Abstract
Particle swarm optimisation (PSO) is a general purpose optimisation algorithm in which a population of particles are attracted to their past success and the success of other particles. This paper introduces a new variant of the PSO algorithm, PSO with Enhanced Memory Particles, where the cognitive influence is enhanced by having particles remember multiple previous successes. The additional positions introduce diversity which aids exploration. Balancing the need for exploitation with this additional diversity is achieved through the use of a small memory and by using Roulette selection to select a single position from memory to use when calculating particles’ velocities. The research shows that PSO EMP performs better than the Standard PSO in most cases and does not perform significantly worse in any case.
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Broderick, I., Howley, E. (2014). Particle Swarm Optimisation with Enhanced Memory Particles. In: Dorigo, M., et al. Swarm Intelligence. ANTS 2014. Lecture Notes in Computer Science, vol 8667. Springer, Cham. https://doi.org/10.1007/978-3-319-09952-1_24
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DOI: https://doi.org/10.1007/978-3-319-09952-1_24
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-09951-4
Online ISBN: 978-3-319-09952-1
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