A Meta-Learning-Based Federated Surrogate-Assisted Evolutionary Algorithm for Dynamic Expensive Optimization

In recent years, surrogate-assisted evolutionary algorithms (SAEAs) have been extensively utilized to address expensive optimization problems. However, it becomes a great challenge how to train the surrogate using the dynamic data distributed across multiple clients due to privacy and security concerns. To address this issue, this paper proposes a meta-learning-based federated surrogate-assisted evolutionary algorithm for dynamic expensive optimization problems. The proposed algorithm utilizes a gradient-based meta-training algorithm to extract the experiences of historical optimization environments as the initial surrogate parameters in the new environment and then utilizes limited data samples for fine-tuning to achieve fast surrogate adaptation in the new environment. In addition, we utilize differential privacy techniques to add artificial noise to the parameters of the global and local surrogates to protect the private information of clients. Experimental results on a set of dynamic optimization benchmark problems with different settings show that the proposed federated surrogate-assisted evolutionary algorithm can achieve superior optimization performance while protecting data privacy.