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

02/04/2021
by   Eneko Osaba, et al.
50

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 as inspiration concepts drawn from 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 stimulate future efforts in this topic. 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.

READ FULL TEXT

page 4

page 22

page 23

page 25

page 26

page 27

page 28

page 29

03/24/2020

COEBA: A Coevolutionary Bat Algorithm for Discrete Evolutionary Multitasking

Multitasking optimization is an emerging research field which has attrac...
06/23/2022

A Survey on Learnable Evolutionary Algorithms for Scalable Multiobjective Optimization

Recent decades have witnessed remarkable advancements in multiobjective ...
11/29/2021

Evolutionary Multitask Optimization: Are we Moving in the Right Direction?

Transfer Optimization, understood as the exchange of information among s...
04/14/2020

dMFEA-II: An Adaptive Multifactorial Evolutionary Algorithm for Permutation-based Discrete Optimization Problems

The emerging research paradigm coined as multitasking optimization aims ...
06/14/2018

Theory of Estimation-of-Distribution Algorithms

Estimation-of-distribution algorithms (EDAs) are general metaheuristics ...
12/09/2013

A preliminary survey on optimized multiobjective metaheuristic methods for data clustering using evolutionary approaches

The present survey provides the state-of-the-art of research, copiously ...