Abstract
Imagine now that each PSO particle can also change its dimension, which means that they have the ability to jump to another (solution space) dimension as they see fit. In that dimension they simply do regular PSO moves but in any iteration they can still jump to any other dimension. In this chapter we shall show how the design of PSO particles is extended into Multi-dimensional PSO (MD PSO) particles so as to perform interdimensional jumps without altering or breaking the natural PSO concept.
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Kiranyaz, S., Ince, T., Gabbouj, M. (2014). Multi-dimensional Particle Swarm Optimization. In: Multidimensional Particle Swarm Optimization for Machine Learning and Pattern Recognition. Adaptation, Learning, and Optimization, vol 15. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-37846-1_4
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DOI: https://doi.org/10.1007/978-3-642-37846-1_4
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