Tabu Machine: A New Neural Network Solution Approach for Combinatorial Optimization Problems

  • Authors:

  • Minghe Sun;Hamid R. Nemati

  • Affiliations:

  • Department of Management Science and Statistics, College of Business, The University of Texas at San Antonio, San Antonio, TX 78249-0632, USA. msun@utsa.edu;Department of Information Systems and Operations Management, Bryan School of Business and Economics, The University of North Carolina, Greensboro, NC 27402-6165, USA. hrnemati@uncg.edu

  • Venue:
  • Journal of Heuristics
  • Year:
  • 2003

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Abstract

A new artificial neural network solution approach is proposed to solve combinatorial optimization problems. The artificial neural network is called the Tabu Machine because it has the same structure as the Boltzmann Machine does but uses tabu search to govern its state transition mechanism. Similar to the Boltzmann Machine, the Tabu Machine consists of a set of binary state nodes connected with bidirectional arcs. Ruled by the transition mechanism, the nodes adjust their states in order to search for a global minimum energy state. Two combinatorial optimization problems, the maximum cut problem and the independent set problem, are used as examples to conduct a computational experiment. Without using overly sophisticated tabu search techniques, the Tabu Machine outperforms the Boltzmann Machine in terms of both solution quality and computation time.