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A Query Optimization Method Based on Improved Genetic Algorithm

A query optimization and genetic algorithm technology, applied in the fields of genetic law, calculation, genetic model, etc., can solve the problems of inaccurate search of local extreme values, prolong the search time, reduce the search accuracy, etc., so as to improve the query speed and shorten the search. time, the effect of improving search accuracy

Active Publication Date: 2018-10-02
KUNMING UNIV OF SCI & TECH
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AI Technical Summary

Problems solved by technology

As far as the genetic algorithm is concerned, it also has certain limitations, and it is easy to encounter the "premature" problem, that is, it only oscillates near the optimal solution in the later stage of the search, and falls into a local extremum and cannot accurately search for the global optimal solution. This not only prolongs the search time but also reduces the search accuracy.

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  • A Query Optimization Method Based on Improved Genetic Algorithm
  • A Query Optimization Method Based on Improved Genetic Algorithm
  • A Query Optimization Method Based on Improved Genetic Algorithm

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Embodiment 1

[0027] Embodiment 1: as Figure 1-3 As shown, a query optimization method based on an improved genetic algorithm, the steps of the query optimization method are as follows:

[0028] Step1. Initial parameter setting: set the number of iterations G, the selection probability pc, the mutation probability pm, and define a query strategy set D as a search space. The query strategy in D requires n steps to complete the query task;

[0029] Step2. Establish a query strategy cost evaluation model: If the query strategy requires n steps to complete, then there are n+1 nodes in the query strategy space, the cost between two nodes is d, and the cost function is:

[0030]

[0031] Among them, X={x 1 ,x 2 ,...x i … x n+1} is the solution of the cost function, and x 1 ,x 2 ,...x n+1 Different from each other; the collection of all solutions is called the solution space, which means the query strategy set D; the nodes in the model represent the query state, and traversing n+1 nodes...

Embodiment 2

[0043] Embodiment 2: as Figure 1-3 As shown, a query optimization method based on an improved genetic algorithm, the steps of the query optimization method are as follows:

[0044] Step1. Initial parameter setting: set the number of iterations G, the selection probability pc, the mutation probability pm, and define a query strategy set D as a search space. The query strategy in D requires n steps to complete the query task;

[0045] Step2. Establish a query strategy cost evaluation model: If the query strategy requires n steps to complete, then there are n+1 nodes in the query strategy space, the cost between two nodes is d, and the cost function is:

[0046]

[0047] Among them, X={x 1 ,x 2 ,...x i … x n+1} is the solution of the cost function, and x 1 ,x 2 ,···x n+1 Different from each other; the collection of all solutions is called the solution space, which means the query strategy set D; the nodes in the model represent the query state, and traversing n+1 nodes...

Embodiment 3

[0056] Embodiment 3: as Figure 1-3 As shown, a query optimization method based on an improved genetic algorithm, the steps of the query optimization method are as follows:

[0057] Step1, initial parameter setting. A query strategy set D is defined as a search space, and the query strategies in D need 9 steps to complete the query task.

[0058] Step1.1. Determine initial parameters: number of iterations G=100, selection probability pc=0.2, mutation probability pm=0.4.

[0059] Step2. Establish a query strategy cost evaluation model. The time consumed by a certain query strategy to obtain the query result is the cost of this query strategy, and the function to calculate the cost of the query strategy is the cost function. A solution of the cost function provides a query strategy, and the query strategy set D is the solution space of the cost function.

[0060] Assuming that the query strategy requires n=9 steps to complete, then there are 10 nodes in the query strategy sp...

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Abstract

The invention relates to a query optimization method based on an improved genetic algorithm, and belongs to the technical field of query optimization. The method comprises: establishing a mathematical model for a query execution policy set, to transform a query optimization problem into a mathematical problem of finding a global optimal solution, wherein the model is called a cost evaluation model of a query policy; and then improving a genetic algorithm, and using a global search ability of the improved genetic algorithm to perform a parallel search on a query policy set, thereby finally obtaining an ideal query execution policy. According to the method provided by the invention, a conventional genetic algorithm is improved, and the improved genetic algorithm is used for query optimization of a large relational database, thereby overcoming a "premature" convergence phenomenon. Compared with other intelligent optimization algorithms, falling into a local extremum can be effectively avoided, thereby shortening search time. In addition, a gene based search policy and a polyploidy based retention policy in the algorithm greatly improve search accuracy.

Description

technical field [0001] The invention relates to a query optimization method based on an improved genetic algorithm, belonging to the technical field of query optimization. Background technique [0002] Since the late 1960s, database technology has experienced more than 40 years of development and has become an important core technology of modern computer systems. Among them, the relational database is a mainstream database based on mathematical concepts, which can directly describe the actual relationship and has high access efficiency. But at the same time, relational databases also have corresponding defects. Their data structures are relatively complex, especially for large-scale relational databases. It is extremely complicated, and the query efficiency is low when performing multi-link query. However, it can be seen from the application examples of most database systems that the query operation occupies the largest proportion in various database operations, and the da...

Claims

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Application Information

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Patent Type & Authority Patents(China)
IPC IPC(8): G06F17/30G06N3/12
CPCG06F16/2453G06N3/126
Inventor 邵剑飞任修仕
Owner KUNMING UNIV OF SCI & TECH
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