What is website optimization "Ant colony algorithm" and its characteristics

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To understand the relationship between ant colony algorithm and SEO, we should first look at the origin of the ant colony algorithm: Ants are one of the most common and most numerous insect species on earth, often appearing in droves in the human life environment. The Swarm bio-intelligence characteristics of these insects have attracted the attention of some scholars. M.dorigo,v.maniezzo, an Italian scholar, observes ants ' foraging habits and finds that ants can always find the shortest path between nests and food sources.

It has been found that this group collaboration function of ants is communicated and coordinated by a volatile chemical substance called pheromone (pheromone) left on its path. Chemical communication is one of the basic information communication methods that ants take, which plays an important role in the life habits of ants. Through the study of ants foraging behavior, they found that the whole ant colony is through this pheromone to cooperate with each other to form positive feedback, so that the ants on multiple paths gradually gather to the shortest path.

In this way, M.dorigo first proposed ant colony algorithm in 1991. The main features are: through positive feedback, distributed collaboration to find the best path. This is a heuristic search algorithm based on population optimization. It makes full use of the ant colony can pass through the simple information transmission between the individual, searching the collective optimization characteristic of the shortest path between the nest and the food, and the similarity between the process and the traveling quotient problem solution. The optimal solution of a traveling quotient problem with NP difficulty is obtained. At the same time, the algorithm is also used to solve the problem of job-shop scheduling, two assignment and multidimensional knapsack, and shows its superiority in solving combinatorial optimization problems.

Over the years, researchers from all over the world have studied and applied the ant colony algorithm carefully, which has been used in many fields such as data analysis, robot collaboration problem solving, power, communication, Water conservancy, mining, chemical engineering, construction, transportation and so on.

Ant colony algorithm can arouse the attention of researchers in relevant fields, because this model can combine the rapidity of problem solving, the global optimization feature and the rationality of the answer in finite time. Among them, the rapidity of the optimization is guaranteed by the information transmission and accumulation of positive feedback type. The premature convergence of the algorithm can be avoided by its distributed computing features, and the ant colony system with greedy heuristic search feature can find the acceptable solution in the early searching process.

This superior problem distributed solution model has been greatly improved and expanded on the basis of the original algorithm model after the attention and effort of the researchers in related fields. After a certain period of time, the ants returning from the food source to reach the D point also encounter obstacles, also need to make a choice. At this point A, b on both sides of the pheromone concentrations are the same, they still half to the left, half to the right. But when a side of the ant has completely bypassed the obstacle to the C point, B side of the ant because the path to go longer, can not reach C point. As shown in Figure 1.

Figure 1: The situation of an ant colony over a period of time before a barrier

At this time for ants from the ant colony to C Point, because of a side of the high concentration of pheromone, B side of the low pheromone, is inclined to choose a side of the path. The result is an increasing number of ants on a side, and eventually all ants choose this shorter path. As shown in Figure 2.

Fig. 2 The path of the Ant colony's final selection

The process is clearly caused by the "positive feedback" of the pheromone left by the ant. It is the exchange of information that is used by an individual ant to search for food. The basic idea of ant colony algorithm is also transformed from this process.

The characteristics of ant colony algorithm

1 ant colony algorithm is a self-organizing algorithm. In system theory, self-organization and its organization are the two basic classifications of organization, the difference is that the organization force or the organization instruction comes from the inside of the system or from the outside of the system, from the inside of the system is self-organization, from outside the system is his organization. If the system is in the process of acquiring space, time, or functional structure without any specific interference from outside, we will say that the system is self-organizing. In the abstract sense, self-organization is the process of increasing the system entropy in the absence of external action (i.e., the process of system from disorder to order). Ant colony algorithm fully breaks this process, with ant colony optimization as an example. When the initial algorithm begins, a single artificial ant to find the solution, the algorithm after a period of evolution, artificial ants through the role of information hormones, spontaneous more and more tend to find some solutions to the optimal solution, this is a disorderly to orderly process.

2 ant colony algorithm is an essentially parallel algorithm. Each ant search process is independent of each other and communicates only through information hormones. So ant colony algorithm can be regarded as a distributed multi-agent system, which begins to search independently in the problem space, which not only increases the reliability of the algorithm, but also makes the algorithm have strong global search ability.

3 The ant colony algorithm is a positive feedback algorithm. From the real ants in the foraging process, we can see that the ant to finally find the shortest path, directly dependent on the shortest path of information hormone accumulation, and the accumulation of information hormones is a positive feedback process. For ant colony algorithm, the initial time in the environment there is exactly the same information hormones, give the system a small disturbance, so that each side of the trajectory concentration is not the same, the ant structure of the solution is good or bad, the algorithm used feedback method is in the better solution through the path left more information hormones, And more information hormones attract more ants, this positive feedback process makes the initial difference is constantly expanding, but also guide the whole system to the direction of the optimal solution evolution. Therefore, positive feedback is an important feature of Ant algorithm, which makes the evolutionary process of the algorithm possible.

4 ant colony algorithm has strong robustness. Compared with other algorithms, Ant colony algorithm is not very good to the initial route, that is, the result of ant colony algorithm is not dependent on the choice of sub initial route, and no manual adjustment is needed in the search process. Secondly, the ant colony algorithm has fewer parameters and is easy to be set, so it is easy to apply ant colony algorithm to solve other combinatorial optimization problems. (Original source Le you think Ningbo website promotion: http://www.nbseo.cc/archives/1843)