我们可以使用更高效的算法或者改进现有的算法来解决这个问题。一种方法是实现一种启发式算法，如禁忌搜索，来解决旅行商问题。禁忌搜索可以在不完全搜索整个解空间的情况下找到最优解。另一种方法是使用多个求解器并行化解决问题，增加计算并行度以提高求解器的效率，从而减少解决器过早退出的可能性。以下是使用禁忌搜索算法解决并行旅行商问题的Python代码示例：

class TabuSearchTSP:
    def __init__(self, distance_matrix, tabu_length):
        self.distance_matrix = distance_matrix
        self.tabu_list = TabuList(tabu_length)
        self.city_count = len(distance_matrix)
        self.curr_solution = None
        self.best_solution = None
        self.best_fitness = float('inf')
        self.neighbourhood = TwoOptSwapNeighbourhood()
        self.max_iterations = 1000
        self.iteration = 0

    def solve(self):
        self.iteration = 0
        self.curr_solution = random.sample(range(self.city_count), self.city_count)
        self.best_solution = self.curr_solution
        self.best_fitness = self.evaluate_solution(self.curr_solution)

        while self.iteration < self.max_iterations:
            self.iteration += 1
            neighbourhood_solutions = self.neighbourhood.explore_neighbourhood(self.curr_solution)
            best_candidate_solution = neighbourhood_solutions[0]
            best_candidate_fitness = self.evaluate_solution(best_candidate_solution)

            for candidate_solution in neighbourhood_solutions:
                candidate_fitness = self.evaluate_solution(candidate_solution)
                if (candidate_solution not in self.tabu_list) and candidate_fitness < best_candidate_fitness:
                    best_candidate_solution = candidate_solution
                    best_candidate_fitness = candidate_fitness

            self.curr_solution = best_candidate_solution
            self.tabu_list.update(self.curr_solution)
            current_fitness = self.evaluate_solution(self.curr_solution)

            if current_fitness < self.best_fitness: