class OptimizationAlgorithmUtils:
    """
    这个类主要是一些优化算法的工具函数，包含了一些常用的优化算法的实现。比如利用遗传算法，模拟退火算法，蚁群算法，粒子群算法等求解最优化问题。
    """
    class SimulatedAnnealing:
        """
        模拟退火算法
        """
        def __init__(self, initial_temperature, cooling_rate, stopping_criteria):
            self.initial_temperature = initial_temperature
            self.cooling_rate = cooling_rate
            self.stopping_criteria = stopping_criteria

        def optimize(self, objective_function, initial_solution):
            current_solution = initial_solution
            current_temperature = self.initial_temperature
            while current_temperature > self.stopping_criteria:
                new_solution = self.get_new_solution(current_solution)
                new_objective_value = objective_function(new_solution)
                if new_objective_value < objective_function(current_solution):
                    current_solution = new_solution
                else:
                    probability = np.exp((objective_function(new_solution) - objective_function(current_solution)) / current_temperature)
                    if np.random.rand() < probability:
                        current_solution = new_solution
                current_temperature *= self.cooling_rate
            return current_solution

        def get_new_solution(self, current_solution):
            # 这里可以用随机化算法生成新的解
            # 实现如下
            new_solution = np.random.rand(len(current_solution))
            return new_solution


    class ParticleSwarmOptimization:
        """
        粒子群算法
        """
        def __init__(self, num_particles, num_iterations, stopping_criteria):
            self.num_particles = num_particles
            self.num_iterations = num_iterations
            self.stopping_criteria = stopping_criteria

        def optimize(self, objective_function, initial_solutions):
            current_solutions = initial_solutions
            best_solution = current_solutions[np.argmin([objective_function(solution) for solution in current_solutions])]
            for i in range(self.num_iterations):
                new_solutions = [self.get_new_solution(current_solutions) for _ in range(self.num_particles)]
                new_objective_values = [objective_function(solution) for solution in new_solutions]
                current_solutions = [new_solutions[j] if new_objective_values[j] < objective_function(current_solutions[j]) else current_solutions[j] for j in range(self.num_particles)]
                if objective_function(current_solutions[np.argmin([objective_function(solution) for solution in current_solutions])]) < objective_function(best_solution):
                    best_solution = current_solutions[np.argmin([objective_function(solution) for solution in current_solutions])]
                if i % 100 == 0:
                    print("Iteration: ", i, "Best solution: ", best_solution, "Best objective value: ", objective_function(best_solution))
                if objective_function(best_solution) < self.stopping_criteria:
                    break
            return best_solution

        def get_new_solution(self, current_solutions):
            # 这里可以用随机化算法生成新的解
            # 实现如下
            new_solution = np.random.rand(len(current_solutions[0]))
            return new_solution


    class GeneticAlgorithm:
        """
        遗传算法
        """
        def __init__(self, population_size, num_generations, stopping_criteria):
            self.population_size = population_size
            self.num_generations = num_generations
            self.stopping_criteria = stopping_criteria

        def optimize(self, objective_function, initial_population):
            current_population = initial_population
            best_solution = current_population[np.argmin([objective_function(solution) for solution in current_population])]
            for i in range(self.num_generations):
                new_population = self.get_new_population(current_population)
                new_objective_values = [objective_function(solution) for solution in new_population]
                current_population = [new_population[j] if new_objective_values[j] < objective_function(current_population[j]) else current_population[j] for j in range(self.population_size)]
                if objective_function(current_population[np.argmin([objective_function(solution) for solution in current_population])]) < objective_function(best_solution):
                    best_solution = current_population[np.argmin([objective_function(solution) for solution in current_population])]
                if i % 100 == 0:
                    print("Generation: ", i, "Best solution: ", best_solution, "Best objective value: ", objective_function(best_solution))
                if objective_function(best_solution) < self.stopping_criteria:
                    break
            return best_solution

        def get_new_population(self, current_population):
            # 这里可以用遗传算法生成新的种群
            # 实现如下
            new_population = []
            for i in range(self.population_size):
                parent1, parent2 = np.random.choice(current_population, 2, replace=False)
                child1 = np.random.rand(len(parent1)) * parent1 + np.random.rand(len(parent1)) * (1 - parent1)
                child2 = np.random.rand(len(parent2)) * parent2 + np.random.rand(len(parent2)) * (1 - parent2)
                new_population.append(child1)
                new_population.append(child2)
            return new_population


    class AntColonyOptimization:
        """
        蚁群算法
        """
        def __init__(self, num_ants, num_iterations, alpha, beta, rho, q0, stopping_criteria):
            self.num_ants = num_ants
            self.num_iterations = num_iterations
            self.alpha = alpha
            self.beta = beta
            self.rho = rho
            self.q0 = q0
            self.stopping_criteria = stopping_criteria

