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Parameter Prediction Based on Features of Evolved Instances for Ant Colony Optimization and the Traveling Salesperson Problem

  • Samadhi Nallaperuma
  • Markus Wagner
  • Frank Neumann
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8672)

Abstract

Ant colony optimization performs very well on many hard optimization problems, even though no good worst case guarantee can be given. Understanding the reasons for the performance and the influence of its different parameter settings has become an interesting problem. In this paper, we build a parameter prediction model for the Traveling Salesperson problem based on features of evolved instances. The two considered parameters are the importance of the pheromone values and of the heuristic information. Based on the features of the evolved instances, we successfully predict the best parameter setting for a wide range of instances taken from TSPLIB.

Keywords

Approximation Ratio Vehicle Route Problem Heuristic Information Hard Instance Travel Salesperson Problem 
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 International Publishing Switzerland 2014

Authors and Affiliations

  • Samadhi Nallaperuma
    • 1
  • Markus Wagner
    • 1
  • Frank Neumann
    • 1
  1. 1.Optimisation and Logistics, School of Computer ScienceThe University of AdelaideAustralia

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