Noisy Optimization by Evolution Strategies With Online Population Size Learning

Research output: Journal Publications and Reviews (RGC: 21, 22, 62)21_Publication in refereed journalpeer-review

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Author(s)

  • Zhenhua Li
  • Shuo Zhang
  • Xinye Cai
  • Xiaomin Zhu
  • Zhun Fan
  • Xiuyi Jia

Related Research Unit(s)

Detail(s)

Original languageEnglish
Pages (from-to)5816-5828
Journal / PublicationIEEE Transactions on Systems, Man, and Cybernetics: Systems
Volume52
Issue number9
Online published13 Dec 2021
Publication statusPublished - Sep 2022

Abstract

Optimization modeling of real-world application problems usually involves noise from various sources. Noisy optimization imposes challenges to optimization methods since the objective values can be different for multiple evaluations. In this article, we propose a novel online population size learning (OPL) technique of evolution strategies for handling noisy optimization problems. By re-evaluating a fraction of the candidates, we measure the strength of noise level of the re-evaluated candidate solutions and adapt the population size according to the noise level. The proposed OPL combines the advantages of both explicit averaging by re-evaluations and the implicit averaging by large population size and overcomes their limitations. We incorporate it with the covariance matrix adaptation evolution strategy (CMA-ES) and obtain OPL-CMA-ES. Compared with the existing noise handling technique, the proposed OPL is much simpler in both concepts and computation. We conduct comprehensive experiments to evaluate the algorithm's performance on standard problems with Gaussian noise. We further evaluate the performance of OPL-CMA-ES on the black-box optimization benchmarks (BBOBs) noisy testbed, which is a standard platform for comparing black-box optimization algorithms, compared with the state-of-the-art noise-handling algorithms. The experimental results show that OPL-CMA-ES achieves remarkable performance and outperforms the compared variants.

Research Area(s)

  • Evolution strategies (ESs), Iron, Linear programming, Noise level, Noise measurement, noise strength, noisy optimization, online population size learning (OPL), Optimization, Sociology, Statistics

Citation Format(s)

Noisy Optimization by Evolution Strategies With Online Population Size Learning. / Li, Zhenhua; Zhang, Shuo; Cai, Xinye; Zhang, Qingfu; Zhu, Xiaomin; Fan, Zhun; Jia, Xiuyi.

In: IEEE Transactions on Systems, Man, and Cybernetics: Systems, Vol. 52, No. 9, 09.2022, p. 5816-5828.

Research output: Journal Publications and Reviews (RGC: 21, 22, 62)21_Publication in refereed journalpeer-review