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

本文标题:python实现A*算法的示例代码-创新互联
文章分享:http://scjbc.cn/article/ehgjs.html

其他资讯