python的viork算法代码
# Python Code for Viork Algorithm
# Implementing the Viork Algorithm (an algorithm for solving optimization problems)
import random
class ViorkAlgorithm:
    def __init__(self, problem_size, num_iterations, num_population, dimension_ranges):
        self.problem_size = problem_size # Number of decision variables
        self.num_iterations = num_iterations # Number of iterations
        self.num_population = num_population # Number of individuals in each population
        self.dimension_ranges = dimension_ranges # Range for each decision variable
       
        self.population = [] # Current population
        self.best_solution = None # Best solution found so far
    def initialize_population(self):
        for _ in range(self.num_population):
            solution = [random.uniform(range[0], range[1]) for range in self.dimension_ranges]
            self.population.append(solution)
        self.best_solution = self.population[0]
    def evaluate_fitness(self):
        for solution in self.population:
            fitness = self.calculate_fitness(solution)
            if fitness > self.calculate_fitness(self.best_solution):
                self.best_solution = solution
    def calculate_fitness(self, solution):
        # Calculate fitness based on the problem statement
        return sum([1 / (1 + x**2) for x in solution])
    def update_population(self):
        for i in range(self.num_iterations):
            for j in range(self.problem_size):
                r = random.uniform(0, 1)
                if r < 0.5:
                    solution = self.population[i][j] + random.uniform(0, 1) * (self.best_solution[j] - self.population[i][j])
                else:
                    solution = self.population[i][j] - random.uniform(0, 1) * (self.best_solution[j] - self.population[i][j])
                self.population[i][j] = solution
    def solve(self):
        self.initialize_population()
       
        for _ in range(self.num_iterations):
            self.evaluate_fitness()
            self.update_population()
        return self.best_solution
# Example Usage
random pythonproblem_size = 5
num_iterations = 100
num_population = 50
dimension_ranges = [(-10, 10)] * problem_size
viork = ViorkAlgorithm(problem_size, num_iterations, num_population, dimension_ranges)
best_solution = viork.solve()
print("Best Solution:", best_solution)

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