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

Subset selection is an important component in evolutionary multiobjectiveoptimization (EMO) algorithms. Clustering, as a classic method to group similardata points together, has been used for subset selection in some fields.However, clustering-based methods have not been evaluated in the context ofsubset selection from solution sets obtained by EMO algorithms. In this paper,we first review some classic clustering algorithms. We also point out thatanother popular subset selection method, i.e., inverted generational distance(IGD)-based subset selection, can be viewed as clustering. Then, we perform acomprehensive experimental study to evaluate the performance of variousclustering algorithms in different scenarios. Experimental results are analyzedin detail, and some suggestions about the use of clustering algorithms forsubset selection are derived. Additionally, we demonstrate that decisionmaker's preference can be introduced to clustering-based subset selection.

Clustering-Based Subset Selection in Evolutionary Multiobjective Optimization