Large-Scale optimization via Evolutionary Multitasking assisted Random Embedding

Yinglan Feng, Liang Feng, Yaqing Hou, Kay Chen Tan

Research output: Chapter in book / Conference proceedingConference article published in proceeding or bookAcademic researchpeer-review

1 Citation (Scopus)

Abstract

Evolutionary algorithms (EAs) often lose their superiority and effectiveness when applied to large-scale optimization problems. In the literature, many research studies have been proposed to improve the search performance of EAs, such as cooperative co-evolution, embedding, and new search operator design. Among those, memetic multi-agent optimization (MeMAO) is a recently proposed paradigm for high-dimensional problems by using random embeddings. It demonstrated high efficacy with the assumption of 'effective dimension However, as prior knowledge is always unknown for a given problem, this method may fail on the large-scale problems that do not have low effective dimensions. Taking this cue, we propose an evolutionary multitasking (EMT) assisted random embedding method (EMT-RE) for solving large-scale optimization problems. Instead of conducting a search on the randomly embedded space directly, we treat the embedded task as the auxiliary task for the given problem. By performing EMT with both the given problem and the randomly embedded task, not only the useful solutions found along the search can be transferred across tasks toward efficient problem solving, but the effectiveness of the search on problems Without a low effective dimensionality is also guaranteed. To evaluate the performance of newly proposed EMT-RE, comprehensive empirical studies are carried out on 8 synthetic continuous optimization functions with up to 2,000 dimensions.

Original languageEnglish
Title of host publication2020 IEEE Congress on Evolutionary Computation, CEC 2020 - Conference Proceedings
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781728169293
DOIs
Publication statusPublished - Jul 2020
Externally publishedYes
Event2020 IEEE Congress on Evolutionary Computation, CEC 2020 - Virtual, Glasgow, United Kingdom
Duration: 19 Jul 202024 Jul 2020

Publication series

Name2020 IEEE Congress on Evolutionary Computation, CEC 2020 - Conference Proceedings

Conference

Conference2020 IEEE Congress on Evolutionary Computation, CEC 2020
Country/TerritoryUnited Kingdom
CityVirtual, Glasgow
Period19/07/2024/07/20

Keywords

  • Evolutionary Multitasking
  • Knowledge Transfer
  • Large-Scale optimization
  • Random Embedding

ASJC Scopus subject areas

  • Control and Optimization
  • Decision Sciences (miscellaneous)
  • Artificial Intelligence
  • Computer Vision and Pattern Recognition
  • Hardware and Architecture

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