带有混合时间窗多配送中心多目标车辆路径优化粒子群算法Python代码
时间: 2023-10-10 21:15:04 浏览: 66
当然,我可以为您提供一个简单的示例代码来实现带有混合时间窗多配送中心多目标车辆路径优化的粒子群算法(Particle Swarm Optimization, PSO)。以下是一个用Python编写的示例代码:
```python
import numpy as np
class Particle:
def __init__(self, num_vehicles, num_customers, num_depots):
self.num_vehicles = num_vehicles
self.num_customers = num_customers
self.num_depots = num_depots
self.position = np.random.randint(low=0, high=num_depots, size=(num_vehicles, num_customers+1))
self.velocity = np.zeros((num_vehicles, num_customers+1))
self.best_position = self.position.copy()
self.best_fitness = float('inf')
class PSO:
def __init__(self, num_particles, num_vehicles, num_customers, num_depots):
self.num_particles = num_particles
self.num_vehicles = num_vehicles
self.num_customers = num_customers
self.num_depots = num_depots
self.particles = []
self.global_best_fitness = float('inf')
self.global_best_position = None
def initialize_particles(self):
for _ in range(self.num_particles):
particle = Particle(self.num_vehicles, self.num_customers, self.num_depots)
self.particles.append(particle)
def update_velocity(self, particle, inertia_weight, cognitive_weight, social_weight):
for i in range(self.num_vehicles):
for j in range(self.num_customers+1):
r1 = np.random.random()
r2 = np.random.random()
cognitive_component = cognitive_weight * r1 * (particle.best_position[i, j] - particle.position[i, j])
social_component = social_weight * r2 * (self.global_best_position[i, j] - particle.position[i, j])
particle.velocity[i, j] = inertia_weight * particle.velocity[i, j] + cognitive_component + social_component
def update_position(self, particle):
particle.position += particle.velocity
def evaluate_fitness(self, particle):
# TODO: Implement fitness evaluation based on your specific problem
def update_best_position(self, particle):
fitness = self.evaluate_fitness(particle)
if fitness < particle.best_fitness:
particle.best_fitness = fitness
particle.best_position = particle.position.copy()
def update_global_best(self):
for particle in self.particles:
if particle.best_fitness < self.global_best_fitness:
self.global_best_fitness = particle.best_fitness
self.global_best_position = particle.best_position.copy()
def run(self, num_iterations, inertia_weight, cognitive_weight, social_weight):
self.initialize_particles()
for _ in range(num_iterations):
for particle in self.particles:
self.update_velocity(particle, inertia_weight, cognitive_weight, social_weight)
self.update_position(particle)
self.update_best_position(particle)
self.update_global_best()
if __name__ == '__main__':
num_particles = 50
num_vehicles = 3
num_customers = 10
num_depots = 2
num_iterations = 100
inertia_weight = 0.9
cognitive_weight = 2.0
social_weight = 2.0
pso = PSO(num_particles, num_vehicles, num_customers, num_depots)
pso.run(num_iterations, inertia_weight, cognitive_weight, social_weight)
```
请注意,以上代码只是一个示例,需要根据您的具体问题进行适当的修改。您需要实现适合您问题的适应度评估函数,并在 `evaluate_fitness` 方法中进行调用。此外,您还需要根据您的问题定义更多的约束和限制条件。
希望这个示例代码能帮助到您!如果您有其他问题,请随时提问。