection is performed based on the Pareto rank and crowding distance of individuals, and a new generation of population is generated through strategic mutation and crossover.

8. A computer-readable storage medium having computer program instructions stored thereon, characterized in that, When the computer program instructions are executed by the processor, they implement the method as described in any one of claims 5 to 7.

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