用netlogo写一个蚁群算法模型

好的，以下是一个简单的蚁群算法模型的 NetLogo 代码：

globals [
  pheromone-map ; 信息素地图
]

turtles-own [
  path ; 蚂蚁的路径
  path-length ; 蚂蚁的路径长度
]

to setup
  clear-all
  reset-ticks
  ; 初始化信息素地图
  set pheromone-map matrix:make-constant 50 50 0
  ; 创建蚂蚁
  create-turtles 10 [
    setxy random-xcor random-ycor
    set color red
  ]
end

to go
  ask turtles [
    ; 选择下一个节点
    let next-node select-next-node
    ; 更新路径和路径长度
    set path lput next-node path
    set path-length path-length + distance next-node
    ; 在当前节点释放信息素
    let current-node first path
    set pheromone-map matrix:set pheromone-map current-node pheromone-map current-node + (1 / path-length)
    ; 如果到达了终点，则返回起点并更新信息素
    if next-node = min-one-of nodes [distance current-node ?] [
      set path lput min-one-of nodes [distance current-node ?] path
      set path-length path-length + distance current-node min-one-of nodes [distance current-node ?]
      set color black
      set path []
      set path-length 0
      update-pheromone-map
    ]
    ; 移动到下一个节点
    move-to next-node
  ]
  tick
end

to select-next-node
  let current-node patch-here
  let next-node nobody
  let max-pheromone 0
  ; 选择下一个节点
  ask neighbors [
    if not member? self path [
      let p pheromone-map current-node self
      if p > max-pheromone [
        set max-pheromone p
        set next-node self
      ]
    ]
  ]
  ; 如果没有可行的节点，则返回起点
  if next-node = nobody [
    set next-node min-one-of nodes [distance current-node ?]
  ]
  ; 返回下一个节点
  report next-node
end

to update-pheromone-map
  ask patches [
    ; 信息素挥发
    set pheromone-map self pheromone-map self * 0.9
    ; 信息素加强
    ask turtles [
      if member? self path and first path = self and self = patch-here [
        set pheromone-map self pheromone-map self + (1 / path-length)
      ]
    ]
  ]
end

这个模型中，每只蚂蚁都会选择下一个节点，并在当前节点释放信息素。如果蚂蚁到达终点，则返回起点并更新信息素。每一轮迭代结束后，所有信息素都会挥发一定的量，同时在路径上的蚂蚁会加强路径上的信息素。通过不断迭代，蚂蚁会逐渐找到最优路径。