[HTML][HTML] Large-scale evolutionary optimization: A review and comparative study
Large-scale global optimization (LSGO) problems have widely appeared in various real-
world applications. However, their inherent complexity, coupled with the curse of …
world applications. However, their inherent complexity, coupled with the curse of …
Learning-aided evolution for optimization
Learning and optimization are the two essential abilities of human beings for problem
solving. Similarly, computer scientists have made great efforts to design artificial neural …
solving. Similarly, computer scientists have made great efforts to design artificial neural …
Cooperative coevolutionary surrogate ensemble-assisted differential evolution with efficient dual differential grouping for large-scale expensive optimization problems
This paper proposes a novel algorithm named surrogate ensemble assisted differential
evolution with efficient dual differential grouping (SEADECC-EDDG) to deal with large-scale …
evolution with efficient dual differential grouping (SEADECC-EDDG) to deal with large-scale …
Evolutionary multitasking via reinforcement learning
Different from traditional evolutionary algorithms (EAs), the multifactorial evolutionary
algorithm (MFEA) is proposed to optimize multiple optimization tasks concurrently. Through …
algorithm (MFEA) is proposed to optimize multiple optimization tasks concurrently. Through …
Evolutionary Multitasking With Centralized Learning for Large-Scale Combinatorial Multi-Objective Optimization
Y Huang, W Zhou, Y Wang, M Li… - IEEE Transactions on …, 2023 - ieeexplore.ieee.org
Evolutionary multitasking (EMT) has attracted much attention in the community of
evolutionary computation recently. It intends to improve the performance of evolutionary …
evolutionary computation recently. It intends to improve the performance of evolutionary …
Evolutionary multitasking for solving nonlinear equation systems
Over the past few years, many evolutionary algorithms have been developed to find multiple
roots of the nonlinear equation system (NES). However, they can only solve one NES in a …
roots of the nonlinear equation system (NES). However, they can only solve one NES in a …
Evolutionary Large-Scale Multiobjective Optimization via Autoencoder-Based Problem Transformation
Addressing the challenge of efficiently handling high-dimensional search spaces in solving
large-scale multiobjective optimization problems (LMOPs) becomes an emerging research …
large-scale multiobjective optimization problems (LMOPs) becomes an emerging research …
Dual-drive collaboration surrogate-assisted evolutionary algorithm by coupling feature reduction and reconstruction
H Yu, Y Gong, L Kang, C Sun, J Zeng - Complex & Intelligent Systems, 2024 - Springer
Surrogate-assisted evolutionary algorithm (SAEA) prevails in the optimization of
computationally expensive problems. However, existing SAEAs confront low efficiency in the …
computationally expensive problems. However, existing SAEAs confront low efficiency in the …
A Surrogate-assisted Evolutionary Algorithm with Dual Restricted Boltzmann Machines and Reinforcement Learning-based Adaptive Strategy Selection
Y Gong, H Yu, L Kang, G Qiao, D Guo, J Zeng - Swarm and Evolutionary …, 2024 - Elsevier
To improve the effectiveness of surrogate-assisted evolutionary algorithms (SAEAs) in
solving high-dimensional expensive optimization problems with multi-polar and multi …
solving high-dimensional expensive optimization problems with multi-polar and multi …
Many-Task Differential Evolutionary Algorithm Based on Bi-Space Similarity
Y Hou, Y Shen, H Han, J Wang - IEEE Transactions on …, 2024 - ieeexplore.ieee.org
Many-task differential evolutionary algorithm is an effective way to optimize multiple tasks
simultaneously. The optimization performance of the algorithm decreases due to the …
simultaneously. The optimization performance of the algorithm decreases due to the …