Clustering of local optima in combinatorial fitness landscapes

Authors:
Gabriela Ochoa;Sébastien Verel;Fabio Daolio;Marco Tomassini
Affiliations:
School of Computer Science, University of Nottingham, Nottingham, UK;INRIA Lille - Nord Europe and University of Nice Sophia-Antipolis, France;Information Systems Department, University of Lausanne, Lausanne, Switzerland;Information Systems Department, University of Lausanne, Lausanne, Switzerland
Venue:
LION'05 Proceedings of the 5th international conference on Learning and Intelligent Optimization
Year:
2011

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Instance generators and test suites for the multiobjective quadratic assignment problem

EMO'03 Proceedings of the 2nd international conference on Evolutionary multi-criterion optimization

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Abstract

Using the recently proposed model of combinatorial landscapes: local optima networks, we study the distribution of local optima in two classes of instances of the quadratic assignment problem. Our results indicate that the two problem instance classes give rise to very different configuration spaces. For the so-called real-like class, the optima networks possess a clear modular structure, while the networks belonging to the class of random uniform instances are less well partitionable into clusters. We briefly discuss the consequences of the findings for heuristically searching the corresponding problem spaces.