Comparison of Single and Multi-objective Evolutionary Algorithms for Robust Link-State Routing

Abstract

Traffic Engineering (TE) approaches are increasingly important in network management to allow an optimized configuration and resource allocation. In link-state routing, the task of setting appropriate weights to the links is both an important and a challenging optimization task. A number of different approaches has been put forward towards this aim, including the successful use of Evolutionary Algorithms (EAs). In this context, this work addresses the evaluation of three distinct EAs, a single and two multi-objective EAs, in two tasks related to weight setting optimization towards optimal intra-domain routing, knowing the network topology and aggregated traffic demands and seeking to minimize network congestion. In both tasks, the optimization considers scenarios where there is a dynamic alteration in the state of the system, in the first considering changes in the traffic demand matrices and in the latter considering the possibility of link failures. The methods will, thus, need to simultaneously optimize for both conditions, the normal and the altered one, following a preventive TE approach towards robust configurations. Since this can be formulated as a bi-objective function, the use of multi-objective EAs, such as SPEA2 and NSGA-II, came naturally, being those compared to a single-objective EA. The results show a remarkable behavior of NSGA-II in all proposed tasks scaling well for harder instances, and thus presenting itself as the most promising option for TE in these scenarios.

Evolutionary robust optimization in production planning – interactions between number of objectives, sample size and choice of robustness measure

“…Pereira et al [42] compared the ability of evolutionary single and multi-objective algorithms to find robust solutions (link weight configurations for traffic routing processes). However, in Pereira et al [42] a solution was considered to be robust when it performed well under two different network conditions; in this sense, two performance measures (rather than one performance measure and one robustness measure) were optimized.…”

“…Pereira et al [42] compared the ability of evolutionary single and multi-objective algorithms to find robust solutions (link weight configurations for traffic routing processes). However, in Pereira et al [42] a solution was considered to be robust when it performed well under two different network conditions; in this sense, two performance measures (rather than one performance measure and one robustness measure) were optimized. The weighted sum of two congestion functions (two performance measures) was applied as fitness function in the ESO algorithm, whereas in both EMO algorithms, the non-dominated sorting genetic algorithm II (NSGA II) [15] and strength Pareto evolutionary algorithm 2, both congestion functions were optimized separately.…”