Abstract
Particle Swarm Optimisation (PSO) is an optimisation algorithm that shows promise. However its performance on complex problems with multiple minima falls short of that of the Ant Colony Optimisation (ACO) algorithm when both algorithms are applied to travelling salesperson type problems (TSP). Unlike ACO, PSO can be easily applied to a wider range of problems than TSP. This paper shows that by adding a memory capacity to each particle in a PSO algorithm performance can be significantly improved to a competitive level to ACO on the smaller TSP problems.
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Hendtlass, T. (2003). Preserving Diversity in Particle Swarm Optimisation. In: Chung, P.W.H., Hinde, C., Ali, M. (eds) Developments in Applied Artificial Intelligence. IEA/AIE 2003. Lecture Notes in Computer Science(), vol 2718. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-45034-3_4
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DOI: https://doi.org/10.1007/3-540-45034-3_4
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