Video Server Site Selection Method Based on Genetic Ant Colony Hybrid Algorithm

Abstract

The invention relates to a video server site selection method based on a multi-constraint priority genetic ant colony hybrid algorithm. The hybrid algorithm combines the improved genetic algorithm with the ant colony optimization algorithm multiple times. The near-optimal solution of a group of problems is transformed into the initial pheromone distribution of the ant colony algorithm, and then the genetic ant colony hybrid algorithm based on the multi-constraint priority assignment strategy is executed. The present invention overcomes the disadvantage that the single-constrained priority allocation strategy of the traditional genetic algorithm cannot find the optimal solution to the problem, and the selection operator of the roulette wheel used in the traditional genetic algorithm reduces the diversity of the population and easily falls into the problem of local optimum .

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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