This paper proposes a novel approach for cost-effective link failure localization in optical networks in order to improve the reliability of telecommunication systems. In such failure localization problems the optical network is usually represented by a graph, where the task is to form connected edge sets, so-called monitoring trails (m-trails), in a way that the failure of a link causes the failure of such a combination of m-trails, which unambiguously identifies the failed link. Every m-trail consumes a given amount of resources (like bandwidth, detectors, amplifiers, etc.). Thus, operators of optical network may prefer a set of paths, whose paths can be established in an easy and cost-effective way, while minimizing the interference with the route of the existing demands, i.e. may maximize the revenue. In this paper, unlike most existing techniques dealing with failure localization in this context, the presently proposed method considers a predefined set of paths in the graph as m-trails. This way the task can also be formulated as a special Set Covering Problem (SCP), whose general form is a frequently used formulation in a certain type of operations research problems (e.g. resource assignment). Since for the SCP task evolutionary algorithms, like Ant Colony Optimization (ACO), has been successfully applied in the operations research field, in this work the failure localization task is solved by using ACO on the SCP formulation of the described covering problem, which is a rather unique combination of approaches of different fields (telecommunication, operations research and evolutionary computation) placing our investigation in the multi-field scope of complex systems. © 2013 IEEE.

In this paper we introduce new, bacterial evolutionary algorithm (BEA) based methods for routing unicast and multicast demands in grooming capable multi-layer optical wavelength division multiplexing (WDM) networks. The methods introduced are compared with both well known heuristic methods, accumulative shortest path heuristic (ASP) and minimum path heuristic (MPH), and as well as with ILP. We prove the strength of our approach by comprehensive simulations in our versatile simulator.