wine数据集分类结果
Grid Search
best parameters: {'algorithm': 'SAMME', 'learning_rate': 0.3593813663804626, 'n_estimators': 60}
best score: 0.9720634920634922
Time Elapse: 113.00295925140381
Grid Search Based GA
Best individual is: {'n_estimators': 100, 'learning_rate': 0.3593813663804626, 'algorithm': 'SAMME'}
with fitness: 0.9719101123595506
Time Elapsed = 46.56487488746643
GA
结果：
-
POPULATION_SIZE = 10 P_CROSSOVER = 0.9 # probability for crossover P_MUTATION = 0.5 # probability for mutating an individual MAX_GENERATIONS = 10 HALL_OF_FAME_SIZE = 5 CROWDING_FACTOR = 20.0 # crowding factor for crossover and mutationBest solution is:
params = (67, 0.34684801135281196, 'SAMME')
Accuracy = 0.97206
-
POPULATION_SIZE = 10
P_CROSSOVER = 0.9 # probability for crossover
P_MUTATION = 0.5 # probability for mutating an individual
MAX_GENERATIONS = 10
HALL_OF_FAME_SIZE = 5
CROWDING_FACTOR = 20.0 # crowding factor for crossover and mutationBest solution is:
params = (67, 0.34684801135281196, 'SAMME')
Accuracy = 0.97206
PSO
算法大致步骤：
- 初始化
- 更新
可参考的blog：(87条消息) 智能优化算法——粒子群算法原理与仿真程序_子木呀的博客-CSDN博客_粒子群优化算法原理
代码实现设置的条件：
MAX_Generation = 10 #迭代次数
Population = 10 #种群数量
dimension = 3
v_low = -1
v_high = 1
pso = PSO(dimension, MAX_Generation, Population,
BOUNDS_LOW, BOUNDS_HIGH, v_low, v_high) #实现的class
更新步骤的核心代码:
网上搜一下，其中w自身权重系数（记不清了），c1是个体学习系数，c2是群落学习系数
c1 = 2.0 # 学习因子
c2 = 2.0
w = 0.8
# 更新速度(核心公式)
self.v[i] = w * self.v[i] + c1 * random.uniform(0, 1) * (
self.p_best[i] - self.x[i]) + c2 * random.uniform(0, 1) * (self.g_best - self.x[i])
# 速度限制
for j in range(self.dimension):
if self.v[i][j] < self.v_low:
self.v[i][j] = self.v_low
if self.v[i][j] > self.v_high:
self.v[i][j] = self.v_high
# 更新位置
self.x[i] = self.x[i] + self.v[i]
# 位置限制
for j in range(self.dimension):
if self.x[i][j] < self.bound[0][j]:
self.x[i][j] = self.bound[0][j]
if self.x[i][j] > self.bound[1][j]:
self.x[i][j] = self.bound[1][j]
大致的意思就是通过公式更新速度和位置，同时对更新后的速度与位置进行修正，因为更新的位置一定是在一定的范围之内的。
结果：
-
MAX_Generation = 10 #迭代次数
Population = 10 #种群数量v_low = -1
v_high = 1
c1 = 2.0 # 学习因子
c2 = 2.0
w = 0.8
n_estimators'=97.59544738, 'learning_rate'=0.93788895, 'algorithm'=SAMME
0.9830158730158731
当前的最佳适应度：0.9830158730158731
time cost: 175.3606903553009
-
################ 改变迭代次数和种群大小#######################
MAX_Generation = 50 #迭代次数
Population = 20 #种群数量v_low = -1
v_high = 1
c1 = 2.0 # 学习因子
c2 = 2.0
w = 0.8当前最佳位置：[22.63664273 0.7580015 0.39425009]
0.9831746031746033
当前的最佳适应度：0.9831746031746033
time cost: 1097.9579124450684 s
-
################ 改变速度大小#######################
MAX_Generation = 50 #迭代次数
Population = 20 #种群数量v_low = -0.5
v_high = 0.5
c1 = 2.0 # 学习因子
c2 = 2.0
w = 0.8当前最佳位置：[7.49302006e+01 6.20588351e-01 2.87210702e-02]
0.9831746031746033
当前的最佳适应度：0.9831746031746033
time cost: 1510.600219488144 s
-
################ 改变速度范围#######################
MAX_Generation = 50 #迭代次数
Population = 20 #种群数量v_low = [-10, -0.1, -0.5]
v_high = [10, 0.1, 0.5]
c1 = 2.0 # 学习因子
c2 = 2.0
w = 0.8当前最佳位置：[17.96950042 0.88984003 0. ]
0.9885714285714287
当前的最佳适应度：0.9885714285714287
time cost: 1343.2985899448395 s
-
改变初始化
当前最佳位置：[58.67594087 0.40936569 0. ]
0.9776190476190475
当前的最佳适应度：0.9776190476190475
time cost: 2636.623948097229 s