Least possible time paths in stochastic time-varying networks
Computers and Operations Research
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
The particle swarm optimization algorithm: convergence analysis and parameter selection
Information Processing Letters
Linear phase FIR filter design using particle swarm optimization and genetic algorithms
Digital Signal Processing
Solving shortest path problem using particle swarm optimization
Applied Soft Computing
A comparison of solution strategies for biobjective shortest path problems
Computers and Operations Research
Learning and optimization using the clonal selection principle
IEEE Transactions on Evolutionary Computation
A genetic algorithm for shortest path routing problem and the sizing of populations
IEEE Transactions on Evolutionary Computation
A neural network for shortest path computation
IEEE Transactions on Neural Networks
Recursive structure element decomposition using migration fitness scaling genetic algorithm
ICSI'11 Proceedings of the Second international conference on Advances in swarm intelligence - Volume Part I
| Hi-index | 12.06 |
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.