Clustering based leaders' selection in multi-objective evolutionary algorithms

  • Authors:

  • Noura Al Moubayed;Andrei Petrovski;John McCall

  • Affiliations:

  • Robert Gordon University, Aberdeen, United Kingdom;Robert Gordon University, Aberdeen, United Kingdom;Robert Gordon University, Aberdeen, United Kingdom

  • Venue:
  • Proceedings of the 13th annual conference companion on Genetic and evolutionary computation
  • Year:
  • 2011

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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.