Surrogate-Assisted Expensive Many-Objective Optimization by Model Fusion

Cheng HE, Ran CHENG, Yaochu JIN, Xin YAO

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

5 Citations (Scopus)


Surrogate-assisted evolutionary algorithms have played an important role in expensive optimization where a small number of real-objective function evaluations are allowed. Usually, the surrogate models are used for the same purpose, e.g., to approximate the real-objective function or the aggregation fitness function. However, there is little work on surrogate-assisted optimization by model fusion, i.e., different surrogate models are fused for different purposes to improve the performance of the algorithm. In this work, we propose a surrogate-assisted approach by model fusion for solving expensive many-objective optimization problems, in which the Kriging assisted objective function approximation method is fused with the classifier assisted approach. The proposed algorithm is compared with some state-of-the-art surrogate-assisted algorithms on DTLZ problems and a real-world problem, and some encouraging results have been achieved by our proposed model fusion based approach. © 2019 IEEE.
Original languageEnglish
Title of host publication2019 IEEE Congress on Evolutionary Computation, CEC 2019 - Proceedings
PublisherInstitute of Electrical and Electronics Engineers Inc.
Number of pages8
ISBN (Print)9781728121536
Publication statusPublished - Jun 2019
Externally publishedYes

Bibliographical note

This work was supported by an EPSRC grant (No. EP/M017869/1), the Ministry of Science and Technology of China grant (No. 2017YFC0804003), and the Science and Technology Innovation Committee Foundation of Shenzhen grant (No. ZDSYS201703031748284).


  • classification
  • Expensive problem
  • fitness approximation
  • Kriging
  • many-objective optimization
  • model fusion
  • surrogate-assisted optimization


Dive into the research topics of 'Surrogate-Assisted Expensive Many-Objective Optimization by Model Fusion'. Together they form a unique fingerprint.

Cite this