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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, and Frank Neumann
Optimisation and Logistics, School of Computer Science, The University of Adelaide, AustraliaAbstract. 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.
LNCS 8672, p. 100 ff.
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© Springer International Publishing Switzerland 2014