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 ...

Paché, G. (2026) Patrick Geddes on Sustainable City Life. Journal of Service Science and Management, 19, 13-28. doi: ...

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 ...

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 ...

1 Guangzhou Institute of Building Science Group Co., Ltd., Guangzhou, Guangdong, China 2 Glenn Department of Civil Engineering, Clemson University, Clemson, SC, United States Modern seismic codes ...

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

In the context of using DNSGA2 to solve dynamic multi-objective optimization problems (DMOPs), a critical issue arises regarding the timing of the callback function execution and its impact on ...

Industrial organizations are racing to implement AI, yet many struggle to demonstrate concrete value from their investments. The missing element isn't better algorithms or more data; it's clarity ...