Task scheduling method based on greedy adaptive ant colony algorithm

An ant colony algorithm and task scheduling technology, which is applied in computing, computing models, data processing applications, etc., can solve problems such as being unsuitable for global optimization, and achieves the effect of strong versatility

Pending Publication Date: 2020-11-20
BEIJING UNIV OF TECH
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The advantage is that the solution speed is fast, but because the algorithm is not considered from the perspective of overall optimiz

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  • Task scheduling method based on greedy adaptive ant colony algorithm
  • Task scheduling method based on greedy adaptive ant colony algorithm
  • Task scheduling method based on greedy adaptive ant colony algorithm

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Embodiment Construction

[0020] The present invention will be described in detail below in conjunction with examples and drawings.

[0021] The implementation of the present invention only takes the solution of the logistics resource scheduling problem as an example, but the algorithm itself is widely applicable to various task scheduling problems with constraints. Such as figure 1 The model shown has a distribution center and several customer nodes to be distributed (hereinafter referred to as nodes), and the position coordinates and resource requirements of each node are known. The constraint here is that the load of the car is limited. In the example, it is set to 100t, so it is impossible to complete all the delivery tasks with only one car. Therefore, a feasible solution must contain multiple paths, and the paths in these feasible solutions pass The nodes of must add up to exactly all distribution nodes. The final optimization requirement is to complete the cargo dispatch task with the smallest to...

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Abstract

The invention discloses a task scheduling method based on a greedy adaptive ant colony algorithm, belongs to the field of cluster intelligent algorithms, and is mainly used for optimizing the execution efficiency and optimization capability of the ant colony algorithm. Firstly, the greedy algorithm is introduced to accelerate the initialization speed of the ant colony algorithm, so that the ant colony algorithm performs iterative optimization on the basis of the optimal solution of the greedy algorithm, and the optimal iterative efficiency of solving is improved; in the execution stage, an efficiency factor and a volatilization coefficient capable of being adaptively adjusted are also added to accelerate the optimization speed of the ant colony algorithm. The efficiency factor enables theselected node to be more reasonable, and the adaptive adjustment mechanism of the volatilization coefficient enables the algorithm to fully utilize the information of the front and back scheduling results to adjust the volatilization coefficient in a targeted manner, thereby adjusting the optimization direction. An ant colony relay is introduced into the ant colony algorithm to solve the problem that task scheduling cannot be completed by a single ant path under the constraint condition.

Description

Technical field [0001] The invention belongs to the field of cluster intelligent algorithms, and is mainly used for optimizing the execution efficiency and optimization ability of the ant colony algorithm. Background technique [0002] Ant colony algorithm is a heuristic combinatorial optimization algorithm based on random search to simulate the foraging behavior of ants. The ants in the ant colony communicate through pheromone, and in the process of searching for food sources, they release pheromone and remain on the path the ant walked. The shorter the path, the more ants pass per unit time, the higher the concentration of pheromone released, and the stronger the attraction to later ants. In the end, all ants choose this path, that is, a path is determined between the nest and the food source. The shortest path. The advantage of ant colony algorithm is that it can handle very complex combinatorial optimization problems without complex mathematical models and complicated param...

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Application Information

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IPC IPC(8): G06Q10/04G06N3/00
CPCG06N3/006G06Q10/04G06Q10/047
Inventor 刘博李玉金
Owner BEIJING UNIV OF TECH
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