WolfOptimization python代码
时间: 2023-11-12 11:05:53 浏览: 27
以下是使用Python实现的Wolf Optimization算法的简单示例代码:
```python
import random
import math
# 目标函数
def objective_function(x):
return sum([i**2 for i in x])
# 初始化种群
def initialize_population(n, lower_bound, upper_bound, dimensions):
population = []
for i in range(n):
wolf = []
for j in range(dimensions):
wolf.append(random.uniform(lower_bound, upper_bound))
population.append(wolf)
return population
# 计算目标函数值
def evaluate_population(population):
fitness = []
for wolf in population:
fitness.append(objective_function(wolf))
return fitness
# 选择最优个体
def get_best_individual(population, fitness):
best_fitness = min(fitness)
index = fitness.index(best_fitness)
return population[index], best_fitness
# 更新猎狼位置
def update_wolf_position(population, a, r1, r2, r3, alpha, beta, gamma, dimensions, lower_bound, upper_bound):
for i, wolf in enumerate(population):
for j in range(dimensions):
A = a[j]
D_alpha = abs(gamma * A - wolf[j])
X1 = alpha[j] - A * D_alpha
C = 2 * r1[j] - 1
D_beta = abs(beta[j] * C - wolf[j])
X2 = beta[j] - A * D_beta
D_delta = abs(gamma * C - wolf[j])
X3 = gamma * C - A * D_delta
wolf[j] = (X1 + X2 + X3) / 3.0
if wolf[j] < lower_bound:
wolf[j] = lower_bound
if wolf[j] > upper_bound:
wolf[j] = upper_bound
population[i] = wolf
return population
# Wolf Optimization算法主函数
def wolf_optimization(n, dimensions, lower_bound, upper_bound, iterations):
population = initialize_population(n, lower_bound, upper_bound, dimensions)
fitness = evaluate_population(population)
best_individual, best_fitness = get_best_individual(population, fitness)
for i in range(iterations):
a = [2.0 - 2.0 * (i / iterations) for i in range(dimensions)]
r1 = [random.uniform(0, 1) for i in range(dimensions)]
r2 = [random.uniform(0, 1) for i in range(dimensions)]
r3 = [random.uniform(0, 1) for i in range(dimensions)]
alpha = population[random.randint(0, n-1)]
beta = population[random.randint(0, n-1)]
gamma = population[random.randint(0, n-1)]
population = update_wolf_position(population, a, r1, r2, r3, alpha, beta, gamma, dimensions, lower_bound, upper_bound)
fitness = evaluate_population(population)
individual, fitness_value = get_best_individual(population, fitness)
if fitness_value < best_fitness:
best_individual, best_fitness = individual, fitness_value
return best_individual, best_fitness
```
使用该代码可以进行Wolf Optimization算法的优化。