单目标优化算法python
时间: 2023-05-27 10:05:09 浏览: 61
单目标优化算法是指优化一个目标函数,使其达到最优值的算法。Python中有多种单目标优化算法可以使用,下面列举其中几种:
1. 爬山算法(Hill Climbing Algorithm):该算法基于贪心思想,每次选择当前状态的邻居状态中最优的一个进行移动,直到达到局部最优解或者无法继续移动。代码实现:
```python
def hill_climbing(problem):
current = problem.initial_state()
while True:
neighbor = problem.random_neighbor(current)
if problem.value(neighbor) > problem.value(current):
current = neighbor
else:
return current
```
2. 模拟退火算法(Simulated Annealing Algorithm):该算法模拟物质在退火过程中的结晶过程,通过控制温度来控制搜索方向。代码实现:
```python
def simulated_annealing(problem):
current = problem.initial_state()
T = 1.0
while T > 0.1:
neighbor = problem.random_neighbor(current)
delta_e = problem.value(neighbor) - problem.value(current)
if delta_e > 0:
current = neighbor
else:
if random.uniform(0, 1) < math.exp(delta_e / T):
current = neighbor
T *= 0.99
return current
```
3. 遗传算法(Genetic Algorithm):该算法通过模拟生物进化过程,对一个种群进行迭代优化,直到达到最优解。代码实现:
```python
def genetic_algorithm(problem, pop_size=50, num_generations=100):
population = [problem.random_state() for _ in range(pop_size)]
for i in range(num_generations):
offspring = []
for j in range(pop_size):
parent1 = random.choice(population)
parent2 = random.choice(population)
child = problem.crossover(parent1, parent2)
if random.uniform(0, 1) < 0.1:
child = problem.mutate(child)
offspring.append(child)
population = offspring
return max(population, key=problem.value)
```
以上是三种常用的单目标优化算法的Python实现。