Evolutionary constrained multiobjective optimization: Scalable high-dimensional constraint benchmarks and algorithm
Evolutionary constrained multiobjective optimization has received extensive attention and
research in the past two decades, and a lot of benchmarks have been proposed to test the …
research in the past two decades, and a lot of benchmarks have been proposed to test the …
Learning-aided evolutionary search and selection for scaling-up constrained multiobjective optimization
The existing constrained multiobjective evolutionary algorithms (CMOEAs) still have great
room for improvement in balancing populations convergence, diversity and feasibility on …
room for improvement in balancing populations convergence, diversity and feasibility on …
Constrained multi-objective optimization problems: Methodologies, algorithms and applications
Constrained multi-objective optimization problems (CMOPs) are widespread in practical
applications such as engineering design, resource allocation, and scheduling optimization …
applications such as engineering design, resource allocation, and scheduling optimization …
Methods to balance the exploration and exploitation in differential evolution from different scales: A survey
Y Zhang, G Chen, L Cheng, Q Wang, Q Li - Neurocomputing, 2023 - Elsevier
Inspired by the evolutionary process in nature, Differential Evolution (DE) has been widely
concerned and used as a numerical global optimizer for decades of years, since its …
concerned and used as a numerical global optimizer for decades of years, since its …
A multi-preference-based constrained multi-objective optimization algorithm
X Feng, Z Ren, A Pan, J Hong, Y Tong - Swarm and Evolutionary …, 2023 - Elsevier
When tackling constrained multi-objective optimization problem, evolutionary algorithms
grapple with the simultaneous need to optimize the conflict objectives and satisfy …
grapple with the simultaneous need to optimize the conflict objectives and satisfy …
A many-objective evolutionary algorithm based on learning assessment and mapping guidance of historical superior information
J Xiong, G Liu, Z Gao, C Zhou, P Hu… - … of Computational Design …, 2024 - academic.oup.com
Multi-objective optimization algorithms have shown effectiveness on problems with two or
three objectives. As the number of objectives increases, the proportion of non-dominated …
three objectives. As the number of objectives increases, the proportion of non-dominated …
Benchmark problems for large-scale constrained multi-objective optimization with baseline results
The interests in evolutionary constrained multiobjective optimization are rapidly increasing
during the past two decades. However, most related studies are limited to small-scale …
during the past two decades. However, most related studies are limited to small-scale …
Constrained multi-objective optimization evolutionary algorithm for real-world continuous mechanical design problems
During the past two decades, evolutionary algorithms have seen great achievements in
solving complex optimization problems owing to the advantages brought by their properties …
solving complex optimization problems owing to the advantages brought by their properties …
Deep reinforcement learning assisted automated guiding vector selection for large-scale sparse multi-objective optimization
Sparse multi-objective optimization problems (SMOPs) are prevalent in a wide range of
applications, spanning from the fields of science to engineering. Existing sparse …
applications, spanning from the fields of science to engineering. Existing sparse …
Reinforcement learning-based differential evolution algorithm for constrained multi-objective optimization problems
X Yu, P Xu, F Wang, X Wang - Engineering Applications of Artificial …, 2024 - Elsevier
Many real-world problems can be established as Constrained Multi-objective Optimization
Problems (CMOPs). It is still challenging to automatically set efficient parameters for …
Problems (CMOPs). It is still challenging to automatically set efficient parameters for …