id: 05972961
dt: a
an: 05972961
au: Moubayed, Noura Al; Petrovski, Andrei; McCall, John
ti: Clustering-based leaders’ selection in multi-objective particle swarm
    optimisation.
so: Yin, Hujun (ed.) et al., Intelligent data engineering and automated
    learning ‒ IDEAL 2011. 12th international conference, Norwich, UK,
    September 7‒9, 2011. Proceedings. Berlin: Springer (ISBN
    978-3-642-23877-2/pbk). Lecture Notes in Computer Science 6936, 100-107
    (2011).
py: 2011
pu: Berlin: Springer
la: EN
cc: 
ut: evolutionary computation; multi-objective particle swarm optimisation;
    leaders’ selection; density-based spatial clustering; principal
    component analysis; domination; OMOPSO
ci: 
li: doi:10.1007/978-3-642-23878-9_13
ab: Summary: 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.
rv: