Diversification Methods for Zero-One Optimization
  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 by the use of binarization procedures and by Diversification-Based
Learning (DBL) procedures which also 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.
