Solving multicommodity capacitated network design problems using a multiobjective evolutionary algorithm

Evolutionary algorithms have been applied to a variety of network flow problems with acceptable results. In this research, a multiobjective evolutionary algorithm (MOEA) is used to solve a variation of the multicommodity capacitated network design problem (MCNDP). This variation represents a hybrid communication network as found in network centric models with multiple objectives including costs, delays, robustness, vulnerability, and reliability. Nodes in such centric systems can have multiple and varying link capacities, rates and information (commodity) quantities to be delivered and received. Each commodity can have an independent prioritized bandwidth requirement as well. Insight to the MCNDP problem domain and Pareto structure is developed. The nondominated sorting genetic algorithm (NSGA-II) is modified and extended to solve such a MCNDP. Since the MCNDP is highly constrained, a novel initialization procedure and mutation method are also integrated into this MOEA. Empirical results and analysis indicate that effective solutions are generated very efficiently.