A New Hypervolume-Based Evolutionary Algorithm for Many-Objective Optimization

In this article, a new hypervolume-based evolutionary multiobjective optimization algorithm (EMOA), namely, R2HCA-EMOA (R2-based hypervolume contribution approximation EMOA), is proposed for many-objective optimization. The core idea of the algorithm is to use an R2 indicator variant to approximate...

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Bibliographic Details
Published in:IEEE transactions on evolutionary computation Vol. 24; no. 5; pp. 839 - 852
Main Authors: Shang, Ke, Ishibuchi, Hisao
Format: Journal Article
Language:English
Published: New York IEEE 01.10.2020
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects:
Algorithms
Approximation algorithms
Convergence
Evolutionary algorithms
hypervolume contribution approximation
many-objective optimization
Mathematical analysis
Monte Carlo methods
Multiple objective analysis
Optimization
Sociology
Tensors
ISSN:1089-778X, 1941-0026
Online Access:Get full text
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Summary:In this article, a new hypervolume-based evolutionary multiobjective optimization algorithm (EMOA), namely, R2HCA-EMOA (R2-based hypervolume contribution approximation EMOA), is proposed for many-objective optimization. The core idea of the algorithm is to use an R2 indicator variant to approximate the hypervolume contribution. The basic framework of the proposed algorithm is the same as SMS-EMOA. In order to make the algorithm computationally efficient, a utility tensor structure is introduced for the calculation of the R2 indicator variant. Moreover, a normalization mechanism is incorporated into R2HCA-EMOA to enhance the performance of the algorithm. Through experimental studies, R2HCA-EMOA is compared with three hypervolume-based EMOAs and several other state-of-the-art EMOAs on 5-, 10-, and 15-objective DTLZ, WFG problems, and their minus versions. Our results show that R2HCA-EMOA is more efficient than the other hypervolume-based EMOAs, and is superior to all the compared state-of-the-art EMOAs.
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ISSN:1089-778X
1941-0026
DOI:10.1109/TEVC.2020.2964705