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Evolutionary Multitask Optimization: a Methodological Overview, Challenges, and Future Research Directions

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Abstract

In this work, we consider multitasking in the context of solving multiple optimization problems simultaneously by conducting a single search process. The principal goal when dealing with this scenario is to dynamically exploit the existing complementarities among the problems (tasks) being optimized, helping each other through the exchange of valuable knowledge. Additionally, the emerging paradigm of evolutionary multitasking tackles multitask optimization scenarios by using biologically inspired concepts drawn from swarm intelligence and evolutionary computation. The main purpose of this survey is to collect, organize, and critically examine the abundant literature published so far in evolutionary multitasking, with an emphasis on the methodological patterns followed when designing new algorithmic proposals in this area (namely, multifactorial optimization and multipopulation-based multitasking). We complement our critical analysis with an identification of challenges that remain open to date, along with promising research directions that can leverage the potential of biologically inspired algorithms for multitask optimization. Our discussions held throughout this manuscript are offered to the audience as a reference of the general trajectory followed by the community working in this field in recent times, as well as a self-contained entry point for newcomers and researchers interested to join this exciting research avenue.

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Notes

  1. IEEE WCCI 2022, accepted competition on Evolutionary Multi-task Optimization: https://wcci2022.org/accepted-competitions/, accessed on February 11th, 2022.

  2. GECCO 2020 Competition on Evolutionary Multi-task Optimization, http://www.bdsc.site/websites/MTO_competition_2020/MTO_Competition_GECCO_2020.html, accessed on February 11th, 2022.

  3. Competitions on Evolutionary Multi-task Optimization held at IEEE Congress on Evolutionary Computation (CEC’2017 to CEC’2021) and Genetic and Evolutionary Computation Conference (GECCO’2020).

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Funding

The authors would like to thank the Basque Government for its funding support through the ELKARTEK program (3KIA project, KK-2020/00049) and the consolidated research group MATHMODE (ref. T1294-19).

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  1. Eneko Osaba and Javier Del Ser equally contributed to this article.

Authors and Affiliations

  1. TECNALIA, Basque Research and Technology Alliance (BRTA), P. Tecnologico, Ed. 700, 48160, Derio, Spain

    Eneko Osaba, Javier Del Ser & Aritz D. Martinez

  2. University of the Basque Country (UPV/EHU), 48013, Bilbao, Spain

    Javier Del Ser

  3. Edinburgh Napier University, Scotland, UK

    Amir Hussain

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  1. Eneko Osaba
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  2. Javier Del Ser
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Correspondence to Eneko Osaba or Javier Del Ser.

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Prof. Amir Hussain is the Editor in Chief of the Cognitive Computation journal. The authors declare that they have no other conflict of interest regarding this work.

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Osaba, E., Del Ser, J., Martinez, A.D. et al. Evolutionary Multitask Optimization: a Methodological Overview, Challenges, and Future Research Directions. Cogn Comput 14, 927–954 (2022). https://doi.org/10.1007/s12559-022-10012-8

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