Too often, when comparing a set of optimization algorithms, little effort, if any at all, is spent for finding the parameter settings which let them perform at their best on a given optimization task. Within this context, automatizing the choice of their parameter settings can be seen as a way to perform fair comparisons between optimization algorithms. In this paper we first compare the performances of two standard PSO versions using the “standard” parameters suggested in the literature. Then, we automatically tune the parameter values of both algorithms using a meta-optimization environment, to allow the two versions to perform at their best. As expected, results obtained by the optimized version are substantially better than those obtained with the standard settings. Moreover, they generalize well on other functions, allowing one to draw interesting conclusions regarding the PSO parameter settings that are commonly used in the literature.

A fair comparison between standard PSO versions / Ugolotti, Roberto; Cagnoni, Stefano. - 587(2016), pp. 3-14. ((Intervento presentato al convegno 10th Italian Workshop on Artificial Life and Evolutionary Computation, WIVACE 2015 tenutosi a ita nel 2015 [10.1007/978-3-319-32695-5_1].

A fair comparison between standard PSO versions

UGOLOTTI, Roberto;CAGNONI, Stefano
2016

Abstract

Too often, when comparing a set of optimization algorithms, little effort, if any at all, is spent for finding the parameter settings which let them perform at their best on a given optimization task. Within this context, automatizing the choice of their parameter settings can be seen as a way to perform fair comparisons between optimization algorithms. In this paper we first compare the performances of two standard PSO versions using the “standard” parameters suggested in the literature. Then, we automatically tune the parameter values of both algorithms using a meta-optimization environment, to allow the two versions to perform at their best. As expected, results obtained by the optimized version are substantially better than those obtained with the standard settings. Moreover, they generalize well on other functions, allowing one to draw interesting conclusions regarding the PSO parameter settings that are commonly used in the literature.
9783319326948
9783319326948
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Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/11381/2813485
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