python实现A*算法的示例代码-创新互联
A*作为最常用的路径搜索算法,值得我们去深刻的研究。路径规划项目。先看一下维基百科给的算法解释:https://en.wikipedia.org/wiki/A*_search_algorithm
创新互联是一家专业从事网站制作、做网站、网页设计的品牌网络公司。如今是成都地区具影响力的网站设计公司,作为专业的成都网站建设公司,创新互联依托强大的技术实力、以及多年的网站运营经验,为您提供专业的成都网站建设、营销型网站建设及网站设计开发服务!A *是最佳优先搜索它通过在解决方案的所有可能路径(目标)中搜索导致成本最小(行进距离最短,时间最短等)的问题来解决问题。 ),并且在这些路径中,它首先考虑那些似乎最快速地引导到解决方案的路径。它是根据加权图制定的:从图的特定节点开始,它构造从该节点开始的路径树,一次一步地扩展路径,直到其一个路径在预定目标节点处结束。
在其主循环的每次迭代中,A *需要确定将其部分路径中的哪些扩展为一个或多个更长的路径。它是基于成本(总重量)的估计仍然到达目标节点。具体而言,A *选择最小化的路径
F(N)= G(N)+ H(n)
其中n是路径上的最后一个节点,g(n)是从起始节点到n的路径的开销,h(n)是一个启发式,用于估计从n到目标的最便宜路径的开销。启发式是特定于问题的。为了找到实际最短路径的算法,启发函数必须是可接受的,这意味着它永远不会高估实际成本到达最近的目标节点。
维基百科给出的伪代码:
function A*(start, goal) // The set of nodes already evaluated closedSet := {} // The set of currently discovered nodes that are not evaluated yet. // Initially, only the start node is known. openSet := {start} // For each node, which node it can most efficiently be reached from. // If a node can be reached from many nodes, cameFrom will eventually contain the // most efficient previous step. cameFrom := an empty map // For each node, the cost of getting from the start node to that node. gScore := map with default value of Infinity // The cost of going from start to start is zero. gScore[start] := 0 // For each node, the total cost of getting from the start node to the goal // by passing by that node. That value is partly known, partly heuristic. fScore := map with default value of Infinity // For the first node, that value is completely heuristic. fScore[start] := heuristic_cost_estimate(start, goal) while openSet is not empty current := the node in openSet having the lowest fScore[] value if current = goal return reconstruct_path(cameFrom, current) openSet.Remove(current) closedSet.Add(current) for each neighbor of current if neighbor in closedSet continue // Ignore the neighbor which is already evaluated. if neighbor not in openSet // Discover a new node openSet.Add(neighbor) // The distance from start to a neighbor //the "dist_between" function may vary as per the solution requirements. tentative_gScore := gScore[current] + dist_between(current, neighbor) if tentative_gScore >= gScore[neighbor] continue // This is not a better path. // This path is the best until now. Record it! cameFrom[neighbor] := current gScore[neighbor] := tentative_gScore fScore[neighbor] := gScore[neighbor] + heuristic_cost_estimate(neighbor, goal) return failure function reconstruct_path(cameFrom, current) total_path := {current} while current in cameFrom.Keys: current := cameFrom[current] total_path.append(current) return total_path
标题名称:python实现A*算法的示例代码-创新互联
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