Mountain Climbing algorithm

Algorithm Description: Mountain climbing algorithm is a local merit method, using heuristic method, is an improvement of depth first search, it uses feedback information to help generate solution decision. belongs to an artificial intelligence algorithm. From the current node, compare the values of the surrounding neighbor nodes. If the current node is the largest, then the current node is returned as the maximum value (both peak peaks), and instead the current node is replaced with the highest neighbor node, thus achieving the goal of climbing up to the top of the mountain. This loops until the highest point is reached.

Advantages and disadvantages of the algorithm:

AdvantagesAvoid traversal and select some nodes by inspiration, so as to achieve the purpose of improving efficiency. Disadvantagesbecause it is not a full search, the results may not be optimal. The following problems are commonly found in mountain climbing algorithms:1), local maximum: A node is higher than any neighbor, but it is not the highest point of the whole problem. 2), Highland: Also known as flat-topped, once the search reaches the Highlands, it is impossible to determine the best direction of the search, will generate random movement, resulting in reduced search efficiency. 3), Ridge: The search may be on both sides of the ridge and back concussion, the pace is very small.

 

Mountain Climbing algorithm