ALMO: Active Learning-Based Multi-Objective Optimization for Accelerating Constrained Evolutionary Algorithms

In multi-objective optimization, standard evolutionary algorithms, such as NSGA-II, are computationally expensive, particularly when handling complex constraints. Constraint evaluations, often the bottleneck, require substantial resources. Pre-trained surrogate models have been used to improve compu...

Full description

Bibliographic Details

Published in:	Applied Sciences
Main Authors:	Karanpreet Singh, Rakesh K. Kapania
Format:	Article
Language:	English
Published:	MDPI AG 2024-10-01
Subjects:	active learning multi-objective optimization multi-disciplinary optimization machine learning evolutionary algorithms optimization
Online Access:	https://www.mdpi.com/2076-3417/14/21/9975