This approach can be viewed as a memory plug-in for large models, providing a fresh perspective and direction for solving the ...

Abstract: An increasing number of machine learning algorithms are being applied to multi-objective optimization problems (MOPs), yielding promising results. However, many of these algorithms suffer ...

New design, verification and simulation solutions to re-engineer AI-powered product innovation at Synopsys Converge 2026.

Abstract: In recent years, many multimodal multi-objective evolutionary algorithms (MMOEAs) have been proposed for the widely existing multimodal multi-objective optimization problems (MMOPs) in ...

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 ...

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 ...

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 ...

Middle East peace, climate change, Ukraine — if Sisyphus were assigned one of today’s global problems, he’d plead to be returned to rock rolling. So let’s focus for a moment on a global challenge that ...