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

doi:10.1109/TEVC.2023.3307244

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

2024-10-01

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DOI：10.1109/TEVC.2023.3307244

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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.

Keywords:

Optimization

Linear programming

Computational modeling

Task analysis

Iron

Closed box

Uncertainty

Data-driven evolutionary optimization

dynamic optimization

kernel methods

multiouput Gaussian processes (GPs)

transfer optimization

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

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