Evolutionary Dynamic Multi-objective Optimization via Regression Transfer Learning

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

4 Scopus Citations
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Author(s)

  • Zhenzhong WANG
  • Min JIANG
  • Xing GAO
  • Liang FENG
  • Weizhen HU

Related Research Unit(s)

Detail(s)

Original languageEnglish
Title of host publication2019 IEEE Symposium Series on Computational Intelligence
PublisherIEEE
Pages2375-2381
ISBN (Electronic)9781728124858
ISBN (Print)9781728124865
Publication statusPublished - Dec 2019

Publication series

NameIEEE Symposium Series on Computational Intelligence, SSCI

Conference

Title2019 IEEE Symposium Series on Computational Intelligence, SSCI 2019
PlaceChina
CityXiamen
Period6 - 9 December 2019

Abstract

Dynamic multi-objective optimization problems (DMOPs) remain a challenge to be settled, because of conflicting objective functions change over time. In recent years, transfer learning has been proven to be a kind of effective approach in solving DMOPs. In this paper, a novel transfer learning based dynamic multi-objective optimization algorithm (DMOA) is proposed called regression transfer learning prediction based DMOA (RTLP-DMOA). The algorithm aims to generate an excellent initial population to accelerate the evolutionary process and improve the evolutionary performance in solving DMOPs. When an environmental change is detected, a regression transfer learning prediction model is constructed by reusing the historical population, which can predict objective values. Then, with the assistance of this prediction model, some high-quality solutions with better predicted objective values are selected as the initial population, which can improve the performance of the evolutionary process. We compare the proposed algorithm with three state-of-the-art algorithms on benchmark functions. Experimental results indicate that the proposed algorithm can significantly enhance the performance of static multi-objective optimization algorithms and is competitive in convergence and diversity.

Research Area(s)

  • dynamic multi-objective optimization, evolutionary algorithm, regression prediction, transfer learning

Citation Format(s)

Evolutionary Dynamic Multi-objective Optimization via Regression Transfer Learning. / WANG, Zhenzhong; JIANG, Min; GAO, Xing; FENG, Liang; HU, Weizhen; TAN, Kay Chen.

2019 IEEE Symposium Series on Computational Intelligence. IEEE, 2019. p. 2375-2381 9002942 (IEEE Symposium Series on Computational Intelligence, SSCI).

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