Binary-SDMOPSO and its application in channel selection for Brain-Computer Interfaces

Abstract

In [1], we introduced Smart Multi-Objective Particle Swarm Optimisation using Decomposition (SDMOPSO). The method uses the decomposition approach proposed in Multi-Objective Evolutionary Algorithms based on Decomposition (MOEA/D), whereby a multi-objective problem (MOP) is represented as several scalar aggregation problems. The scalar aggregation problems are viewed as particles in a swarm; each particle assigns weights to every optimisation objective. The problem is solved then as a Multi-Objective Particle Swarm Optimisation (MOPSO), in which every particle uses information from a set of defined neighbours. This work customize SDMOSPO to cover binary problems and applies the proposed binary method on the channel selection problem for Brain-Computer Interfaces(BCI).

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Cite this paper

@article{Moubayed2010BinarySDMOPSOAI, title={Binary-SDMOPSO and its application in channel selection for Brain-Computer Interfaces}, author={Noura Al Moubayed and Bashar Awwad Shiekh Hasan and John Q. Gan and Andrei Petrovski and John A. W. McCall}, journal={2010 UK Workshop on Computational Intelligence (UKCI)}, year={2010}, pages={1-6} }