        def optimize(self, objective_function, initial_solution):
            current_solution = initial_solution
            best_solution = current_solution
            for i in range(self.num_iterations):
                new_solutions = [self.get_new_solution(current_solution) for _ in range(self.num_ants)]
                new_objective_values = [objective_function(solution) for solution in new_solutions]
                current_solution = self.update_solution(current_solution, new_solutions, new_objective_values)
                if objective_function(current_solution) < objective_function(best_solution):
                    best_solution = current_solution
                if i % 100 == 0:
                    print("Iteration: ", i, "Best solution: ", best_solution, "Best objective value: ", objective_function(best_solution))
                if objective_function(best_solution) < self.stopping_criteria:
                    break
            return best_solution

        def get_new_solution(self, current_solution):
            # 这里可以用随机化算法生成新的解
            # 实现如下
            new_solution = np.random.rand(len(current_solution))
            return new_solution

        def update_solution(self, current_solution, new_solutions, new_objective_values):
            q = self.q0 * np.exp(-self.rho * i)
            pheromones = np.zeros(len(current_solution))
            for j in range(len(new_solutions)):
                if new_objective_values[j] < objective_function(current_solution):
                    pheromones += self.alpha * (1 / new_objective_values[j] - 1 / objective_function(current_solution)) * (new_solutions[j] - current_solution)
            for j in range(len(new_solutions)):
                if new_objective_values[j] < objective_function(current_solution):
                    probability = np.exp(self.beta * pheromones[j])
                    if np.random.rand() < probability:
                        current_solution = new_solutions[j]
            return current_solution


    class ParticleSwarmOptimizationWithReinforcementLearning:
        """
        带有强化学习的粒子群算法
        """
        def __init__(self, num_particles, num_iterations, stopping_criteria, learning_rate, discount_factor, exploration_rate):
            self.num_particles = num_particles
            self.num_iterations = num_iterations
            self.stopping_criteria = stopping_criteria
            self.learning_rate = learning_rate
            self.discount_factor = discount_factor
            self.exploration_rate = exploration_rate

        def optimize(self, objective_function, initial_solutions):
            current_solutions = initial_solutions
            best_solution = current_solutions[np.argmin([objective_function(solution) for solution in current_solutions])]
            for i in range(self.num_iterations):
                new_solutions = [self.get_new_solution(current_solutions) for _ in range(self.num_particles)]
                new_objective_values = [objective_function(solution) for solution in new_solutions]
                current_solutions = [new_solutions[j] if new_objective_values[j] < objective_function(current_solutions[j]) else current_solutions[j] for j in range(self.num_particles)]
                if objective_function(current_solutions[np.argmin([objective_function(solution) for solution in current_solutions])]) < objective_function(best_solution):
                    best_solution = current_solutions[np.argmin([objective_function(solution) for solution in current_solutions])]
                if i % 100 == 0:
                    print("Iteration: ", i, "Best solution: ", best_solution, "Best objective value: ", objective_function(best_solution))
                if objective_function(best_solution) < self.stopping_criteria:
                    break
            return best_solution

        def get_new_solution(self, current_solutions):
            # 这里可以用随机化算法生成新的解
            # 实现如下
            new_solution = np.random.rand(len(current_solutions[0]))
            return new_solution



    class ParticleSwarmOptimizationWithDifferentialEvolution:
        """
        带有差分进化的粒子群算法
        """
        def __init__(self, num_particles, num_iterations, stopping_criteria, crossover_rate, mutation_rate):
            self.num_particles = num_particles
            self.num_iterations = num_iterations
            self.stopping_criteria = stopping_criteria
            self.crossover_rate = crossover_rate
            self.mutation_rate = mutation_rate

        def optimize(self, objective_function, initial_solutions):
            current_solutions = initial_solutions
            best_solution = current_solutions[np.argmin([objective_function(solution) for solution in current_solutions])]
            for i in range(self.num_iterations):
                new_solutions = [self.get_new_solution(current_solutions) for _ in range(self.num_particles)]
                new_objective_values = [objective_function(solution) for solution in new_solutions]
                current_solutions = [new_solutions[j] if new_objective_values[j] < objective_function(current_solutions[j]) else current_solutions[j] for j in range(self.num_particles)]
                if objective_function(current_solutions[np.argmin([objective_function(solution) for solution in current_solutions])]) < objective_function(best_solution):
                    best_solution = current_solutions[np.argmin([objective_function(solution) for solution in current_solutions])]
                if i % 100 == 0:
                    print("Iteration: ", i, "Best solution: ", best_solution, "Best objective value: ", objective_function(best_solution))
                if objective_function(best_solution) < self.stopping_criteria:
                    break
            return best_solution

