Large language model-aided evolutionary search for constrained multiobjective optimization
Evolutionary algorithms excel in solving complex optimization problems, especially those
with multiple objectives. However, their stochastic nature can sometimes hinder rapid …
with multiple objectives. However, their stochastic nature can sometimes hinder rapid …
Coevolutionary multitasking for constrained multiobjective optimization
Addressing the challenges of constrained multiobjective optimization problems (CMOPs)
with evolutionary algorithms requires balancing constraint satisfaction and optimization …
with evolutionary algorithms requires balancing constraint satisfaction and optimization …
A cooperative multistep mutation strategy for multiobjective optimization problems with deceptive constraints
Constrained multiobjective optimization problems with deceptive constraints (DCMOPs) are
a kind of complex optimization problems and have received some attention. For DCMOPs …
a kind of complex optimization problems and have received some attention. For DCMOPs …
A strengthened constrained-dominance based evolutionary algorithm for constrained many-objective optimization
W Zhang, J Liu, J Liu, Y Liu, S Tan - Applied Soft Computing, 2024 - Elsevier
Solving constrained multi-objective optimization problems have received increasing
attention. However, there are few researches based on constrained many-objective …
attention. However, there are few researches based on constrained many-objective …
Membrane computing for IoT task offloading: An efficient multi-objective constrained optimization framework
S Tuo, Y Huyan, T Fan, Y Zhao - Applied Soft Computing, 2025 - Elsevier
The computational tasks associated with Internet of Things (IoT) applications have become
increasingly complex, with IoT devices (IoTDs) now being utilized in a multitude of contexts …
increasingly complex, with IoT devices (IoTDs) now being utilized in a multitude of contexts …
Hierarchical optimization by spatial-temporal indictor in multi-scale decision pyramid for constrained large-scale multi-objective problems
Q Wang, Y Xi, Q Zhang, T Li, B Li - Expert Systems with Applications, 2024 - Elsevier
Most existing constrained multi-objective evolutionary algorithms (CMOEAs) are not so
efficient when handling constrained large-scale multi-objective problems (CLSMOPs). To …
efficient when handling constrained large-scale multi-objective problems (CLSMOPs). To …
Spectral-energy efficiency tradeoff of massive MIMO by a constrained large-scale multi-objective algorithm through decision transfer
Q Wang, T Li - Complex & Intelligent Systems, 2025 - Springer
To better balance the spectral efficiency (SE) and energy efficiency (EE) in the massive
multiple-input multiple output system with a large number of users (MaMIMO-LU), the SE-EE …
multiple-input multiple output system with a large number of users (MaMIMO-LU), the SE-EE …
An Efficient Approach for Solving Expensive Constrained Multiobjective Optimization Problems
KH Rahi - arXiv preprint arXiv:2405.13298, 2024 - arxiv.org
To solve real-world expensive constrained multi-objective optimization problems (ECMOPs),
surrogate/approximation models are commonly incorporated in evolutionary algorithms to …
surrogate/approximation models are commonly incorporated in evolutionary algorithms to …
Constrained Sampling-Based Evolutionary Neural Architecture Search for GANs
In recent years, many researchers have adopted neural architecture search (NAS)
techniques to automatically design generative adversarial networks (GANs). However, due …
techniques to automatically design generative adversarial networks (GANs). However, due …