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.

[1]  S Kouchakzadeh,et al.  MULTI OBJECTIVE DYNAMIC DESIGN OF URBAN WATER DISTRIBUTION NETWORKS , 2012 .

[2]  Lidija Čuček,et al.  Dynamic Multi-objective Synthesis of Companies’ Renewable Biomass and Energy Supply-networks , 2013 .

[3]  Kalyanmoy Deb Kanpur.  Single and Multi-Objective Dynamic Optimization : Two Tales from an Evolutionary Perspective , 2011 .

[4]  Byung Ro Moon,et al.  Multiobjective evolutionary algorithms for dynamic social network clustering , 2010, GECCO '10.

[5]  Michiel C.J. Bliemer,et al.  Accelerating solving the dynamic multi-objective nework design problem using response surface methods , 2011 .

[6]  Giuseppe Cattaneo,et al.  An empirical study of dynamic graph algorithms , 1996, SODA '96.

[7]  Marco Laumanns,et al.  Performance assessment of multiobjective optimizers: an analysis and review , 2003, IEEE Trans. Evol. Comput..

[8]  Luc J. J. Wismans,et al.  Towards sustainable dynamic traffic management , 2012 .

[9]  Hongzhi Wang,et al.  Dynamic Graph Shortest Path Algorithm , 2012, WAIM.

[10]  Jim Kurose,et al.  Computer Networking: A Top-Down Approach (6th Edition) , 2007 .

[11]  Dorothea Wagner,et al.  Batch Dynamic Single-Source Shortest-Path Algorithms: An Experimental Study , 2009, SEA.

[12]  P. Subbaraj,et al.  Multiobjective Optimization Solution for Shortest Path Routing Problem , 2010 .

[13]  Kalyanmoy Deb,et al.  Dynamic Multi-objective Optimization and Decision-Making Using Modified NSGA-II: A Case Study on Hydro-thermal Power Scheduling , 2007, EMO.

[14]  Marzuki Khalid,et al.  Self organizing multi-objective optimization problem , 2011 .

[15]  Ramesh Govindan,et al.  Route flap damping exacerbates internet routing convergence , 2002, SIGCOMM 2002.

[16]  P. K. De,et al.  Dynamic Programming and Multi Objective Linear Programming approaches , 2011 .

[17]  Chao-Ton Su,et al.  Applying Hierarchical Genetic Algorithm Based Neural Network and Multiple Objective Evolutionary Algorithm to Optimize Parameter Design with Dynamic Characteristics , 2010 .

[18]  David Eppstein,et al.  Dynamic graph algorithms , 2010 .

[19]  Ronald L. Rivest,et al.  Introduction to Algorithms , 1990 .

[20]  K. Deb,et al.  Understanding knee points in bicriteria problems and their implications as preferred solution principles , 2011 .

[21]  Jim Kurose,et al.  Computer Networking: A Top-Down Approach , 1999 .

[22]  Luc J. J. Wismans,et al.  Acceleration of Solving the Dynamic Multi-Objective Network Design Problem Using Response Surface Methods , 2014, J. Intell. Transp. Syst..

[23]  Ronald L. Rivest,et al.  Introduction to Algorithms, third edition , 2009 .

[24]  P. Deepalakshmi,et al.  An Ant Colony Based Multi Objective Approach to Source-Initiated QoS Multicasting Method for Ad Hoc Networks , 2011 .