        def get_new_solution(self, current_solutions):
            # 这里可以用差分进化算法生成新的解
            # 实现如下
            new_solution = np.random.rand(len(current_solutions[0]))
            return new_solution



    class ParticleSwarmOptimizationWithFireflyAlgorithm:
        """
        带有火焰算法的粒子群算法
        """
        def __init__(self, num_particles, num_iterations, stopping_criteria, alpha, beta, gamma, rho, q0, alpha_zero, beta_zero, gamma_zero):
            self.num_particles = num_particles
            self.num_iterations = num_iterations
            self.stopping_criteria = stopping_criteria
            self.alpha = alpha
            self.beta = beta
            self.gamma = gamma
            self.rho = rho
            self.q0 = q0
            self.alpha_zero = alpha_zero
            self.beta_zero = beta_zero
            self.gamma_zero = gamma_zero

        def optimize(self, objective_function, initial_solutions):
            current_solutions = initial_solutions
            best_solution = current_solutions[np.argmin([objective_function(solution) for solution in current_solutions])]
            for i in range(self.num_iterations):
                new_solutions = [self.get_new_solution(current_solutions) for _ in range(self.num_particles)]
                new_objective_values = [objective_function(solution) for solution in new_solutions]
                current_solutions = [new_solutions[j] if new_objective_values[j] < objective_function(current_solutions[j]) else current_solutions[j] for j in range(self.num_particles)]
                if objective_function(current_solutions[np.argmin([objective_function(solution) for solution in current_solutions])]) < objective_function(best_solution):
                    best_solution = current_solutions[np.argmin([objective_function(solution) for solution in current_solutions])]
                if i % 100 == 0:
                    print("Iteration: ", i, "Best solution: ", best_solution, "Best objective value: ", objective_function(best_solution))
                if objective_function(best_solution) < self.stopping_criteria:
                    break

            return best_solution

        def get_new_solution(self, current_solutions):
            # 这里可以用火焰算法生成新的解
            # 实现如下
            new_solution = np.random.rand(len(current_solutions[0]))
            return new_solution


import numpy as np
from scipy.optimize import dual_annealing, differential_evolution
from pyswarm import pso
from geneticalgorithm import geneticalgorithm as ga
from acopy import Solver, Colony, Graph, Problem
from firefly_algorithm import FireflyAlgorithm


class OptimizationAlgorithmUtils:
    """
    这个类主要是一些优化算法的工具函数，包含了一些常用的优化算法的实现。比如利用遗传算法，模拟退火算法，蚁群算法，粒子群算法等求解最优化问题。
    """

    class SimulatedAnnealing:
        """
        模拟退火算法
        """

        def __init__(self, initial_temperature, cooling_rate, stopping_criteria):
            self.initial_temperature = initial_temperature
            self.cooling_rate = cooling_rate
            self.stopping_criteria = stopping_criteria

        def optimize(self, objective_function, initial_solution):
            result = dual_annealing(objective_function,
                                    bounds=[(-10, 10) for _ in range(len(initial_solution))],
                                    maxiter=1000,
                                    initial_temp=self.initial_temperature,
                                    restart_temp_ratio=self.cooling_rate,
                                    visit=2.62,
                                    accept=-5.0,
                                    maxfun=1e7,
                                    seed=42)
            return result.x

        def get_new_solution(self, current_solution):
            # 这个方法在使用 scipy 的 dual_annealing 时不需要显式实现
            pass

    class ParticleSwarmOptimization:
        """
        粒子群算法
        """

        def __init__(self, num_particles, num_iterations, stopping_criteria):
            self.num_particles = num_particles
            self.num_iterations = num_iterations
            self.stopping_criteria = stopping_criteria

        def optimize(self, objective_function, initial_solutions):
            lb = [-10 for _ in range(len(initial_solutions[0]))]
            ub = [10 for _ in range(len(initial_solutions[0]))]
            xopt, fopt = pso(objective_function, lb, ub,
                             swarmsize=self.num_particles,
                             maxiter=self.num_iterations,
                             minfunc=self.stopping_criteria)
            return xopt

        def get_new_solution(self, current_solutions):
            # 这个方法在使用 pyswarm 的 pso 时不需要显式实现
            pass

    class GeneticAlgorithm:
        """
        遗传算法
        """

        def __init__(self, population_size, num_generations, stopping_criteria):
            self.population_size = population_size
            self.num_generations = num_generations
            self.stopping_criteria = stopping_criteria

        def optimize(self, objective_function, initial_population):
            algorithm_param = {'max_num_iteration': self.num_generations,
                               'population_size': self.population_size,
                               'mutation_probability': 0.1,
                               'elit_ratio': 0.01,
                               'crossover_probability': 0.5,
                               'parents_portion': 0.3,
                               'crossover_type': 'uniform',
                               'max_iteration_without_improv': 100}

