A Data-Driven Evolutionary Transfer Optimization for Expensive Problems in Dynamic Environments

Ke LI, Renzhi CHEN, Xin YAO

Research output: Journal PublicationsJournal Article (refereed)peer-review

2 Citations (Scopus)

Abstract

Many real-world problems are computationally costly and the objective functions evolve over time. Data-driven, a.k.a. surrogate-assisted, evolutionary optimization has been recognized as an effective approach to tackle expensive black-box optimization problems in a static environment whereas it has rarely been studied under dynamic environments. This article proposes a simple yet effective transfer learning framework to empower data-driven evolutionary optimization to solve expensive dynamic optimization problems. Specifically, a hierarchical multioutput Gaussian process is proposed to capture the correlation among data collected from different time steps with a linearly increased number of hyperparameters. Furthermore, an adaptive source task selection along with a bespoke warm staring initialization mechanisms are proposed to better leverage the knowledge extracted from previous optimization processes. By doing so, the data-driven evolutionary optimization can jump start the optimization in the new environment with a very limited computational budget. Experiments on synthetic benchmark test problems and a real-world case study demonstrate the effectiveness of our proposed algorithm in comparison with nine state-of-the-art peer algorithms.

Original languageEnglish
Pages (from-to)1396-1411
Number of pages16
JournalIEEE Transactions on Evolutionary Computation
Volume28
Issue number5
DOIs
Publication statusPublished - 2024
Externally publishedYes

Bibliographical note

Publisher Copyright:
© 1997-2012 IEEE.

Funding

This work was supported in part by the UKRI Future Leaders Fellowship under Grant MR/S017062/1 and Grant MR/X011135/1; in part by NSFC under Grant 62376056 and Grant 62076056; in part by the Royal Society under Grant IES/R2/212077; in part by EPSRC under Grant 2404317; in part by the Kan Tong Po Fellowship under Grant KTP\R1\231017; and in part by the Amazon Research Award and Alan Turing Fellowship.

Keywords

  • Data-driven evolutionary optimization
  • dynamic optimization
  • kernel methods
  • multiouput Gaussian processes (GPs)
  • transfer optimization

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