Multicriteria Network Design Using Distributed Evolutionary Algorithm

Abstract

In this paper, we revisit a general class of multicriteria multiconstrained network design problems and attempt to solve, in a novel way, with a Distributed Evolutionary Algorithm (EA). A major challenge to solving such problems is to capture possibly all the (representative) equivalent and diverse solutions at convergence. In this work, we formulate, without loss of generality, a bi-criteria bi-constrained communication network topological design problem. Two of the primary objectives to be optimized are network delay and cost subject to satisfaction of reliability and flow-constraints. This is a NP-hard problem and the two-objective optimal solution front is not known a priori. Therefore, we adopt randomized search method and use multiobjective EA that produces diverse solution-space. We employ a distributed version of the algorithm and generate solutions from multiple tribes to ensure convergence. We tested this approach for designing networks of different sizes and found that the approach scales well with larger networks. Results are compared with those obtained by two traditional approaches namely, the exhaustive search and a heuristic search.