English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

We present a data mining approach for reducing the search space of localsearch algorithms in a class of binary integer programs including the setcovering and partitioning problems. The quality of locally optimal solutionstypically improves if a larger neighborhood is used, while the computation timeof searching the neighborhood increases exponentially. To overcome this, weextract variable associations from the instance to be solved in order toidentify promising pairs of flipping variables in the neighborhood search.Based on this, we develop a 4-flip neighborhood local search algorithm thatincorporates an efficient incremental evaluation of solutions and an adaptivecontrol of penalty weights. Computational results show that the proposed methodimproves the performance of the local search algorithm for large-scale setcovering and partitioning problems.

Exploiting variable associations to configure efficient local search algorithms in large-scale binary integer programs