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Efficient sparse large-scale multiobjective optimization based on cross-scale knowledge fusion
Due to the curse of dimensionality and the unknown sparsity of search spaces, evolutionary
algorithms face immense challenges in approximating optimal solutions for widely studied …
algorithms face immense challenges in approximating optimal solutions for widely studied …
Solving sparse multi-objective optimization problems via dynamic adaptive grou** and reward-penalty sparse strategies
Abstract Sparse Multi-Objective Optimization Problems (SMOPs) are commonly encountered
in various fields such as machine learning, signal processing, and data mining. While …
in various fields such as machine learning, signal processing, and data mining. While …
Improving two-layer encoding of evolutionary algorithms for sparse large-scale multiobjective optimization problems
J Jiang, H Wang, J Hong, Z Liu, F Han - Complex & Intelligent Systems, 2024 - Springer
Sparse large-scale multiobjective problems (LSMOPs) are characterized as an NP-hard
issue that undergoes a significant presence of zero-valued variables in Pareto optimal …
issue that undergoes a significant presence of zero-valued variables in Pareto optimal …
A Pareto Optimal Service Selection Approach for Cloud Manufacturing
X Zheng, J Li, M Zhu, J Dai - 2024 - researchsquare.com
The cloud manufacturing model plays a crucial role in the deep integration of information
technology and the manufacturing industry, which dynamically adjusts manufacturing …
technology and the manufacturing industry, which dynamically adjusts manufacturing …