RT Conference Proceedings
T1 DMFEA-II: 												An adaptive multifactorial evolutionary algorithm for permutation-based discrete optimization problems
A1 Osaba, Eneko
A1 Martinez, Aritz D.
A1 Galvez, Akemi
A1 Iglesias, Andres
A1 Ser, Javier Del
AB The emerging research paradigm coined as multitasking optimization aims to solve multiple optimization tasks concurrently by means of a single search process. For this purpose, the exploitation of complementarities among the tasks to be solved is crucial, which is often achieved via the transfer of genetic material, thereby forging the Transfer Optimization field. In this context, Evolutionary Multitasking addresses this paradigm by resorting to concepts from Evolutionary Computation. Within this specific branch, approaches such as the Multifactorial Evolutionary Algorithm (MFEA) has lately gained a notable momentum when tackling multiple optimization tasks. This work contributes to this trend by proposing the first adaptation of the recently introduced Multifactorial Evolutionary Algorithm II (MFEA-II) to permutation-based discrete optimization environments. For modeling this adaptation, some concepts cannot be directly applied to discrete search spaces, such as parent-centric interactions. In this paper we entirely reformulate such concepts, making them suited to deal with permutation-based search spaces without loosing the inherent benefits of MFEA-II. The performance of the proposed solver has been assessed over 5 different multitasking setups, composed by 8 datasets of the well-known Traveling Salesman (TSP) and Capacitated Vehicle Routing Problems (CVRP). The obtained results and their comparison to those by the discrete version of the MFEA confirm the good performance of the developed dMFEA-II, and concur with the insights drawn in previous studies for continuous optimization.
PB Association for Computing Machinery, Inc
SN 9781450371278
YR 2020
FD 2020-07-08
LK https://hdl.handle.net/11556/1922
UL https://hdl.handle.net/11556/1922
LA eng
NO Osaba , E , Martinez , A D , Galvez , A , Iglesias , A & Ser , J D 2020 , DMFEA-II : An adaptive multifactorial evolutionary algorithm for permutation-based discrete optimization problems . in GECCO 2020 Companion - Proceedings of the 2020 Genetic and Evolutionary Computation Conference Companion . GECCO 2020 Companion - Proceedings of the 2020 Genetic and Evolutionary Computation Conference Companion , Association for Computing Machinery, Inc , pp. 1690-1696 , 2020 Genetic and Evolutionary Computation Conference, GECCO 2020 , Cancun , Mexico , 8/07/20 . https://doi.org/10.1145/3377929.3398084
NO conference
NO Publisher Copyright: © 2020 ACM.
NO Eneko Osaba, Aritz D. Martinez and Javier Del Ser are supported by the Basque Government through the EMAITEK and ELKARTEK funding programs. Javier Del Ser receives support from the Consolidated Research Group MATHMODE (IT1294-19) granted by the Department of Education of the same institution. 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 UnionâĂŹs Horizon 2020 programme, Marie Sklodowska-Curie Actions grant agreement #778035.
DS TECNALIA Publications
RD 1 sept 2024