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道路网络的优化是建立可持续发展交通运输系统的重要环节。从投资费用、可靠性和对环境的影响等3个因素综合考虑,构建了多目标道路网规划模型。将混合粒子群优化算法引入到道路网规划中,克服了传统优化方法易陷入局部最优和维数灾难等弊端,并应用微分进化算法确定混合粒子群的参数。通过算例求解验证表明了该方法的可行性和有效性,同时,与采用遗传算法所得结果进行比较,得知粒子群优化方法的搜索时间短而且优化结果更接近最优解。
Road network optimization is to establish an important part of the sustainable development of transport systems. Considering the three factors of investment cost, reliability and environmental impact, a multi-objective road network planning model is constructed. The hybrid particle swarm optimization algorithm is introduced into the road network planning to overcome the drawbacks of the traditional optimization methods such as easy to fall into local optimum and dimensionality disasters. The differential evolution algorithm is used to determine the parameters of the hybrid particle swarm optimization. The results show that the proposed method is feasible and effective, and compared with the results obtained by genetic algorithm, it is found that PSO has short search time and the optimized result is closer to the optimal solution.