Video server location selection method based on genetic ant colony hybrid algorithm

Abstract

The invention relates to a video server location selection method based on a multi-constraint priority genetic ant colony hybrid algorithm. The hybrid algorithm carries out multiple times of combination on an improved genetic algorithm and an ant colony optimization algorithm, the improved genetic algorithm is executed firstly, a group of problem near-optimum solutions generated by the improved genetic algorithm is converted to initial pheromone distribution of the ant colony algorithm; and then, a genetic ant colony hybrid algorithm based on a multi-constraint priority distribution strategy is executed. The shortcoming that the single-constraint priority distribution strategy in the traditional genetic algorithm can not find out the problem optimum solution is overcome, and the problems that the traditional genetic algorithm adopts a roulette wheel selection operator, the colony diversity is reduced, and falling into local optimum easily happens can be solved.

Description

technical field [0001] The invention relates to the technical field of computer-executed facility site selection, in particular to a video server site selection method based on a genetic ant colony mixing algorithm. Background technique [0002] With the development of science and technology, people can more conveniently obtain the information they need from the Internet. Among them, video information plays an important role in people's lives, and people have higher and higher requirements for video information. Therefore, how to better meet people's demand for video playback is an urgent problem to be solved, and the most important thing to solve this problem is the selection of the location of the video content storage server. Therefore, there are many ways to solve the location problem of video content storage server. After studying the location problem of video content storage server, it can be classified as the constrained p-median problem in the location problem, and ...

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Problems solved by technology

[0003] However, there are more or less problems in the above algorithms. For example, the genetic algorithm adopts a single constraint priority allocation strategy when solving the video content storage server location, and the basic roulette wheel selection operator, the algorithm convergence speed is slow, It is easy to fall into a local optimum, which leads to an increase in investment costs. When the ant colony algorithm solves the location of the video content storage server, the expected heuristic factor for the location selection of the video server uses the ability of the video server to make the location selection unreasonable, and due to the early pheromone The lack of and randomness of the lead to a lower convergence rate

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