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2018 | OriginalPaper | Buchkapitel

Finding the Most Influential Parameters of Coalitions in a PSO-CO Algorithm

verfasst von : Patricia Ruiz, Bernabé Dorronsoro, Juan Carlos de la Torre, Juan Carlos Burguillo

Erschienen in: Information Processing and Management of Uncertainty in Knowledge-Based Systems. Applications

Verlag: Springer International Publishing

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Abstract

Literature reveals that optimization algorithms are generally composed of a large number of parameters that highly influence on its performance. In the early stages of the definition of a new algorithm, it is crucial to know how the uncertainty in the input parameters affects the behavior of the algorithm, influencing on its final output, so that it is possible to set up the most efficient configuration.

In this work, we are making a sensitivity analysis using the Extended Fourier Amplitude Sensitivity Test to compute the first order effects and interactions for each parameter on a recently proposed particle swarm optimization algorithm that implements a dynamic structured swarm, based on coalitions. This technique, inherited from game theory, includes four new parameters that are analyzed and tested on a well-known benchmark for continuous optimization. Results give interesting insights of the importance of one of the parameters over the rest.

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Titel: Finding the Most Influential Parameters of Coalitions in a PSO-CO Algorithm
verfasst von: Patricia Ruiz
Bernabé Dorronsoro
Juan Carlos de la Torre
Juan Carlos Burguillo
Verlag: Springer International Publishing
Buch: Information Processing and Management of Uncertainty in Knowledge-Based Systems. Applications
Print ISBN: 978-3-319-91478-7

Electronic ISBN: 978-3-319-91479-4

Copyright-Jahr: 2018
DOI: https://doi.org/10.1007/978-3-319-91479-4_24

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