MO-MFCGA: Multiobjective multifactorial cellular genetic algorithm for evolutionary multitasking
Conference paper
Osaba, E., Del Ser, J., Martinez, A., Lobo, J., Nebro, A. and Yang, X. 2021. MO-MFCGA: Multiobjective multifactorial cellular genetic algorithm for evolutionary multitasking. 2021 IEEE Symposium Series on Computational Intelligence (SSCI). Orlando, FL, USA 05 - 07 Dec 2021 IEEE. pp. 1-8 https://doi.org/10.1109/SSCI50451.2021.9660024
Type | Conference paper |
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Title | MO-MFCGA: Multiobjective multifactorial cellular genetic algorithm for evolutionary multitasking |
Authors | Osaba, E., Del Ser, J., Martinez, A., Lobo, J., Nebro, A. and Yang, X. |
Abstract | Multiobjetive optimization has gained a considerable momentum in the evolutionary computation scientific community. Methods coming from evolutionary computation have shown a remarkable performance for solving this kind of optimization problems thanks to their implicit parallelism and the simultaneous convergence towards the Pareto front. In any case, the resolution of multiobjective optimization problems (MOPs) from the perspective of multitasking optimization remains almost unexplored. Multitasking is an incipient research stream which explores how multiple optimization problems can be simultaneously addressed by performing a single search process. The main motivation behind this solving paradigm is to exploit the synergies between the different problems (or tasks) being optimized. Going deeper, we resort in this paper to the also recent paradigm Evolutionary Multitasking (EM). We introduce the adaptation of the recently proposed Multifactorial Cellular Genetic Algorithm (MFCGA) for solving MOPs, giving rise to the Multiobjective MFCGA (MO-MFCGA). An extensive performance analysis is conducted using the Multiobjective Multifactorial Evolutionary Algorithm as comparison baseline. The experimentation is conducted over 10 multitasking setups, using the Multiobjective Euclidean Traveling Salesman Problem as benchmarking problem. We also perform a deep analysis on the genetic transferability among the problem instances employed, using the synergies among tasks aroused along the MO-MFCGA search procedure. |
Keywords | Multiobjective Optimization; Transfer Optimization; Evolutionary Multitasking; Cellular Genetic Algorithm; Traveling Salesman Problem |
Sustainable Development Goals | 9 Industry, innovation and infrastructure |
Middlesex University Theme | Creativity, Culture & Enterprise |
Conference | 2021 IEEE Symposium Series on Computational Intelligence (SSCI) |
Page range | 1-8 |
Proceedings Title | 2021 IEEE Symposium Series on Computational Intelligence (SSCI) |
ISBN | |
Electronic | 9781728190488 |
Paperback | 9781728190495 |
Publisher | IEEE |
Publication dates | |
Online | 24 Jan 2022 |
05 Dec 2021 | |
Publication process dates | |
Deposited | 18 Jan 2023 |
Accepted | 01 Oct 2021 |
Output status | Published |
Accepted author manuscript | |
Copyright Statement | © 2021 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works |
Digital Object Identifier (DOI) | https://doi.org/10.1109/SSCI50451.2021.9660024 |
Web of Science identifier | WOS:000824464300204 |
Web address (URL) of conference proceedings | https://ieeexplore.ieee.org/xpl/conhome/9659537/proceeding |
Language | English |
https://repository.mdx.ac.uk/item/8q3vq
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