A Hybrid Surrogate-Assisted Evolutionary Algorithm for Computationally Expensive Many-Objective Optimization

Kanzhen Wan; Cheng He; Auraham Camacho; Ke Shang; Ran Cheng; Hisao Ishibuchi

doi:10.1109/CEC.2019.8789913

A Hybrid Surrogate-Assisted Evolutionary Algorithm for Computationally Expensive Many-Objective Optimization

Kanzhen Wan, Cheng He, Auraham Camacho, Ke Shang, Ran Cheng, Hisao Ishibuchi

Research output: Chapter in book / Conference proceeding › Conference article published in proceeding or book › Academic research › peer-review

12 Citations (Scopus)

Abstract

Many real-world optimization problems are challenging because the evaluation of solutions is computationally expensive. As a result, the number of function evaluations is limited. Surrogate-assisted evolutionary algorithms are promising approaches to tackle this kind of problems. However, their performance highly depends on the number of objectives. Thus, they may not be suitable for many-objective optimization. This paper proposes a novel hybrid algorithm for computationally expensive many-objective optimization, called C-M-EA. The proposed approach combines two surrogate-assisted evolutionary algorithms during the search process. We compare the performance of the proposed approach with seven multi-objective evolutionary algorithms. Our experimental results show that our approach is competitive for solving computationally expensive many-objective optimization problems.

Original language	English
Title of host publication	2019 IEEE Congress on Evolutionary Computation, CEC 2019 - Proceedings
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	2018-2025
Number of pages	8
ISBN (Electronic)	9781728121536
DOIs	https://doi.org/10.1109/CEC.2019.8789913
Publication status	Published - Jun 2019
Externally published	Yes
Event	2019 IEEE Congress on Evolutionary Computation, CEC 2019 - Wellington, New Zealand Duration: 10 Jun 2019 → 13 Jun 2019

Publication series

Name	2019 IEEE Congress on Evolutionary Computation, CEC 2019 - Proceedings

Conference

Conference	2019 IEEE Congress on Evolutionary Computation, CEC 2019
Country/Territory	New Zealand
City	Wellington
Period	10/06/19 → 13/06/19

Keywords

Expensive many-objective optimization
Hybrid optimization
Surrogate-assisted evolutionary optimization

ASJC Scopus subject areas

Computational Mathematics
Modelling and Simulation

More information

10.1109/CEC.2019.8789913

Cite this

Wan, K., He, C., Camacho, A., Shang, K., Cheng, R., & Ishibuchi, H. (2019). A Hybrid Surrogate-Assisted Evolutionary Algorithm for Computationally Expensive Many-Objective Optimization. In 2019 IEEE Congress on Evolutionary Computation, CEC 2019 - Proceedings (pp. 2018-2025). Article 8789913 (2019 IEEE Congress on Evolutionary Computation, CEC 2019 - Proceedings). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/CEC.2019.8789913

Wan, Kanzhen ; He, Cheng ; Camacho, Auraham et al. / A Hybrid Surrogate-Assisted Evolutionary Algorithm for Computationally Expensive Many-Objective Optimization. 2019 IEEE Congress on Evolutionary Computation, CEC 2019 - Proceedings. Institute of Electrical and Electronics Engineers Inc., 2019. pp. 2018-2025 (2019 IEEE Congress on Evolutionary Computation, CEC 2019 - Proceedings).

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Wan, K, He, C, Camacho, A, Shang, K, Cheng, R & Ishibuchi, H 2019, A Hybrid Surrogate-Assisted Evolutionary Algorithm for Computationally Expensive Many-Objective Optimization. in 2019 IEEE Congress on Evolutionary Computation, CEC 2019 - Proceedings., 8789913, 2019 IEEE Congress on Evolutionary Computation, CEC 2019 - Proceedings, Institute of Electrical and Electronics Engineers Inc., pp. 2018-2025, 2019 IEEE Congress on Evolutionary Computation, CEC 2019, Wellington, New Zealand, 10/06/19. https://doi.org/10.1109/CEC.2019.8789913

A Hybrid Surrogate-Assisted Evolutionary Algorithm for Computationally Expensive Many-Objective Optimization. / Wan, Kanzhen; He, Cheng; Camacho, Auraham et al.
2019 IEEE Congress on Evolutionary Computation, CEC 2019 - Proceedings. Institute of Electrical and Electronics Engineers Inc., 2019. p. 2018-2025 8789913 (2019 IEEE Congress on Evolutionary Computation, CEC 2019 - Proceedings).

Research output: Chapter in book / Conference proceeding › Conference article published in proceeding or book › Academic research › peer-review

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Wan K, He C, Camacho A, Shang K, Cheng R, Ishibuchi H. A Hybrid Surrogate-Assisted Evolutionary Algorithm for Computationally Expensive Many-Objective Optimization. In 2019 IEEE Congress on Evolutionary Computation, CEC 2019 - Proceedings. Institute of Electrical and Electronics Engineers Inc. 2019. p. 2018-2025. 8789913. (2019 IEEE Congress on Evolutionary Computation, CEC 2019 - Proceedings). doi: 10.1109/CEC.2019.8789913

A Hybrid Surrogate-Assisted Evolutionary Algorithm for Computationally Expensive Many-Objective Optimization

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