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

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

3 Scopus Citations
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Detail(s)

Original languageEnglish
Title of host publication2021 IEEE CONGRESS ON EVOLUTIONARY COMPUTATION
Subtitle of host publication2021 PROCEEDINGS
PublisherIEEE
Pages1672-1679
ISBN (Electronic)978-1-7281-8392-3, 978-1-7281-8393-0
ISBN (Print)978-1-7281-8394-7
Publication statusPublished - 2021

Publication series

NameIEEE Congress on Evolutionary Computation

Conference

Title2021 IEEE Congress on Evolutionary Computation (IEEE CEC 2021)
LocationVirtual
PlacePoland
CityKraków
Period28 June - 1 July 2021

Abstract

Since multi-objective optimization (MOO) involves multiple conflicting objectives, the high dimensionality of the solution space has a much more severe impact on multi-objective 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.

Research Area(s)

  • High-Dimensional Optimization, Evolutionary Multitasking, Random Embedding, Knowledge Transfer, ALGORITHM

Citation Format(s)

EMT-ReMO: Evolutionary Multitasking for High-Dimensional Multi-Objective Optimization via Random Embedding. / Feng, Yinglan; Feng, Liang; Hou, Yaqing et al.
2021 IEEE CONGRESS ON EVOLUTIONARY COMPUTATION: 2021 PROCEEDINGS. IEEE, 2021. p. 1672-1679 (IEEE Congress on Evolutionary Computation).

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