1. Introduction to the algorithm
Ant colony optimization (ACO), also known as ant algorithm, is a probabilistic algorithm used to find optimized paths in the graph . Its inspiration comes from the behavior of ants finding paths in the process of searching for food.
Ant colony algorithm is a simulation evolution algorithm .
Second, the basic principle of the algorithm
Ants release a substance called " pheromone " during walking, which is used to mark their walking path.
In the process of looking for food, choose the direction of walking according to the concentration of pheromone , and finally reach the place where the food is.
Pheromone will gradually volatilize over time .
Therefore, when ant 1 arrives at the food location and chooses the way back, it will choose the road on the right (high pheromone concentration). Over time, the concentration of the road on the right gradually increases, and all ants will choose this shorter path. .
Three, algorithm characteristics
1, self-organizing algorithm: self-organization is such that no outside process of the system under the action of reduced entropy (i.e. the system from disorder to order change process).
2. Parallel algorithm: The search process of each ant is independent of each other , and only communicates through pheromone. At the same time, the independent solution search is started, which increases the reliability of the algorithm and also makes the algorithm have a strong global search ability .
3. Positive feedback algorithm: Ants can finally find the shortest path, which directly depends on the accumulation of pheromone on the shortest path, and the accumulation of pheromone is a process of positive feedback . Positive feedback is an important feature of ant algorithm, which enables the evolution of the algorithm to proceed.