Abstract
Particle Swarm Optimizer (PSO) is one of the evolutionary computation techniques based on swarm intelligence. Comprehensive Learning Particle Swarm Optimizer (CLPSO) is a variant of the original Particle Swarm Optimizer which uses a new learning strategy to make the particles have different learning exemplars for different dimensions. This paper investigates the effects of learning proportion P c in the CLPSO, showing that different P c realizes different performance on different problems.
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Liang, J.J., Qin, A.K., Suganthan, P.N., Baskar, S. (2004). Evaluation of Comprehensive Learning Particle Swarm Optimizer. In: Pal, N.R., Kasabov, N., Mudi, R.K., Pal, S., Parui, S.K. (eds) Neural Information Processing. ICONIP 2004. Lecture Notes in Computer Science, vol 3316. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-30499-9_34
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DOI: https://doi.org/10.1007/978-3-540-30499-9_34
Publisher Name: Springer, Berlin, Heidelberg
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