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 Panicle 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 PC in the CLPSO, showing that different Pc realizes different performance on different problems. © Springer-Verlag Berlin Heidelberg 2004.
Cite
CITATION STYLE
Liang, J. J., Qin, A. K., Suganthan, P. N., & Baskar, S. (2004). Evaluation of comprehensive learning particle swarm optimizer. Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). Springer Verlag. https://doi.org/10.1007/978-3-540-30499-9_34
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