Federated Bayesian optimization (FBO) has been introduced in recent years to avoid privacy leakage when multiple clients involve in finishing a global optimization task. Parameter-sharing-based FBOs, as one branch of FBOs, however, compromise the ...

• This work explores the combination of compressed sensing and BO to achieve privacy-preserving federated optimization via data sharing. Different from other data-sharing strategies in other privacy-preserving BO work, the proposed ...

Conventional Bayesian optimization approaches assume that all available data are located on one device, which does not consider privacy concerns since data storage and transmission may pose threats to data security. Existing differential privacy-...

• We propose to learn the appropriate level of noise to be added to each of the solution instead of the newly infilled solutions only.
• The level of noise is learned by optimizing a utility-privacy trade-off function that simultaneously ...

Building energy management usually involves a number of objectives, such as investment costs, thermal comfort, system resilience, battery life, and many others. However, most existing studies merely consider optimizing less than three objectives since it ...

In surrogate-assisted multi-/many-objective evolutionary optimization, each solution normally has an approximated value on each objective, resulting in increased difficulties in selecting solutions for expensive objective evaluations due to complicated ...

Surrogate-assisted evolutionary algorithms are one effective approach to handling expensive problems and have attracted increasing attention over the past decades. However, existing surrogate-assisted evolutionary algorithms pay little ...

Tire pattern image has always been an important clue in the handling of traffic accident cases and criminal investigation cases. By analyzing the tire pattern information left at the scene of the accident, the investigators can quickly narrow the scope ...

Neural Architecture Search (NAS) is studied to automatically design the deep neural network structure, freeing people from heavy network design tasks. Traditional NAS based on individual performance evaluation needs to train many networks generated by the ...

Various applications of the Internet of Things assisted by deep learning such as autonomous driving and smart furniture have gradually penetrated people’s social life. These applications not only provide people with great convenience but also promote the ...

For problems with irregular Pareto fronts, only part of the objective space is covered by optimal solutions. Most decomposition based evolutionary many-objective algorithms, however, predefine uniformly distributed weight or reference vectors, making them ...

The major difficulty in multi-objective optimization evolutionary algorithms (MOEAs) is how to find an appropriate solution which is able to converge towards the true Pareto Front with high diversity. In order to strengthen the ...

• We propose a new approach (EDV) that combines indicator I ε + with direction vectors to handle MaOPs.

With the apartment being a major residence styles, children are not provided with enough opportunities to play. Therefore, chances for learning many things that could be learned through playing these traditional plays are also decreasing. Interactive ...

Display Omitted Novel meta-heuristic to the multi-objective optimisation problem.The multi-objective optimization algorithm compared with other work in the literature.The algorithm possesses outstanding performance. In addition to dominance-based and ...