Abstract
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.
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Moubayed, N.A., Petrovski, A., McCall, J. (2011). Clustering-Based Leaders’ Selection in Multi-Objective Particle Swarm Optimisation. In: Yin, H., Wang, W., Rayward-Smith, V. (eds) Intelligent Data Engineering and Automated Learning - IDEAL 2011. IDEAL 2011. Lecture Notes in Computer Science, vol 6936. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-23878-9_13
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DOI: https://doi.org/10.1007/978-3-642-23878-9_13
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