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Multicriteria Network Design Using Distributed Evolutionary Algorithm

  • Rajeev Kumar
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 2913)

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.

Keywords

Span Tree Network Design Pareto Front Exhaustive Search Multiobjective Optimization 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2003

Authors and Affiliations

  • Rajeev Kumar
    • 1
  1. 1.Department of Computer Science and EngineeringIndian Institute of Technology KharagpurKharagpurIndia

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