Multi-objective framework for cost-effective OTN switch placement using NSGA-II with embedded domain knowledge

Abstract

Abstract The continuous growth of traffic coming from a plethora of bandwidth-hungry applications will drive network operators to further pursue strategies to cost-efficiently plan and dimension their transport networks. In view of this trend, this paper presents an evolutionary multi-objective design framework for routing a set of services in an optical transport network such that the key resources impacting capital expenditures (CapEx) – line interfaces and optical transport network (OTN) switches – are minimized. Particularly, the multi-objective problem is customized to select the most cost-effective network nodes to place OTN switches, while at the same time keeping the number of line interfaces required to a minimum. To solve the multi-objective design problem, different strategies were considered to produce the Pareto front of non-dominated solutions, using the Non-dominated Sorting Genetic Algorithm (NSGA-II). These strategies differ on how mutation and crossover solutions are generated: randomly or exploiting prior knowledge. The solution quality obtained with both strategies after a fixed number of generations is compared. The results indicate that embedding expert knowledge within the genetic algorithm leads to better convergence results. Moreover, the knowledge-based implementation of the genetic algorithm presents on average a 59% increase in the hyper volume rate when compared to the purely random evolutionary algorithm for the same number of generations.