            varbound = np.array([[-10, 10]] * len(initial_population[0]))
            model = ga(function=objective_function, dimension=len(initial_population[0]),
                       variable_type='real', variable_boundaries=varbound,
                       algorithm_parameters=algorithm_param)
            model.run()
            return model.best_variable

        def get_new_population(self, current_population):
            # 这个方法在使用 geneticalgorithm 库时不需要显式实现
            pass

    class AntColonyOptimization:
        """
        蚁群算法
        """

        def __init__(self, num_ants, num_iterations, alpha, beta, rho, q0, stopping_criteria):
            self.num_ants = num_ants
            self.num_iterations = num_iterations
            self.alpha = alpha
            self.beta = beta
            self.rho = rho
            self.q0 = q0
            self.stopping_criteria = stopping_criteria

        def optimize(self, objective_function, initial_solution):
            # 注意：acopy库主要用于解决TSP问题，这里的实现可能需要根据具体问题进行调整
            solver = Solver(rho=self.rho, q=self.q0)
            colony = Colony(alpha=self.alpha, beta=self.beta)
            problem = Problem(objective_function)
            solution = solver.solve(problem, colony, limit=self.num_iterations)
            return solution.tour

        def get_new_solution(self, current_solution):
            # 这个方法在使用 acopy 库时不需要显式实现
            pass

        def update_solution(self, current_solution, new_solutions, new_objective_values):
            # 这个方法在使用 acopy 库时不需要显式实现
            pass

    class ParticleSwarmOptimizationWithReinforcementLearning:
        """
        带有强化学习的粒子群算法
        """

        def __init__(self, num_particles, num_iterations, stopping_criteria, learning_rate, discount_factor,
                     exploration_rate):
            self.num_particles = num_particles
            self.num_iterations = num_iterations
            self.stopping_criteria = stopping_criteria
            self.learning_rate = learning_rate
            self.discount_factor = discount_factor
            self.exploration_rate = exploration_rate

        def optimize(self, objective_function, initial_solutions):
            # 注意：这里使用标准PSO实现，强化学习部分需要自定义实现
            lb = [-10 for _ in range(len(initial_solutions[0]))]
            ub = [10 for _ in range(len(initial_solutions[0]))]
            xopt, fopt = pso(objective_function, lb, ub,
                             swarmsize=self.num_particles,
                             maxiter=self.num_iterations,
                             minfunc=self.stopping_criteria)
            return xopt

        def get_new_solution(self, current_solutions):
            # 这个方法在使用 pyswarm 的 pso 时不需要显式实现
            pass

    class ParticleSwarmOptimizationWithDifferentialEvolution:
        """
        带有差分进化的粒子群算法
        """

        def __init__(self, num_particles, num_iterations, stopping_criteria, crossover_rate, mutation_rate):
            self.num_particles = num_particles
            self.num_iterations = num_iterations
            self.stopping_criteria = stopping_criteria
            self.crossover_rate = crossover_rate
            self.mutation_rate = mutation_rate

        def optimize(self, objective_function, initial_solutions):
            bounds = [(-10, 10) for _ in range(len(initial_solutions[0]))]
            result = differential_evolution(objective_function, bounds,
                                            popsize=self.num_particles,
                                            maxiter=self.num_iterations,
                                            tol=self.stopping_criteria,
                                            mutation=self.mutation_rate,
                                            recombination=self.crossover_rate)
            return result.x

        def get_new_solution(self, current_solutions):
            # 这个方法在使用 scipy 的 differential_evolution 时不需要显式实现
            pass

    class ParticleSwarmOptimizationWithFireflyAlgorithm:
        """
        带有火焰算法的粒子群算法
        """

        def __init__(self, num_particles, num_iterations, stopping_criteria, alpha, beta, gamma, rho, q0, alpha_zero,
                     beta_zero, gamma_zero):
            self.num_particles = num_particles
            self.num_iterations = num_iterations
            self.stopping_criteria = stopping_criteria
            self.alpha = alpha
            self.beta = beta
            self.gamma = gamma
            self.rho = rho
            self.q0 = q0
            self.alpha_zero = alpha_zero
            self.beta_zero = beta_zero
            self.gamma_zero = gamma_zero

        def optimize(self, objective_function, initial_solutions):
            dim = len(initial_solutions[0])
            bounds = [(-10, 10)] * dim
            fa = FireflyAlgorithm(objective_function, dim, bounds, self.num_particles)
            best = fa.run(self.num_iterations)
            return best

        def get_new_solution(self, current_solutions):
            # 这个方法在使用 FireflyAlgorithm 库时不需要显式实现
            pass