RT Conference Proceedings T1 A Coevolutionary Variable Neighborhood Search Algorithm for Discrete Multitasking (CoVNS): Application to Community Detection over Graphs A1 Osabay, Eneko A1 Villar-Rodriguezy, Esther A1 Seryz, Javier Del AB The main goal of the multitasking optimization paradigm is to solve multiple and concurrent optimization tasks in a simultaneous way through a single search process. For attaining promising results, potential complementarities and synergies between tasks are properly exploited, helping each other by virtue of the exchange of genetic material. This paper is focused on Evolutionary Multitasking, which is a perspective for dealing with multitasking optimization scenarios by embracing concepts from Evolutionary Computation. This work contributes to this field by presenting a new multitasking approach named as Coevolutionary Variable Neighborhood Search Algorithm, which finds its inspiration on both the Variable Neighborhood Search metaheuristic and coevolutionary strategies. The second contribution of this paper is the application field, which is the optimal partitioning of graph instances whose connections among nodes are directed and weighted. This paper pioneers on the simultaneous solving of this kind of tasks. Two different multitasking scenarios are considered, each comprising 11 graph instances. Results obtained by our method are compared to those issued by a parallel Variable Neighborhood Search and independent executions of the basic Variable Neighborhood Search. The discussion on such results support our hypothesis that the proposed method is a promising scheme for simultaneous solving community detection problems over graphs. PB Institute of Electrical and Electronics Engineers Inc. SN 9781728125473 YR 2020 FD 2020-12-01 LK https://hdl.handle.net/11556/2424 UL https://hdl.handle.net/11556/2424 LA eng NO Osabay , E , Villar-Rodriguezy , E & Seryz , J D 2020 , A Coevolutionary Variable Neighborhood Search Algorithm for Discrete Multitasking (CoVNS) : Application to Community Detection over Graphs . in 2020 IEEE Symposium Series on Computational Intelligence, SSCI 2020 . , 9308447 , 2020 IEEE Symposium Series on Computational Intelligence, SSCI 2020 , Institute of Electrical and Electronics Engineers Inc. , pp. 768-774 , 2020 IEEE Symposium Series on Computational Intelligence, SSCI 2020 , Virtual, Canberra , Australia , 1/12/20 . https://doi.org/10.1109/SSCI47803.2020.9308447 NO conference NO Publisher Copyright: © 2020 IEEE. DS TECNALIA Publications RD 28 jul 2024