001019523 001__ 1019523
001019523 005__ 20250204113742.0
001019523 0247_ $$2doi$$a10.1007/s11721-023-00231-6
001019523 0247_ $$2ISSN$$a1935-3812
001019523 0247_ $$2ISSN$$a1935-3820
001019523 0247_ $$2datacite_doi$$a10.34734/FZJ-2023-05470
001019523 0247_ $$2WOS$$aWOS:001122634800001
001019523 037__ $$aFZJ-2023-05470
001019523 041__ $$aEnglish
001019523 082__ $$a004
001019523 1001_ $$0P:(DE-Juel1)184894$$aJimenez-Romero, Cristian$$b0$$ufzj
001019523 245__ $$aEmergent communication enhances foraging behavior in evolved swarms controlled by spiking neural networks
001019523 260__ $$aNew York, NY [u.a.]$$bSpringer$$c2024
001019523 3367_ $$2DRIVER$$aarticle
001019523 3367_ $$2DataCite$$aOutput Types/Journal article
001019523 3367_ $$0PUB:(DE-HGF)16$$2PUB:(DE-HGF)$$aJournal Article$$bjournal$$mjournal$$s1708320411_7678
001019523 3367_ $$2BibTeX$$aARTICLE
001019523 3367_ $$2ORCID$$aJOURNAL_ARTICLE
001019523 3367_ $$00$$2EndNote$$aJournal Article
001019523 520__ $$aSocial insects such as ants and termites communicate via pheromones which allow them to coordinate their activity and solve complex tasks as a swarm, e.g. foraging for food or finding their way back to the nest. This behavior was shaped through evolutionary processes over millions of years. In computational models, self-coordination in swarms has been implemented using probabilistic or pre-defined simple action rules to shape the decision of each agent and the collective behavior. However, manual tuned decision rules may limit the emergent behavior of the swarm. In this work we investigate the emergence of self-coordination and communication in evolved swarms without defining any explicit rule. For this purpose, we evolve a swarm of agents representing an ant colony. We use an evolutionary algorithm to optimize a spiking neural network (SNN) which serves as an artificial brain to control the behavior of each agent. The goal of the evolved colony is to find optimal ways to forage for food and return it to the nest in the shortest amount of time. In the evolutionary phase, the ants are able to learn to collaborate by depositing pheromone near food piles and near the nest to guide other ants. The pheromone usage is not manually encoded into the network; instead, this behavior is established through the optimization procedure. We observe that pheromone-based communication enables the ants to perform better in comparison to colonies where communication via pheromone did not emerge. Furthermore, we assess the foraging performance of the ant colonies by comparing the SNN-based model to a multi-agent rule-based system. Our results show that the SNN-based model can efficiently complete the foraging task in a short amount of time. Our approach illustrates that even in the absence of pre-defined rules, self-coordination via pheromone emerges as a result of the network optimization. This work serves as a proof of concept for the possibility of creating complex applications utilizing SNNs as underlying architectures for multiagent interactions where communication and self-coordination is desired.
001019523 536__ $$0G:(DE-HGF)POF4-5111$$a5111 - Domain-Specific Simulation & Data Life Cycle Labs (SDLs) and Research Groups (POF4-511)$$cPOF4-511$$fPOF IV$$x0
001019523 536__ $$0G:(EU-Grant)945539$$aHBP SGA3 - Human Brain Project Specific Grant Agreement 3 (945539)$$c945539$$fH2020-SGA-FETFLAG-HBP-2019$$x1
001019523 536__ $$0G:(EU-Grant)800858$$aICEI - Interactive Computing E-Infrastructure for the Human Brain Project (800858)$$c800858$$fH2020-SGA-INFRA-FETFLAG-HBP$$x2
001019523 536__ $$0G:(DE-Juel1)JL SMHB-2021-2027$$aJL SMHB - Joint Lab Supercomputing and Modeling for the Human Brain (JL SMHB-2021-2027)$$cJL SMHB-2021-2027$$x3
001019523 536__ $$0G:(DE-Juel1)HDS-LEE-20190612$$aHDS LEE - Helmholtz School for Data Science in Life, Earth and Energy (HDS LEE) (HDS-LEE-20190612)$$cHDS-LEE-20190612$$x4
