Adaptive comprehensive learning particle swarm optimization with cooperative archive

Applied Soft Computing, pp. 533-546, 2019.

Cited by: 0|Bibtex|Views24|DOI:https://doi.org/10.1016/j.asoc.2019.01.047
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Other Links: dblp.uni-trier.de|academic.microsoft.com|www.sciencedirect.com

Abstract:

Comprehensive learning particle swarm optimization (CLPSO) enhances its exploration capability by exploiting all other particles’ historical information to update each particle’s velocity. However, CLPSO adopts a set of fixed comprehensive learning (CL) probabilities to learn from other particles, which may impair its performance on compl...More

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