Improved quality of solutions for multiobjective spanning tree problem using distributed evolutionary algorithm

  • Authors:

  • Rajeev Kumar;P. K. Singh;P. P. Chakrabarti

  • Affiliations:

  • Department of Computer Science and Engineering, Indian Institute of Technology Kharagpur, Kharagpur, WB, India;Department of Computer Science and Engineering, Indian Institute of Technology Kharagpur, Kharagpur, WB, India;Department of Computer Science and Engineering, Indian Institute of Technology Kharagpur, Kharagpur, WB, India

  • Venue:
  • HiPC'04 Proceedings of the 11th international conference on High Performance Computing
  • Year:
  • 2004

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Abstract

The problem of computing spanning trees along with specific constraints has been studied in many forms Most of the problem instances are NP-hard, and many approximation and stochastic algorithms which yield a single solution, have been proposed Essentially, such problems are multi-objective in nature, and a major challenge to solving the problems is to capture possibly all the (representative) equivalent and diverse solutions at convergence In this paper, we attempt to solve the generic multi-objective spanning tree (MOST) problem, in a novel way, using an evolutionary algorithm (EA) We consider, without loss of generality, edge-cost and diameter as the two objectives, and use a multiobjective evolutionary algorithm (MOEA) that produces diverse solutions without needing a priori knowledge of the solution space We employ a distributed version of the algorithm and generate solutions from multiple tribes We use this approach for generating (near-) optimal spanning trees from benchmark data of different sizes Since no experimental results are available for MOST, we consider two well known diameter-constrained spanning tree algorithms and modify them to generate a Pareto-front for comparison Interestingly, we observe that none of the existing algorithms could provide good solutions in the entire range of the Pareto-front.