狼群算法PYTHON

### 狼群算法 Python 实现教程

#### 初始化参数设置
为了实现狼群算法，首先需要定义一些基本参数。这些参数包括种群大小、迭代次数以及搜索空间范围。

import numpy as np

def initialize_parameters(num_wolves, dim, lb, ub):
    wolves = np.random.uniform(lb, ub, (num_wolves, dim))
    alpha_pos = np.zeros(dim)
    beta_pos = np.zeros(dim)
    delta_pos = np.zeros(dim)

    return wolves, alpha_pos, beta_pos, delta_pos

此部分初始化了狼的位置，并设定了三个最佳个体位置变量 `alpha_pos`, `beta_pos` 和 `delta_pos` 来记录当前找到的最佳解[^1]。

#### 定义适应度函数
适应度函数决定了每只狼的好坏程度，在实际应用中这取决于具体问题的目标函数。

def fitness_function(positions):
    # 这里以Rosenbrock function为例作为目标函数
    positions = np.asarray(positions)
    sum_ = 0
    for i in range(len(positions)-1):
        sum_ += 100 * (positions[i+1] - positions[i]**2)**2 + (1 - positions[i])**2
    
    return sum_

上述代码实现了 Rosenbrock 函数作为示例中的适应度计算方法[^3]。

#### 更新狼的位置
根据狼群的行为模式更新狼的位置，这里主要考虑游走行为、召唤行为和围攻行为。

def update_positions(wolves, alpha_pos, beta_pos, delta_pos, a):
    updated_wolves = []
    
    for wolf in wolves:
        r1 = np.random.rand(len(alpha_pos))
        r2 = np.random.rand(len(alpha_pos))

        A1 = 2*a*r1 - a
        C1 = 2*r2
        
        D_alpha = abs(C1*alpha_pos - wolf)
        X1 = alpha_pos - A1*D_alpha

        r1 = np.random.rand(len(beta_pos))
        r2 = np.random.rand(len(beta_pos))

        A2 = 2*a*r1 - a
        C2 = 2*r2
        
        D_beta = abs(C2*beta_pos - wolf)
        X2 = beta_pos - A2*D_beta 

        r1 = np.random.rand(len(delta_pos))
        r2 = np.random.rand(len(delta_pos))

        A3 = 2*a*r1 - a
        C3 = 2*r2
        
        D_delta = abs(C3*delta_pos - wolf)
        X3 = delta_pos-A3*D_delta

        new_position = (X1+X2+X3)/3
        updated_wolves.append(new_position)

    return np.array(updated_wolves)

这段代码展示了如何依据三类顶级捕食者的平均移动方向调整其他成员的位置向量[^2]。

#### 主循环逻辑
最后构建主循环来进行多次迭代直到满足终止条件为止。

def main():
    num_iterations = 500
    num_wolves = 30
    dimensionality = 2
    lower_bound = -5.12
    upper_bound = 5.12
    a = 2

    wolves, alpha_pos, beta_pos, delta_pos = initialize_parameters(
        num_wolves=num_wolves,
        dim=dimensionality,
        lb=lower_bound,
        ub=upper_bound
    )

    best_fitness = float('inf')

    for iteration in range(num_iterations):

        for idx, wolf in enumerate(wolves):
            current_fitness = fitness_function(wolf)

            if current_fitness < best_fitness:
                best_fitness = current_fitness
                
                if current_fitness < fitness_function(alpha_pos):
                    delta_pos = beta_pos.copy()
                    beta_pos = alpha_pos.copy()
                    alpha_pos = wolf.copy()

                elif current_fitness < fitness_function(beta_pos):
                    delta_pos = beta_pos.copy()
                    beta_pos = wolf.copy()

                elif current_fitness < fitness_function(delta_pos):
                    delta_pos = wolf.copy()

        a = 2 - iteration * ((2) / num_iterations)
        
        wolves = update_positions(wolves=wolves, 
                                 alpha_pos=alpha_pos, 
                                 beta_pos=beta_pos, 
                                 delta_pos=delta_pos, 
                                 a=a)

        print(f"Iteration {iteration}: Best Fitness={best_fitness}")

if __name__ == "__main__":
    main()

以上即为完整的狼群算法Python实现过程，其中包含了必要的组件如初始化、评估机制及核心演化操作等。