EMT-ReMO : Evolutionary Multitasking for High-Dimensional Multi-Objective Optimization via Random Embedding

Yinglan FENG, Liang FENG, Yaqing HOU, Kay Chen TAN, Sam KWONG

Research output: Book Chapters | Papers in Conference ProceedingsConference paper (refereed)Researchpeer-review

3 Citations (Scopus)


Since multi-objective optimization (MOO) involves multiple conflicting objectives, the high dimensionality of the solution space has a much more severe impact on multiobjective problems than single-objective optimization. Taking the advantage of random embedding, some related works have been proposed to scale derivative-free MOO methods to high-dimensional functions. However, with the premise of “low effective dimensionality”, a single randomly embedded subspace cannot guarantee the effectiveness of obtained solutions. Taking this cue, we propose an evolutionary multitasking paradigm for multi-objective optimization via random embedding (EMT-ReMO) to enhance the efficiency and effectiveness of current embedding-based methods in solving high-dimensional optimization problems with low effective dimensions. In EMT-ReMO, the target problem is firstly embedded into multiple low-dimensional subspaces by using different random embeddings, aiming to build up a multi-task environment for identifying the underlying effective subspace. Then the implicit multi-objective evolutionary multitasking is performed with seamless knowledge transfer to enhance the optimization process. Experimental results obtained on six high-dimensional MOO functions with or without low effective dimensions have confirmed the effectiveness as well as the efficiency of the proposed EMT-ReMO.

Original languageEnglish
Title of host publicationProceedings of the 2021 IEEE Congress on Evolutionary Computation, CEC 2021
PublisherInstitute of Electrical and Electronics Engineers Inc.
Number of pages8
ISBN (Electronic)9781728183930
ISBN (Print)9781728183947
Publication statusPublished - 2021
Externally publishedYes
Event2021 IEEE Congress on Evolutionary Computation - Virtual, Krakow, Poland
Duration: 28 Jun 20211 Jul 2021


Conference2021 IEEE Congress on Evolutionary Computation
Abbreviated titleCEC 2021
CityVirtual, Krakow

Bibliographical note

Funding Information:
This work is partially supported by the National Key Research and Development Project, Ministry of Science and Technology, China (Grant No. 2018AAA0101301), and by the National Natural Science Foundation of China (NSFC) under grant No. 61876162, No. 61876025 and No. 61906032, and by the Research Grants Council of the Hong Kong SAR under grant No. PolyU11202418 and grant No. PolyU11209219.

Publisher Copyright:
© 2021 IEEE


  • Evolutionary multitasking
  • High-dimensional optimization
  • Knowledge transfer
  • Random embedding


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