Find multi-objective paths in stochastic networks via chaotic immune PSO

Path finding is a fundamental research topic in transportation planning, intelligent transportation system, routine selection, etc. It is usually simplified as the shortest path (SP) in deterministic networks. However, some parameters in real life are stochastic. In this article, a more pragmatic model for stochastic networks was proposed, which not only considers determinist variables but also the mean and variances of random variables. In order to fasten the solution of our model, a novel method was proposed, which combines artificial immune system (AIS), chaos operator, and particle swarm optimization (PSO). Numerical experiments were presented to demonstrate that this proposed model is valid, effective, and more close to real-life, and CIPSO outperforms GA and PSO in respect of route optimality and convergence time.