Mar 23, 2025 · First, we briefly introduce the workflow of materials machine learning. Then, the Pareto fronts in multi-objective optimization and the corresponding algorithms are …

Such complicated systems urgently desire advanced methods to conquer the multi-objective optimization problems (MOPs). As a promising AI method, the development and application of …

Selected collection of recent research on multi-objective approach to machine learning; Recent developments in evolutionary multi-objective optimization; Applies the concept of Pareto …

Jan 30, 2023 · Different approaches are being used in the literature to reduce the number of objectives required for optimization. This work aims to propose a machine learning …

Feb 1, 2025 · Real-time decision-making in dynamic multi-objective optimization problems (DMOPs) is challenging due to constantly changing objectives and constraints. This paper …

Sep 1, 2022 · To accelerate data-driven studies for various optimization applications in chemical engineering, a comprehensive machine learning aided multi-objective optimization and multi …

Oct 10, 2018 · In this paper, we explicitly cast multi-task learning as multi-objective optimization, with the overall objective of finding a Pareto optimal solution. To this end, we use algorithms …

Dec 3, 2024 · Simultaneously considering multiple objectives in machine learning has been a popular approach for several decades, with various benefits for multi-task learning, the …

Extensive experiments combine four machine learning and deep learning algorithms with an evolution-ary optimisation algorithm. The performance of the combined training and …

Dec 5, 2021 · In multi-objective optimisation problems, we try to optimise many objective functions simultaneously while trying to find a balance between all competitive objective functions …