Evolutionary Large-Scale Multiobjective Optimization via Self-guided Problem Transformation

Research output: Chapters, Conference Papers, Creative and Literary WorksRGC 32 - Refereed conference paper (with host publication)peer-review

6 Scopus Citations
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Detail(s)

Original languageEnglish
Title of host publication2022 IEEE Congress on Evolutionary Computation (CEC)
Subtitle of host publicationConference Proceedings
PublisherIEEE
Number of pages8
ISBN (Electronic)9781665467087
ISBN (Print)978-1-6654-6709-4
Publication statusPublished - 2022

Publication series

NameIEEE Congress on Evolutionary Computation, CEC - Conference Proceedings

Conference

Title2022 International Joint Conference on Neural Networks (IJCNN 2022), the 2022 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE 2022), and the 2022 IEEE Congress on Evolutionary Computation (IEEE CEC 2022)
LocationPadua Congress Center
PlaceItaly
CityPadua
Period18 - 23 July 2022

Abstract

The performance of traditional multiobj ective evolutionary algorithms (MOEAs) often deteriorates rapidly when using them to solve large-scale multiobjective optimization problems (LMOPs). To effectively handle LMOPs, we propose a large-scale MOEA via self-guided problem transformation. In the proposed optimizer, the original large-scale search space is transferred to a lower-dimensional weighted space by the guidance of solutions themselves, aiming to effectively search in the weighted space for speeding up the convergence of the population. Specifically, the variables of the target LMOP are adaptively and randomly divided into multiple equal groups, and then solutions are self-guided to construct the small-scale weighted space correspondingly to these variable groups. In this way, each solution is projected as a self-guided vector with multiple weight variables, and then new weight vectors can be generated by searching in the weighted space. Next, new offspring is produced by inversely mapping the newly generated weight vectors to the original search space of this LMOP. Finally, the proposed optimizer is tested on two different LMOP test suites by comparing them with five competitive large-scale MOEAs. Experimental results show some advantages of the proposed algorithm in solving the considered benchmarks.

Research Area(s)

  • Evolutionary Algorithm, Large-Scale Multiobjective Optimization, Self-Guided Problem Transformation

Citation Format(s)

Evolutionary Large-Scale Multiobjective Optimization via Self-guided Problem Transformation. / Liu, Songbai; Jiang, Min; Lin, Qiuzhen et al.
2022 IEEE Congress on Evolutionary Computation (CEC): Conference Proceedings. IEEE, 2022. (IEEE Congress on Evolutionary Computation, CEC - Conference Proceedings).

Research output: Chapters, Conference Papers, Creative and Literary WorksRGC 32 - Refereed conference paper (with host publication)peer-review