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A distributed database query optimization method based on a multi-ant colony genetic algorithm

A genetic algorithm and query optimization technology, applied in the field of Internet databases

Inactive Publication Date: 2019-04-23
CHANGZHOU INST OF TECH RES FOR SOLID STATE LIGHTING
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Problems solved by technology

[0007] The purpose of the present invention is to provide a distributed database query optimization method based on multi-ant colony genetic algorithm, which solves the technical problem of using multi-ant colony algorithm to improve the efficiency of distributed database query

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  • A distributed database query optimization method based on a multi-ant colony genetic algorithm
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  • A distributed database query optimization method based on a multi-ant colony genetic algorithm

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Embodiment Construction

[0036] The basic idea of ​​applying the ant colony algorithm to solve the optimization problem is: use the walking path of the ants to represent the feasible solution of the problem to be optimized, and all the paths of the entire ant colony constitute the solution space of the problem to be optimized. Ants with shorter paths released more pheromones. As time went on, the concentration of pheromones accumulated on shorter paths gradually increased, and the number of ants who chose this path also increased. In the end, the entire ants will concentrate on the best path under the action of positive feedback, which corresponds to the optimal solution of the problem to be optimized. Ants find the shortest path thanks to pheromones and the environment. Suppose there are two paths leading from the ant nest to food. At the beginning, the number of ants on the two paths is about the same: when the ants reach the end, they will return immediately, and the ants on the short path The roun...

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Abstract

The invention discloses a distributed database query optimization method based on a multi-ant colony genetic algorithm, and belongs to the internet database technology field. The method comprises thefollowing steps of establishing a distributed database framework; analyzing the query cost of the distributed database, and then updating the ant colony algorithm into a multi-ant colony algorithm; utilizing a smoothing mechanism and a multi-ant-colony mutual learning mechanism to avoid falling into local optimum and premature phenomena, so that the global search capability of the whole algorithmis improved. According to the present invention, the technical problem of improving the query efficiency of the distributed database by adopting the multi-ant-colony algorithm is solved, by introducing the multi-ant-colony algorithm, a learning operator is provided in the algorithm, the sub-ant-colony is mutually learned, the local optimum is prevented, the algorithm performance is improved, and the algorithm can obtain a better global optimum solution.

Description

technical field [0001] The invention belongs to the technical field of Internet databases, in particular to a distributed database query optimization method based on multi-ant colony genetic algorithm. Background technique [0002] An important feature of a distributed database is that most of the content it processes does not come from one place, and its query operation has the following characteristics: data is transferred between networks, and after the transfer is completed, it is processed locally. However, when a query relationship increases with the number of connected relationships, the cost of executing the query operation will increase exponentially, which causes the complexity of distributed database query technology. [0003] Algorithms that can help optimize queries currently fall into two categories: deterministic algorithms and stochastic algorithms. [0004] Each step of the deterministic algorithm is like searching for the root cause, and the algorithm is n...

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06F16/2453G06F16/2458G06N3/00G06N3/12
CPCG06N3/006G06N3/126
Inventor 马锐王鑫苏静濮斌
Owner CHANGZHOU INST OF TECH RES FOR SOLID STATE LIGHTING
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