Abstract
This paper investigates the effect of problem size on the roaming behaviour of particles in the particle swarm optimization (PSO) algorithm. Both the extent and impact of the roaming behaviour in the absence of boundary constraints is investigated, as well as the PSO algorithm’s ability to find good solutions outside of the area in which particles are initialized. Four basic PSO variations and a diverse set of real parameter benchmark problems were used as basis for the investigation. Problem size was found to have a significant impact on algorithm performance and roaming behaviour. The larger the problem is that is being considered, the more important it is to address roaming behaviour.
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Grobler, J., Engelbrecht, A.P. (2017). A Scalability Analysis of Particle Swarm Optimization Roaming Behaviour. In: Tan, Y., Takagi, H., Shi, Y. (eds) Advances in Swarm Intelligence. ICSI 2017. Lecture Notes in Computer Science(), vol 10385. Springer, Cham. https://doi.org/10.1007/978-3-319-61824-1_13
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DOI: https://doi.org/10.1007/978-3-319-61824-1_13
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