An ant colony algorithm based on search concentration and dynamic pheromone updating

A technology of dynamic information and ant colony algorithm, applied in the field of artificial intelligence, can solve problems such as long system operation period, prevent solution space search, and stagnation, and achieve the effect of improving global search capabilities, shortening search time, and improving operating efficiency

Inactive Publication Date: 2019-01-15
KUNMING UNIV OF SCI & TECH
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Problems solved by technology

But at the same time, the ant colony algorithm also has certain limitations. For example, the lack of pheromones in the initial stage leads to the blindness of most ants' searches. The running cycle of the system is too long and the convergence speed is slow
In addition, with the expansion of the scale of the problem, after the ants search

Method used

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  • An ant colony algorithm based on search concentration and dynamic pheromone updating
  • An ant colony algorithm based on search concentration and dynamic pheromone updating
  • An ant colony algorithm based on search concentration and dynamic pheromone updating

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Embodiment 1

[0044] Embodiment 1: as Figure 1-5 as shown, figure 1 The optimization method flow of the present invention is shown. In the present invention, we use MATLAB simulation software to simulate and calculate, as figure 1 As shown, the ant colony algorithm optimization method based on search concentration and dynamic pheromone update first performs environment modeling and initialization, and then performs search iterations. After each round of iterations is completed, the optimal path information is counted. optimization, and finally get the optimal solution. The specific steps are described in detail below:

[0045] Step1. Initialize the TSP problem definition data. The example uses the TSP problem standard dataset Oliver30, such as figure 2 As shown, it is the coordinate point positions of each city. First, the distance between any two cities is calculated by using each coordinate point and put into the city distance matrix D; the basic parameters of the ant colony algorit...

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Abstract

The invention discloses an ant colony algorithm optimization method based on searching concentration degree and dynamic pheromone update, belonging to the artificial intelligence field. Compared withthe classical ant colony algorithm, this method has the following improvements: (1) according to the path found by the previous generation of ant colony, the search concentration factor is introducedinto the selection strategy, which can adjust the range of ant selection node adaptively; (2) In the global pheromone updating phase, the search time is shortened by changing the pheromone increment dynamically; (3) the pheromone rollback mechanism is used to jump out of the local extremum when the optimal solution is not improved after many iterations. Through the above improvements, the invention obviously improves the global searching ability and the running efficiency compared with the basic ant colony algorithm.

Description

technical field [0001] The invention relates to an ant colony algorithm optimization method based on search concentration and dynamic pheromone update, belonging to the field of artificial intelligence. Background technique [0002] With the rapid development of science and technology, the traditional control theory based on the precise model of the object and the optimization algorithm using determinism have encountered great difficulties. So people got inspiration from the intelligent control behavior of simulating human beings, combined artificial intelligence with automatic control theory, and created the intelligent control theory; people were inspired by biological evolution and bionics, and proposed many heuristic intelligent optimization methods. Such as taboo algorithm, neural network algorithm, genetic algorithm, immune algorithm and ant colony algorithm, etc., they provide a new way to solve many complex optimization problems. [0003] Among them, the ant colony ...

Claims

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Application Information

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IPC IPC(8): G06N3/00
CPCG06N3/006
Inventor 钱谦王晓婷
Owner KUNMING UNIV OF SCI & TECH
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