000206840 001__ 206840
000206840 005__ 20190812205828.0
000206840 037__ $$aCONF
000206840 245__ $$aDistributed Particle Swarm Optimization using Optimal Computing Budget Allocation for Multi-Robot Learning
000206840 269__ $$a2015
000206840 260__ $$c2015
000206840 336__ $$aConference Papers
000206840 520__ $$aParticle Swarm Optimization (PSO) is a population-based metaheuristic that can be applied to optimize controllers for multiple robots using only local information. In order to cope with noise in the robotic performance evaluations, different re-evaluation strategies were proposed in the past. In this article, we apply a statistical technique called Optimal Computing Budget Allocation to improve the performance of distributed PSO in the presence of noise. In particular, we compare a distributed PSO OCBA algorithm suitable for resource-constrained mobile robots with a centralized version that uses global information for the allocation. We show that the distributed PSO OCBA outperforms a previous distributed noise-resistant PSO variant, and that the performance of the distributed PSO OCBA approaches that of the centralized one as the communication radius is increased. We also explore different parametrizations of the PSO OCBA algorithm, and show that the choice of parameter values differs from previous guidelines proposed for stand-alone OCBA.
000206840 700__ $$0244279$$g194716$$aDi Mario, Ezequiel Leonardo
000206840 700__ $$0247111$$g176482$$aNavarro, Inaki
000206840 700__ $$aMartinoli, Alcherio$$g105782$$0241071
000206840 7112_ $$dMay 25-28 2015$$cSendai, Japan$$aIEEE Congress on Evolutionary Computation
000206840 8564_ $$zPreprint$$yPreprint$$uhttps://infoscience.epfl.ch/record/206840/files/EDM_CEC15.pdf$$s333819
000206840 909C0 $$xU11904$$pDISAL$$0252151
000206840 909CO $$ooai:infoscience.tind.io:206840$$qGLOBAL_SET$$pconf$$pENAC
000206840 917Z8 $$x194716
000206840 917Z8 $$x194716
000206840 917Z8 $$x253580
000206840 917Z8 $$x222475
000206840 937__ $$aEPFL-CONF-206840
000206840 973__ $$rREVIEWED$$sACCEPTED$$aEPFL
000206840 980__ $$aCONF