Network path optimization under dynamic conditions

Most network optimization problems are studied under a static scenario in which connectivity of the network and weights associated with the links of the networks are assumed to be fixed. However, in practice, they are likely to change with time and if the network is to be used over time under dynamic conditions, they need to be re-optimized as soon as there is a change. Since optimization process requires some finite time, there is a need for a efficient dynamic optimization strategy for solving such problems. In this study, we extend a previously proposed “Frozen-time” algorithm to network optimization by which new and optimized networks can be obtained in a computationally fast manner. We propose three different variations of the optimization strategies and show proof-of-principle simulation results on a 20-node network having 190 different source-destination paths. The results are interesting and suggest a viable further research.