【摘要】 人工鱼群算法是一种新的群体智能优化算法,可较好地避免局部极值并取得全局极值,但针对离散优化问题却存在开发平衡及探索能力差等缺点。为此,设计一种自适应变异的人工鱼群算法,在迭代过程中添加变异算子并自动调节视野范围和拥挤度因子。将该算法应用于多等级选择的离散型交通网络二层规划模型设计中,上下层模型分别采用人工鱼群算法及Frank-Wolfe算法进行求解,从而为求解这类模型提供新方法。仿真结果表明,该算法具有较好的稳定性和收敛速度,能够应用于大型城市交通网络设计中。更多还原

【Abstract】 Artificial Fish Swarm Algorithm(AFSA) is a novel intelligence optimization algorithm,which may effectively avoid local optimum solution and find the global optimum solution.But the algorithm has several disadvantages such as the poor ability to keep the balance of exploration and exploration for the discrete optimization problems.To overcome the problems,an algorithm with normal mutation operator is proposed.It can dynamically adjust the vision and congestion operator.In the bi-level programming model for discrete network design problems,the upper model is solved by AFSA with normal mutation operator and lower model by the Frank-Wolfe algorithm.Simulation results demonstrate this algorithm is efficient and effective,and it is suitable for applying to large scale road network.更多还原