COEBA: A Coevolutionary Bat Algorithm for Discrete Evolutionary Multitasking: A coevolutionary bat algorithm for discrete evolutionary multitasking

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ISBN: 978-3-030-50425-0
ISBN: 9783030504250
ISBN: 978-3-030-50426-7
DOI: 10.1007/978-3-030-50426-7_19
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2020
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Springer Nature
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Multitasking optimization is an emerging research field which has attracted lot of attention in the scientific community. The main purpose of this paradigm is how to solve multiple optimization problems or tasks simultaneously by conducting a single search process. The main catalyst for reaching this objective is to exploit possible synergies and complementarities among the tasks to be optimized, helping each other by virtue of the transfer of knowledge among them (thereby being referred to as Transfer Optimization). In this context, Evolutionary Multitasking addresses Transfer Optimization problems by resorting to concepts from Evolutionary Computation for simultaneous solving the tasks at hand. This work contributes to this trend by proposing a novel algorithmic scheme for dealing with multitasking environments. The proposed approach, coined as Coevolutionary Bat Algorithm, finds its inspiration in concepts from both co-evolutionary strategies and the metaheuristic Bat Algorithm. We compare the performance of our proposed method with that of its Multifactorial Evolutionary Algorithm counterpart over 15 different multitasking setups, composed by eight reference instances of the discrete Traveling Salesman Problem. The experimentation and results stemming therefrom support the main hypothesis of this study: the proposed Coevolutionary Bat Algorithm is a promising meta-heuristic for solving Evolutionary Multitasking scenarios.
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Publisher Copyright: © Springer Nature Switzerland AG 2020.
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Transfer optimization , Evolutionary multitasking , Bat algorithm , Multifactorial optimization , Traveling salesman problem , Transfer optimization , Evolutionary multitasking , Bat algorithm , Multifactorial optimization , Traveling salesman problem , Theoretical Computer Science , General Computer Science , Project ID , info:eu-repo/grantAgreement/EC/H2020/778035/EU/PDE-based geometric modelling, image processing, and shape reconstruction/PDE-GIR , info:eu-repo/grantAgreement/EC/H2020/778035/EU/PDE-based geometric modelling, image processing, and shape reconstruction/PDE-GIR , Funding Info , Eneko Osaba and Javier Del Ser would like to thank the Basque Government for its support through the EMAITEK and ELKARTEK programs. Javier Del Ser receives support from the Consolidated Research Group MATHMODE (IT1294-9) granted by the Department of Education of the Basque Government. Andres Iglesias and Akemi Galvez thank the Computer Science National Program of the Spanish Research Agency and European Funds, Project #TIN2017-89275-R (AEI/FEDER, UE), and the PDE-GIR project of the European Union’s Horizon 2020 programme, Marie Sklodowska-Curie Actions grant agreement #778035. , Eneko Osaba and Javier Del Ser would like to thank the Basque Government for its support through the EMAITEK and ELKARTEK programs. Javier Del Ser receives support from the Consolidated Research Group MATHMODE (IT1294-9) granted by the Department of Education of the Basque Government. Andres Iglesias and Akemi Galvez thank the Computer Science National Program of the Spanish Research Agency and European Funds, Project #TIN2017-89275-R (AEI/FEDER, UE), and the PDE-GIR project of the European Union’s Horizon 2020 programme, Marie Sklodowska-Curie Actions grant agreement #778035.
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Osaba , E , Del Ser , J , Yang , X-S , Iglesias , A & Galvez , A 2020 , COEBA: A Coevolutionary Bat Algorithm for Discrete Evolutionary Multitasking : A coevolutionary bat algorithm for discrete evolutionary multitasking . in V V Krzhizhanovskaya , G Závodszky , M H Lees , P M A Sloot , P M A Sloot , P M A Sloot , J J Dongarra , S Brissos & J Teixeira (eds) , unknown . vol. 12141 , 0302-9743 , Springer Nature , pp. 244-256 , 20th International Conference on Computational Science, ICCS 2020 , Amsterdam , Netherlands , 3/06/20 . https://doi.org/10.1007/978-3-030-50426-7_19
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