Multiobjective optimization of MPLS-IP networks with a variable neighborhood genetic algorithm

This paper presents a Genetic Algorithm for the optimization of multiple indices of Quality of Service of Multi Protocol Label Switching (MPLS) IP networks. The proposed algorithm, the Variable Neighborhood Multiobjective Genetic Algorithm (VN-MGA), is a Genetic Algorithm based on the NSGA-II, with the particular feature that solutions are encoded defining two different kinds of neighborhoods. The first neighborhood is defined by considering as decision variables the edges that form the routes to be followed by each request, whilst the second part of solution is kept constant. The second neighborhood is defined by considering the request sequence as decision variable, with the first part kept constant. Comparisons are performed with: (i) a VNS algorithm that performs a switch between the same two neighborhoods that are used in VN-MGA; and (ii) the results obtained with an integer linear programming solver, running a scalarized version of the multiobjective problem. The results indicate that the proposed VN-MGA outperforms the pure VNS algorithm, and provides a good approximation of the exact Pareto fronts obtained with Integer Linear Programming (ILP) approach, at a much smaller computational cost. Besides potential benefits of the application of the proposed approach to the optimization of packet routing in MPLS networks, this work raises the theoretical issue of the systematic application of variable encodings, which allow variable neighborhood searches, as generic operators inside general evolutionary computation algorithms. Codice rivista: E013138 Titolo rivista: APPLIED SOFT COMPUTING Issn: 1568-4946 Cordiali saluti CINECA - Servizio Gestione Rivist

ON THE IMPACT OF THE SOLUTION REPRESENTATION FOR THE INTERNET PROTOCOL NETWORK DESIGN PROBLEM WITH MAX-HOP CONSTRAINTS

The IP (Internet Protocol) Network Design Problem can be shortly stated as follows. Given a set of nodes and a set of traffic demands, we want to determine the minimum cost capacity installation such that all the traffic is routed. Capacity is provided by means of links of a given capacity and traffic must be loaded on the network according to the OSPF-ECM (Open Shortest Path First-Equal Commodity Multiflow) protocol, with additional constraints on the maximum number of hops. The problem is strongly NP-Hard, and the literature proposes local search-based heuristics that do not take into account max-hop constraints, or assume a simplified OSPF routing. The core in a local search approach is the network loading algorithm for the evaluation of the neighbor solutions costs. It presents critical aspects concerning both computational efficiency and memory requirements. Starting from a tabu search prototype, we show how these aspects deeply impact on the design of a local search procedure, even at the logical level. We present several properties of the related network loading problem, that allow to overcome the critical issues and lead to an efficient solution evaluation