A multi-cloud MLOps framework improves AI service reliability through automated deployment, canary releases, and ...

Combinatorial optimization problems are often encountered in real-world applications, including logistics, scheduling and ...

Abstract: Dynamic constrained multi-objective optimization problems (DCMOPs) present significant challenges due to the evolving nature of both objectives and constraints. These problems require ...

Decentralized protocols allow practically limitless access across multiple platforms. This opens a series of problems both at the level of individual users and industry-wide. The main problem is how ...

Arbor separates strategy from execution using isolated git worktrees, so engineering teams can finally trace which optimization actually moved the needle.

In the field of multi-objective evolutionary optimization, prior studies have largely concentrated on the scalability of objective functions, with relatively less emphasis on the scalability of ...

Tourism development in emerging destinations requires balancing economic benefits with ecological sustainability. In this study, we investigate the case of multi-attraction tourism planning in Qujing ...

Modern seismic codes ensure life safety, but code-compliant buildings can still suffer significant economic losses from earthquake-induced damage, even during moderate events. Performance-Based ...

Abstract: This paper studies the multi-objective optimization problems (MOPs) of Markovian jump systems (MJSs) closed by general controllers. Firstly, the linear quadratic regulator (LQR) problem of ...