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Annals of Computer Science and Information Systems, Volume 13

Communication Papers of the 2017 Federated Conference on Computer Science and Information Systems

The Revised Stochastic Simplex Bisection Algorithm and Particle Swarm Optimization

DOI: http://dx.doi.org/10.15439/2017F119

Citation: Communication Papers of the 2017 Federated Conference on Computer Science and Information Systems, M. Ganzha, L. Maciaszek, M. Paprzycki (eds). ACSIS, Vol. 13, pages 103110 ()

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Abstract. The stochastic simplex bisection (SSB) algorithm is evaluated against particle swarm optimization (PSO) on a prominent test set. The original SSB algorithm performs on par with the PSO algorithm and a revised version of the SSB algorithm outperforms both of them. Detailed analysis of the performance on select objective functions brings to light key properties of the three algorithms. The core SSB algorithm is here viewed as a sampling tool for an outer loop that employs statistical pattern recognition. This opens the door for a host of other schemes.


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