Utilize AI to analyze application runtime data (e.g., rendering time, communication latency), obtain optimization suggestions (such as reducing component re-rendering, reusing hardware connections), ...
The challenge of resource allocation for UAV swarms in dynamic and uncertain electromagnetic environments has been ...
“I’m working on a Multi-Objective Bayesian Optimization (MOBO) problem involving a system with roughly 60 input parameters and around 30 performance evaluation metrics that we would ideally like to ...
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 ...
Abstract: Railway alignment design is a crucial but difficult task that should trade off many objective factors. Currently, although several Multi-objective Intelligent Alignment Optimization (M-IAO) ...
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 ...
This repository contains the code to run the experiments in the paper (Buckingham et al., 2025): Buckingham, J. M., Rojas Gonzalez, S., & Branke, J. (2025). Knowledge Gradient for Multi-Objective ...
Search optimization now requires combining traditional SEO with AI-focused GEO and answer-driven AEO strategies AI search usage continues to grow, with 10% of US consumers currently using generative ...
1 School of Mathematics and Statistics, Sichuan University of Science and Engineering, Zigong, China. 2 Institute of Computational Mathematics and Scientific/Engineering Computing, Chinese Academy of ...
Abstract: This paper addresses multi-objective optimization problems using conflict-averse multi-objective extremum seeking (CAMOES) for unknown static mapping. As for the traditional multi-objective ...
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