RT Conference Proceedings
T1 A Multifactorial Cellular Genetic Algorithm for Multimodal Multitask Optimization
A1 Osaba, Eneko
A1 Del Ser, Javier
A1 Martinez, Aritz D.
A1 Lobo, Jesus L.
AB In multimodal optimization problems the main goal is to find as many global optima as possible by using a single search process. This type of optimization tasks emerges in many real-world scenarios in assorted fields including medicine, physics, and aerospace, among many others. However, addressing several multimodal optimization problems simultaneously has received little attention from the multitask optimization community to date. Even though solving different multimodal problems at the same time can largely benefit from the existing synergies among the modes of different tasks, this setup has been less studied than other optimization tasks. This work finds its inspiration in the incipient concepts of Evolutionary Multitasking and Multifactorial Optimization to propose a multifactorial Cellular Genetic Algorithm for solving multimodal optimization problems. Our designed algorithm expedites the search for the global optima of different problems at a time by including several algorithmic steps aimed at adapting the search itself as per the synergies found over the exploration of the problems' landscape. An extensive experimentation has been designed using 14 different functions from the CEC'2013 competition on multimodal optimization benchmark. Besides evaluating the performance of the devised algorithm to retain the global optima of every function in the benchmark, we also conduct an analysis of the transfer of knowledge among such functions. Finally, we compare its performance to that of a winning proposal in this CEC'2013 competition so as to reflect on the suitability of the multitasking paradigm to solve multimodal optimization tasks.
PB Institute of Electrical and Electronics Engineers Inc.
SN 9781665467087
YR 2022
FD 2022
LK https://hdl.handle.net/11556/2736
UL https://hdl.handle.net/11556/2736
LA eng
NO Osaba , E , Del Ser , J , Martinez , A D & Lobo , J L 2022 , A Multifactorial Cellular Genetic Algorithm for Multimodal Multitask Optimization . in 2022 IEEE Congress on Evolutionary Computation, CEC 2022 - Conference Proceedings . 2022 IEEE Congress on Evolutionary Computation, CEC 2022 - Conference Proceedings , Institute of Electrical and Electronics Engineers Inc. , 2022 IEEE Congress on Evolutionary Computation, CEC 2022 , Padua , Italy , 18/07/22 . https://doi.org/10.1109/CEC55065.2022.9870324
NO conference
NO Publisher Copyright: © 2022 IEEE.
NO ACKNOWLEDGMENTS The authors would like to thank the Basque Government through EMAITEK and ELKARTEK funding grants. J. Del Ser also acknowledges funding support from the Department of Education of the Basque Government (Consolidated Research Group MATHMODE, IT1294-19).
DS TECNALIA Publications
RD 28 jul 2024