10维的粒子群算法代码模板
时间: 2024-04-15 08:23:28 浏览: 15
粒子群算法(Particle Swarm Optimization,PSO)是一种优化算法,用于解决各种优化问题。下面是一个简单的10维粒子群算法的代码模板:
```python
import random
class Particle:
def __init__(self, dim):
self.position = [random.uniform(-5, 5) for _ in range(dim)]
self.velocity = [random.uniform(-1, 1) for _ in range(dim)]
self.best_position = self.position.copy()
self.best_fitness = float('inf')
class PSO:
def __init__(self, num_particles, dim, max_iter):
self.num_particles = num_particles
self.dim = dim
self.max_iter = max_iter
self.particles = [Particle(dim) for _ in range(num_particles)]
self.global_best_position = None
self.global_best_fitness = float('inf')
def optimize(self):
for _ in range(self.max_iter):
for particle in self.particles:
fitness = self.evaluate(particle.position)
if fitness < particle.best_fitness:
particle.best_position = particle.position.copy()
particle.best_fitness = fitness
if fitness < self.global_best_fitness:
self.global_best_position = particle.position.copy()
self.global_best_fitness = fitness
self.update_velocity(particle)
self.update_position(particle)
def evaluate(self, position):
# 计算适应度函数值,根据具体问题实现
def update_velocity(self, particle):
w = 0.5 # 惯性权重
c1 = 2.0 # 自我认知学习因子
c2 = 2.0 # 社会认知学习因子
for i in range(self.dim):
r1 = random.random()
r2 = random.random()
particle.velocity[i] = (w * particle.velocity[i] +
c1 * r1 * (particle.best_position[i] - particle.position[i]) +
c2 * r2 * (self.global_best_position[i] - particle.position[i]))
def update_position(self, particle):
for i in range(self.dim):
particle.position[i] += particle.velocity[i]
# 使用示例
pso = PSO(num_particles=20, dim=10, max_iter=100)
pso.optimize()
print("Global best position:", pso.global_best_position)
print("Global best fitness:", pso.global_best_fitness)
```
这个代码模板实现了一个简单的10维粒子群算法。你可以根据具体的优化问题,实现适应度函数`evaluate`来计算每个粒子的适应度值。在`update_velocity`函数中,根据惯性权重、自我认知学习因子和社会认知学习因子来更新粒子的速度。在`update_position`函数中,根据速度更新粒子的位置。最后,通过调用`optimize`方法来运行粒子群算法,并输出全局最优解和适应度值。
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