DocumentCode :
239149
Title :
Analysis of fitness noise in particle swarm optimization: From robotic learning to benchmark functions
Author :
Di Mario, Ezequiel ; Navarro, Inaki ; Martinoli, Alcherio
Author_Institution :
Distrib. Intell. Syst. & Algorithms Lab., Ecole Polytech. Fed. de Lausanne, Lausanne, Switzerland
fYear :
2014
fDate :
6-11 July 2014
Firstpage :
2785
Lastpage :
2792
Abstract :
Population-based learning techniques have been proven to be effective in dealing with noise and are thus promising tools for the optimization of robotic controllers, which have inherently noisy performance evaluations. This article discusses how the results and guidelines derived from tests on benchmark functions can be extended to the fitness distributions encountered in robotic learning. We show that the large-amplitude noise found in robotic evaluations is disruptive to the initial phases of the learning process of PSO. Under these conditions, neither increasing the population size nor increasing the number of iterations are efficient strategies to improve the performance of the learning. We also show that PSO is more sensitive to good spurious evaluations of bad solutions than bad evaluations of good solutions, i.e., there is a non-symmetric effect of noise on the performance of the learning.
Keywords :
Gaussian noise; control engineering computing; intelligent robots; learning (artificial intelligence); learning systems; multi-robot systems; particle swarm optimisation; statistical distributions; PSO; benchmark functions; fitness distributions; fitness noise analysis; multirobotic learning; particle swarm optimization; robotic controller optimization; Benchmark testing; Noise; Noise measurement; Optimization; Robot sensing systems; Standards;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Evolutionary Computation (CEC), 2014 IEEE Congress on
Conference_Location :
Beijing
Print_ISBN :
978-1-4799-6626-4
Type :
conf
DOI :
10.1109/CEC.2014.6900514
Filename :
6900514
Link To Document :
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