With the development of edge devices and cloud computing, the question of how to
accomplish machine learning and optimization tasks in a privacy-preserving and secure way …

Recent decades have witnessed great advancements in multiobjective evolutionary
algorithms (MOEAs) for multiobjective optimization problems (MOPs). However, these …

Evolutionary multitasking optimization (EMTO) is a new optimization paradigm proposed in
the field of evolutionary computation in recent years. EMTO can solve several different …

Bayesian optimization is a powerful surrogate-assisted algorithm for solving expensive black-
box optimization problems. While Bayesian optimization was developed for centralized …

Nature, with its numerous surprising rules, serves as a rich source of creativity for the
development of artificial intelligence, inspiring researchers to create several nature-inspired …

In surrogate-assisted evolutionary optimization, privacy-preservation and trusted data
sharing has become an increasingly important concern, especially in scenarios involving …

Conventional Bayesian optimization approaches assume that all available data are located
on one device, which does not consider privacy concerns since data storage and …

The construction of machine learning models involves many bi-level multiobjective
optimization problems (BL-MOPs), where upper-level (UL) candidate solutions must be …

In recent years, federated learning has made significant progress in preserving data privacy.
In this paradigm, clients train local models without sharing their raw data, thereby …

Recently, evolutionary computation (EC) has experienced significant advancements due to
the integration of machine learning, distributed computing, and big data technologies. These …