No objectives succeed without data. Sustainability attributes scatter across messy text, images, and databases. Companies ...

Autoscaling is the primary method to control the performance level and the cost of cloud-native systems, thereby making them ...

A novel differentiable approach optimizes geometric waveguide coatings, achieving substantial gains in light efficiency and ...

Mathematical optimization offers today’s businesses a fundamentally different approach to worst-case scenario prepping. Rather than relying on gut instinct and static data, optimization leverages ...

This paper addresses carrier aircraft landing scheduling considering bolting and aerial refueling. It defines fuel and wake ...

By leveraging inference-time scaling and a novel "reflection" mechanism, ALE-Agent solves the context-drift problems that ...

ABSTRACT: Multi-objective optimization remains a significant and realistic problem in engineering. A trade-off among conflicting objectives subject to equality and inequality constraints is known as ...

Artificial deep neural networks (ADNNs) have become a cornerstone of modern machine learning, but they are not immune to challenges. One of the most significant problems plaguing ADNNs is the ...

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 ...

College of Mechanical and Electronic Engineering, Shanghai Jianqiao University, Shanghai, China Introduction: To enhance energy management in electric vehicles (EVs), this study proposes an ...

Abstract: Surrogate-assisted evolutionary algorithms (SAEAs) have gained increasing attention for addressing expensive many-objective optimization problems (EMaOPs). Generally, the same type of ...

Abstract: The conventional resource allocation methods, using a central node, are not resilient, owing to the failure of the central unit. An advanced solution is to apply distributed optimization by ...