001019523 536__ $$0G:(DE-Juel1)CSD-SSD-20190612$$aCSD-SSD - Center for Simulation and Data Science (CSD) - School for Simulation and Data Science (SSD) (CSD-SSD-20190612)$$cCSD-SSD-20190612$$x5
001019523 536__ $$0G:(DE-Juel1)PHD-NO-GRANT-20170405$$aPhD no Grant - Doktorand ohne besondere Förderung (PHD-NO-GRANT-20170405)$$cPHD-NO-GRANT-20170405$$x6
001019523 536__ $$0G:(DE-Juel1)Helmholtz-SLNS$$aSLNS - SimLab Neuroscience (Helmholtz-SLNS)$$cHelmholtz-SLNS$$x7
001019523 588__ $$aDataset connected to CrossRef, Journals: juser.fz-juelich.de
001019523 7001_ $$0P:(DE-Juel1)161462$$aYegenoglu, Alper$$b1$$eCorresponding author
001019523 7001_ $$0P:(DE-Juel1)184896$$aPérez Martín, Aarón$$b2$$ufzj
001019523 7001_ $$0P:(DE-Juel1)165859$$aDiaz, Sandra$$b3$$ufzj
001019523 7001_ $$0P:(DE-Juel1)151166$$aMorrison, Abigail$$b4$$ufzj
001019523 773__ $$0PERI:(DE-600)2394781-0$$a10.1007/s11721-023-00231-6$$p1-28$$tSwarm intelligence$$v18$$x1935-3812$$y2024
001019523 8564_ $$uhttps://juser.fz-juelich.de/record/1019523/files/FZJ-2023-05470.pdf$$yOpenAccess
001019523 8564_ $$uhttps://juser.fz-juelich.de/record/1019523/files/FZJ-2023-05470.gif?subformat=icon$$xicon$$yOpenAccess
001019523 8564_ $$uhttps://juser.fz-juelich.de/record/1019523/files/FZJ-2023-05470.jpg?subformat=icon-1440$$xicon-1440$$yOpenAccess
001019523 8564_ $$uhttps://juser.fz-juelich.de/record/1019523/files/FZJ-2023-05470.jpg?subformat=icon-180$$xicon-180$$yOpenAccess
001019523 8564_ $$uhttps://juser.fz-juelich.de/record/1019523/files/FZJ-2023-05470.jpg?subformat=icon-640$$xicon-640$$yOpenAccess
001019523 8767_ $$d2024-01-16$$eHybrid-OA$$jDEAL
001019523 909CO $$ooai:juser.fz-juelich.de:1019523$$pdnbdelivery$$popenCost$$pec_fundedresources$$pVDB$$pdriver$$pOpenAPC_DEAL$$popen_access$$popenaire
001019523 9101_ $$0I:(DE-588b)5008462-8$$6P:(DE-Juel1)184894$$aForschungszentrum Jülich$$b0$$kFZJ
001019523 9101_ $$0I:(DE-588b)5008462-8$$6P:(DE-Juel1)161462$$aForschungszentrum Jülich$$b1$$kFZJ
001019523 9101_ $$0I:(DE-588b)5008462-8$$6P:(DE-Juel1)184896$$aForschungszentrum Jülich$$b2$$kFZJ
001019523 9101_ $$0I:(DE-588b)5008462-8$$6P:(DE-Juel1)165859$$aForschungszentrum Jülich$$b3$$kFZJ
001019523 9101_ $$0I:(DE-588b)5008462-8$$6P:(DE-Juel1)151166$$aForschungszentrum Jülich$$b4$$kFZJ
001019523 9131_ $$0G:(DE-HGF)POF4-511$$1G:(DE-HGF)POF4-510$$2G:(DE-HGF)POF4-500$$3G:(DE-HGF)POF4$$4G:(DE-HGF)POF$$9G:(DE-HGF)POF4-5111$$aDE-HGF$$bKey Technologies$$lEngineering Digital Futures – Supercomputing, Data Management and Information Security for Knowledge and Action$$vEnabling Computational- & Data-Intensive Science and Engineering$$x0
001019523 9141_ $$y2024
001019523 915pc $$0PC:(DE-HGF)0000$$2APC$$aAPC keys set
001019523 915pc $$0PC:(DE-HGF)0001$$2APC$$aLocal Funding
001019523 915__ $$0LIC:(DE-HGF)CCBY4$$2HGFVOC$$aCreative Commons Attribution CC BY 4.0
001019523 915__ $$0StatID:(DE-HGF)0113$$2StatID$$aWoS$$bScience Citation Index Expanded$$d2023-08-22
001019523 915__ $$0StatID:(DE-HGF)3002$$2StatID$$aDEAL Springer$$d2023-08-22$$wger
001019523 915__ $$0StatID:(DE-HGF)0510$$2StatID$$aOpenAccess
001019523 915__ $$0StatID:(DE-HGF)0160$$2StatID$$aDBCoverage$$bEssential Science Indicators$$d2023-08-22
001019523 915__ $$0StatID:(DE-HGF)0200$$2StatID$$aDBCoverage$$bSCOPUS$$d2024-12-28
001019523 915__ $$0StatID:(DE-HGF)0199$$2StatID$$aDBCoverage$$bClarivate Analytics Master Journal List$$d2024-12-28
001019523 915__ $$0StatID:(DE-HGF)1040$$2StatID$$aDBCoverage$$bZoological Record$$d2024-12-28
001019523 915__ $$0StatID:(DE-HGF)1160$$2StatID$$aDBCoverage$$bCurrent Contents - Engineering, Computing and Technology$$d2024-12-28
001019523 915__ $$0StatID:(DE-HGF)0150$$2StatID$$aDBCoverage$$bWeb of Science Core Collection$$d2024-12-28
001019523 920__ $$lno
001019523 9201_ $$0I:(DE-Juel1)JSC-20090406$$kJSC$$lJülich Supercomputing Center$$x0
001019523 980__ $$ajournal
001019523 980__ $$aVDB
001019523 980__ $$aUNRESTRICTED
001019523 980__ $$aI:(DE-Juel1)JSC-20090406
001019523 980__ $$aAPC
001019523 9801_ $$aAPC
001019523 9801_ $$aFullTexts