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Clustering based leaders' selection in multi-objective evolutionary algorithms

Al Moubayed, N; Petrovski, A; McCall, J

Authors

A Petrovski

J McCall



Contributors

Natalio Krasnogor
Editor

Abstract

Clustering-based Leaders Selection (CLS) is a novel leaders selection technique in multi-objective evolutionary algorithms. Clustering is applied on both the objective and solution spaces whereby each individual is assigned to two clusters; one in the objective space and the other in the solution space. Mapping between clusters in both spaces is then applied to recognize regions with potentially better solutions. A leaders archive is used where a representative of each cluster in the objective and solution spaces is stored. The results of applying CLS integrated with NSGAII on seven standard multi-objective problems, show that clustering based leaders selection NSGAII (NSGAII/C) is highly competitive comparing with the original algorithm.

Citation

Al Moubayed, N., Petrovski, A., & McCall, J. (2011). Clustering based leaders' selection in multi-objective evolutionary algorithms. In N. Krasnogor (Ed.), . https://doi.org/10.1145/2001858.2001913

Conference Name Proceedings of the 13th annual conference companion on Genetic and evolutionary computation - GECCO '11
Conference Location Dublin, Irland
Start Date Jul 12, 2011
End Date Jul 16, 2011
Online Publication Date Jul 12, 2011
Publication Date 2011-07
Deposit Date Jan 26, 2016
Publisher Association for Computing Machinery (ACM)
Pages 95-96
ISBN 9781450306904
DOI https://doi.org/10.1145/2001858.2001913
Keywords Leaders Selection, Multi-Objective Optimization, Clustering, Evolutionary Algorithm, Density Based Spatial Clustering, Principal Component Analysis.
Public URL https://durham-repository.worktribe.com/output/1151590