Incremental Data-driven Optimization of Complex Systems in Nonstationary Environments

TL;DR

This paper introduces a novel data-driven optimization algorithm for complex systems operating in nonstationary environments, utilizing ensemble learning, multi-task evolutionary algorithms, and outlier detection to adaptively track optimal solutions.

Contribution

It presents an integrated approach combining ensemble learning, multi-task optimization, and outlier detection specifically designed for dynamic, nonstationary environments, which is a novel contribution.

Findings

Outperforms four state-of-the-art algorithms on six benchmark problems.

Effectively tracks optimal solutions in changing environments.

Demonstrates robustness and adaptability in dynamic settings.

Abstract

Existing work on data-driven optimization focuses on problems in static environments, but little attention has been paid to problems in dynamic environments. This paper proposes a data-driven optimization algorithm to deal with the challenges presented by the dynamic environments. First, a data stream ensemble learning method is adopted to train the surrogates so that each base learner of the ensemble learns the time-varying objective function in the previous environments. After that, a multi-task evolutionary algorithm is employed to simultaneously optimize the problems in the past environments assisted by the ensemble surrogate. This way, the optimization tasks in the previous environments can be used to accelerate the tracking of the optimum in the current environment. Since the real fitness function is not available for verifying the surrogates in offline data-driven optimization, a support vector domain description that was designed for outlier detection is introduced to select a reliable solution. Empirical results on six dynamic optimization benchmark problems demonstrate the effectiveness of the proposed algorithm compared with four state-of-the-art data-driven optimization algorithms.