Clustering-Based Leaders’ Selection in Multi-Objective Particle Swarm Optimisation

  • Noura Al Moubayed
  • Andrei Petrovski
  • John McCall
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6936)


Clustering-based Leaders’ Selection (CLS) is a novel approach for leaders selection in multi-objective particle swarm optimisation. Both objective and solution spaces are clustered. An indirect mapping between clusters in both spaces is defined to recognize regions with potentially better solutions. A leaders archive is built which contains representative particles of selected clusters in the objective and solution spaces. The results of applying CLS integrated with OMOPSO on seven standard multi-objective problems, show that clustering based leaders selection OMOPSO (OMOPSO/C) is highly competitive compared to the original algorithm.


Evolutionary Computation Multi-Objective Particle Swarm Optimisation Leaders’ Selection Density Based Spatial Clustering Principal Component Analysis Domination OMOPSO 


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© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Noura Al Moubayed
    • 1
  • Andrei Petrovski
    • 1
  • John McCall
    • 1
  1. 1.Robert Gordon UniversityAberdeenUK

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