蛇优算法 python
时间: 2023-07-10 20:11:46 浏览: 43
蛇优算法(Snake Optimization Algorithm,简称SOA)是一种基于蛇的行为模拟的启发式优化算法,适用于解决优化问题。下面是一份Python实现:
```python
import numpy as np
class Snake:
def __init__(self, dim, bounds):
self.position = np.random.uniform(bounds[0], bounds[1], size=dim)
self.velocity = np.zeros_like(self.position)
self.fitness = np.inf
def update(self, best_position, w, c1, c2):
r1 = np.random.uniform(size=self.position.shape)
r2 = np.random.uniform(size=self.position.shape)
self.velocity = w * self.velocity \
+ c1 * r1 * (best_position - self.position) \
+ c2 * r2 * (self.leader_position - self.position)
self.position += self.velocity
def evaluate(self, fitness_func):
self.fitness = fitness_func(self.position)
class SOA:
def __init__(self, dim, bounds, num_snakes, max_iter, fitness_func):
self.dim = dim
self.bounds = bounds
self.num_snakes = num_snakes
self.max_iter = max_iter
self.fitness_func = fitness_func
self.swarm = [Snake(dim, bounds) for _ in range(num_snakes)]
self.best_position = np.inf * np.ones_like(bounds[0])
self.best_fitness = np.inf
def optimize(self, w=0.5, c1=1, c2=1):
for i in range(self.max_iter):
for snake in self.swarm:
snake.evaluate(self.fitness_func)
if snake.fitness < self.best_fitness:
self.best_position = snake.position
self.best_fitness = snake.fitness
for snake in self.swarm:
if snake.fitness <= np.median([x.fitness for x in self.swarm]):
snake.leader_position = self.best_position
else:
neighbors = [x for x in self.swarm if np.linalg.norm(x.position - snake.position) < 1]
if neighbors:
snake.leader_position = max(neighbors, key=lambda x: x.fitness).position
else:
snake.leader_position = self.best_position
snake.update(self.best_position, w, c1, c2)
return self.best_position, self.best_fitness
```
使用示例:
```python
# 定义要优化的函数
def sphere(x):
return np.sum(x**2)
# 实例化SOA类并运行SOA算法
dim = 10
bounds = (-5.12, 5.12)
num_snakes = 50
max_iter = 100
soa = SOA(dim, bounds, num_snakes, max_iter, sphere)
best_position, best_fitness = soa.optimize()
print('Optimal solution:', best_position)
print('Optimal value:', best_fitness)
```