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Journal of Heuristics

, Volume 25, Issue 4–5, pp 643–671 | Cite as

Diversification methods for zero-one optimization

  • Fred Glover
  • Gary Kochenberger
  • Weihong XieEmail author
  • Jianbin Luo
Article
  • 61 Downloads

Abstract

We introduce new diversification methods for zero-one optimization that significantly extend strategies previously introduced in the setting of metaheuristic search. Our methods incorporate easily implemented strategies for partitioning assignments of values to variables, accompanied by processes called augmentation and shifting which create greater flexibility and generality. We then show how the resulting collection of diversified solutions can be further diversified by means of permutation mappings, which equally can be used to generate diversified collections of permutations for applications such as scheduling and routing. These methods can be applied to non-binary vectors using binarization procedures and by diversification-based learning procedures that provide connections to applications in clustering and machine learning. Detailed pseudocode and numerical illustrations are provided to show the operation of our methods and the collections of solutions they create.

Keywords

Mathematical optimization Binary programming Metaheuristics Adaptive memory Learning 

Notes

Acknowledgements

This research has been supported in part by the Key Laboratory of International Education Cooperation of Guangdong University of Technology.

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Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2018

Authors and Affiliations

  1. 1.School of ManagementGuangdong University of TechnologyGuangzhouChina
  2. 2.ECEE-College of Engineering and Applied ScienceUniversity of Colorado – BoulderBoulderUSA
  3. 3.College of BusinessUniversity of Colorado at DenverDenverUSA

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