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A New Hypervolume-based Evolutionary Algorithm for Many-objective Optimization
  • Ke Shang ,
  • Hisao Ishibuchi
Ke Shang
Southern University of Science and Technology, Southern University of Science and Technology

Corresponding Author:[email protected]

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Hisao Ishibuchi
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Abstract

In this paper, a new hypervolume-based evolution- ary multi-objective optimization algorithm (EMOA), namely R2HCA-EMOA (R2-based Hypervolume Contribution Approx- imation 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.
Oct 2020Published in IEEE Transactions on Evolutionary Computation volume 24 issue 5 on pages 839-852. 10.1109/TEVC.2020.2964705