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dc.contributor.authorGarcía-Valdez, Mario
dc.contributor.authorMancilla, Alejandra
dc.contributor.authorCastillo, O.
dc.contributor.authorMerelo Guervos, Juan Julián 
dc.date.accessioned2023-04-13T07:39:13Z
dc.date.available2023-04-13T07:39:13Z
dc.date.issued2023-02-09
dc.identifier.citationGarcía-Valdez, M.; Mancilla, A.; Castillo, O.; Merelo-Guervós, J.J. Distributed and Asynchronous Population-Based Optimization Applied to the Optimal Design of Fuzzy Controllers. Symmetry 2023, 15, 467. [https://doi.org/10.3390/sym15020467]es_ES
dc.identifier.urihttps://hdl.handle.net/10481/81030
dc.descriptionData Availability Statement: All data and code are available with an open source license from https://github.com/mariosky/fuzzy-control (accessed on 17 January 2023).es_ES
dc.description.abstractDesigning a controller is typically an iterative process during which engineers must assess the performance of a design through time-consuming simulations; this becomes even more burdensome when using a population-based metaheuristic that evaluates every member of the population. Distributed algorithms can mitigate this issue, but these come with their own challenges. This is why, in this work, we propose a distributed and asynchronous bio-inspired algorithm to execute the simulations in parallel, using a multi-population multi-algorithmic approach. Following a cloud-native pattern, isolated populations interact asynchronously using a distributed message queue, which avoids idle cycles when waiting for other nodes to synchronize. The proposed algorithm can mix different metaheuristics, one for each population, first because it is possible and second because it can help keep total diversity high. To validate the speedup benefit of our proposal, we optimize the membership functions of a fuzzy controller for the trajectory tracking of a mobile autonomous robot using distributed versions of genetic algorithms, particle swarm optimization, and a mixed-metaheuristic configuration. We compare sequential versus distributed implementations and demonstrate the benefits of mixing the populations with distinct metaheuristics. We also propose a simple migration strategy that delivers satisfactory results. Moreover, we compare homogeneous and heterogenous configurations for the populations’ parameters. The results show that even when we use random heterogeneous parameter configuration in the distributed populations, we obtain an error similar to that in other work while significantly reducing the execution time.es_ES
dc.description.sponsorshipTecNM-15340.22-Pes_ES
dc.description.sponsorshipDemocratAI PID2020- 115570GB-C22es_ES
dc.language.isoenges_ES
dc.publisherMDPIes_ES
dc.rightsAtribución 4.0 Internacional*
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/*
dc.subjectFuzzy controles_ES
dc.subjectBio-inspired algorithmses_ES
dc.subjectDistributed algorithmses_ES
dc.titleDistributed and Asynchronous Population-Based Optimization Applied to the Optimal Design of Fuzzy Controllerses_ES
dc.typeinfo:eu-repo/semantics/articlees_ES
dc.rights.accessRightsinfo:eu-repo/semantics/openAccesses_ES
dc.identifier.doi10.3390/sym15020467
dc.type.hasVersioninfo:eu-repo/semantics/publishedVersiones_ES


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