Python编程实现粒子群算法(PSO)详解

class bird:
 """
 speed:速度
 position:位置
 fit:适应度
 lbestposition:经历的最佳位置
 lbestfit:经历的最佳的适应度值
 """
 def __init__(self, speed, position, fit, lBestPosition, lBestFit):
  self.speed = speed
  self.position = position
  self.fit = fit
  self.lBestFit = lBestPosition
  self.lBestPosition = lPestFit

import random

class PSO:
 """
 fitFunc:适应度函数
 birdNum:种群规模
 w:惯性权重
 c1,c2:个体学习因子，社会学习因子
 solutionSpace:解空间，列表类型：[最小值，最大值]
 """
 def __init__(self, fitFunc, birdNum, w, c1, c2, solutionSpace):
  self.fitFunc = fitFunc
  self.w = w
  self.c1 = c1
  self.c2 = c2
  self.birds, self.best = self.initbirds(birdNum, solutionSpace)

 def initbirds(self, size, solutionSpace):
  birds = []
  for i in range(size):
   position = random.uniform(solutionSpace[0], solutionSpace[1])
   speed = 0
   fit = self.fitFunc(position)
   birds.append(bird(speed, position, fit, position, fit))
  best = birds[0]
  for bird in birds:
   if bird.fit > best.fit:
    best = bird
  return birds,best

 def updateBirds(self):
  for bird in self.birds:
   # 更新速度
   bird.speed = self.w * bird.speed + self.c1 * random.random() * (bird.lBestPosition - bird.position) + self.c2 * random.random() * (self.best.position - bird.position)
   # 更新位置
   bird.position = bird.position + bird.speed
   # 跟新适应度
   bird.fit = self.fitFunc(bird.position)
   # 查看是否需要更新经验最优
   if bird.fit > bird.lBestFit:
    bird.lBestFit = bird.fit
    bird.lBestPosition = bird.position

 def solve(self, maxIter):
  # 只考虑了最大迭代次数，如需考虑阈值，添加判断语句就好
  for i in range(maxIter):
   # 更新粒子
   self.updateBirds()
   for bird in self.birds:
    # 查看是否需要更新全局最优
    if bird.fit > self.best.fit:
     self.best = bird