Large-Scale Optimization via Evolutionary Multitasking assisted Random Embedding

Research output: Chapters, Conference Papers, Creative and Literary Works (RGC: 12, 32, 41, 45)32_Refereed conference paper (with ISBN/ISSN)peer-review

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Detail(s)

Original languageEnglish
Title of host publication2020 IEEE Congress on Evolutionary Computation (CEC)
Subtitle of host publication2020 Conference Proceedings
PublisherInstitute of Electrical and Electronics Engineers
Number of pages8
ISBN (Electronic)978-1-7281-6929-3
ISBN (Print)978-1-7281-6930-9
Publication statusPublished - Jul 2020

Publication series

NameIEEE Congress on Evolutionary Computation, CEC - Conference Proceedings

Conference

Title2020 IEEE Congress on Evolutionary Computation, CEC 2020
LocationVirtual
PlaceUnited Kingdom
CityGlasgow
Period19 - 24 July 2020

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.

Research Area(s)

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

Citation Format(s)

Large-Scale Optimization via Evolutionary Multitasking assisted Random Embedding. / Feng, Yinglan; Feng, Liang; Hou, Yaqing; Tan, Kay Chen.

2020 IEEE Congress on Evolutionary Computation (CEC): 2020 Conference Proceedings. Institute of Electrical and Electronics Engineers, 2020. 9185660 (IEEE Congress on Evolutionary Computation, CEC - Conference Proceedings).

Research output: Chapters, Conference Papers, Creative and Literary Works (RGC: 12, 32, 41, 45)32_Refereed conference paper (with ISBN/ISSN)peer-review