北方苍鹰算法python

北方苍鹰算法是一种优化算法，它模拟了北方苍鹰捕猎的过程，包括猎物识别和攻击、追逐和逃生。该算法可以用于解决各种优化问题。

以下是一个使用Python实现北方苍鹰算法的示例：

import random
import numpy as np

def initialize_population(population_size, num_variables, variable_ranges):
    population = []
    for _ in range(population_size):
        individual = []
        for i in range(num_variables):
            lower_bound, upper_bound = variable_ranges[i]
            value = random.uniform(lower_bound, upper_bound)
            individual.append(value)
        population.append(individual)
    return population

def fitness_function(individual):
    # 计算适应度函数的值
    # 这里可以根据具体的优化问题进行定义
    x = individual[0]
    y = individual[1]
    fitness = x**2 + y**2
    return fitness

def ngo_algorithm(population_size, num_variables, variable_ranges, num_iterations):
    population = initialize_population(population_size, num_variables, variable_ranges)
    best_fitness = float('inf')
    best_individual = None

    for _ in range(num_iterations):
        for individual in population:
            fitness = fitness_function(individual)
            if fitness < best_fitness:
                best_fitness = fitness
                best_individual = individual

        for individual in population:
            new_individual = []
            for i in range(num_variables):
                x = individual[i]
                best_x = best_individual[i]
                lower_bound, upper_bound = variable_ranges[i]
                new_x = x + random.uniform(-1, 1) * abs(x - best_x)
                new_x = np.clip(new_x, lower_bound, upper_bound)
                new_individual.append(new_x)
            individual[:] = new_individual

    return best_individual

# 设置算法参数
population_size = 50
num_variables = 2
variable_ranges = [(-5, 5), (-5, 5)]
num_iterations = 100

# 运行北方苍鹰算法
best_solution = ngo_algorithm(population_size, num_variables, variable_ranges, num_iterations)
print("Best solution:", best_solution)

这个示例中，我们首先定义了一个`initialize_population`函数来初始化种群。然后，我们定义了一个适应度函数`fitness_function`，用于计算每个个体的适应度值。接下来，我们实现了北方苍鹰算法的主要逻辑，包括选择最优个体和更新个体的过程。最后，我们使用定义的参数运行北方苍鹰算法，并输出最佳解。