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In the case of the path that a knight must travel on a chessboard to get through the 64 squares, depth search is used to find a solution that meets the requirements.
The orange arrows show the path of the DFS algorithm over the eight nodes in the figure below.
The implementation of the depth search is done in C++, where the first to last nodes of the graph are "iterators". Recurrence is used to traverse the nodes in depth because of the two variables "nodeVisited" and "nodeProcessed".
2 functions called "processNode" and "ProcessSide" are used to observe the path of the nodes in the image.
The first two digits represent the number of nodes and sides, followed by 8 pairs of digits representing the connections of the nodes. There is a path from the first to the last part of the DFS algorithm.
There are several applications of the depth search.
A directed acyclic graph is a set of nodes where each has a single address that is not with itself. It can be applied to organize activities that have some dependency on each other in order to efficiently organize the execution of a list of activities.
The bridges in graphs are connected in a way that only one of them can reach each end. If we remove one of the nodes, we won't be able to access the other one. This can be used to prioritize.
If you want to solve a puzzle or maze, you can use the solution steps to represent the steps.
Each step of the puzzle solution is dependent on the previous one.
It is important to know how connected certain activities are in order to decrease dependency. This can be solved with the help of the DFS algorithm because the system will be more understandable if the activities are grouped in a better way.
A breadth-ready search is a search that traverses the entire graph, starting at the root and going all the way to the neighbors. Next, for each of the neighbors, their adjacent neighbors are explored, and so on until the entire graph is traversed. The search ends if the node is found before all the other ones.
The width search is used to find the best path at each moment of the traversal.
The orange arrows show the path of the BFS algorithm in the figure below.
The implementation of the breadth-first search algorithm in C is shown below, where the first entered and last entered nodes are used to traverse the graph. Two variables are used to organize the path.
The queue data structure is used to visit the nodes in order of arrival. That is the first thing that happens when a queue starts to form.
The following image shows two functions called ProcessNode and ProcessSide that are used to observe the path of the nodes.
The first two digits represent the number of nodes and sides, followed by 8 pairs of digits representing the connections of the nodes, in the image above. The path of the BFS is shown in the final part.
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why bfs gives shortest path?
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How to dpf regeneration?
