Happy you think about "Ant colony algorithm" search engine optimization strategy

Intermediary transaction http://www.aliyun.com/zixun/aggregation/6858.html ">seo diagnose Taobao guest cloud host technology Hall

Through positive feedback and distributed collaboration to find the optimal path, Ant colony algorithm is a heuristic search algorithm based on population optimization. Ants are one of the most common and most numerous insect species on earth, often appearing in droves in the human life environment. 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, which is 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.

The following three aspects of the ant colony algorithm to analyze the search engine optimization strategy:

First, the site's directory level to be as little as possible

In-station optimization requirements of the site directory hierarchy as little as possible, not more than three layers, but you have not thought for what? According to the shortest path principle of ant colony algorithm, your directory hierarchy is short and helps your site to be the shortest total path to increase the weight of your site.

Second, as far as possible to find high-quality high correlation of the station do outside the chain

This is also the link strategy that has been advocated, according to the Ant colony algorithm pheromone principle, if a highly relevant station points to you, that is to say, your ant group eventually foraging place is the key phrase you want to do, if a related high quality of the station point to you, you can be said to be standing on a very high pheromone path, Because the path is extremely high in pheromone, it is very likely that the path of the final ant selection.

Third, the website should try to keep the update

Because the ant colony algorithm pheromone increment This parameter, the update equals you to produce the pheromone increment, thus the pheromone increase, naturally also helps the ant finally to be in your path converges. Why is the hot keyword not good? Because in the Group Word field, the ant already converges in a path, the path contains the pheromone is very high, so unless you can stand on the path of the group, it is difficult to get rankings. Why are less competitive words good to do? Because in the Group Word field, the ant has not yet recruited an absolute convergence of the path, even if there is no high pheromone. At random you are most likely to be the path you are in, to become the ultimate convergence of ants, so as to get a good ranking. (Wen/Le you think compiling: Ningbo SEO Research Center/Ningbo Professional website promotion, website Optimization Center http://www.nbseo.cc/archives/1838)