Table of contents
Back view
The principle and steps of ant colony algorithm
Basic definition
Programming ideas
Fitness function Algorithm
rules
Features
Main parameters
Code
Result analysis
Outlook
back view
The grid path optimization has the characteristics of large amount of calculation, many possible solutions, high difficulty to solve, and many feasible solutions. Generally, it cannot be solved directly. In this paper, the ant colony algorithm is used to solve the problem. The ant colony algorithm has better global optimization capabilities.
Ant Colony Algorithm
basic definition
The basic idea of applying the ant colony algorithm to solve the optimization problem is: use the walking path of the ants to represent the feasible solution of the problem to be optimized, and all the paths of the entire ant colony constitute the solution space of the problem to be optimized. Ants with shorter paths released more pheromones. As time went on, the concentration of pheromones accumulated on shorter paths gradually increased, and the number of ants who chose this path also increased. In the end, the entire ant will concentrate on the best path under the action of positive feedback, which corresponds to the optimal path of the problem to be optimized.
programming ideas
Ants find the shortest path thanks to pheromones and the environment. Suppose there are two paths leading from the ant nest to food. At the beginning, the number of ants on the two paths is about the same: when the ants reach the end, they will return immediately, and the ants on the short path The round-trip time is short and the repetition frequency is fast. The number of ants going back and forth per unit time will be more, and more pheromones will be left, which will attract more ants and leave more pheromones. The long distance path is the opposite, so more and more ants gather on the shortest path.
Ants possess intelligent behavior thanks to their simple behavioral rules, which allow for diversity and positive feedback. When foraging, diversity prevents ants from going into a dead end and infinite loop, which is a kind of innovative ability; positive feedback keeps good information, which is a kind of learning enhancement ability. The ingenious combination of the two makes intelligent