A Decision Variable Clustering-Based Evolutionary Algorithm for Large-Scale Many-Objective Optimization
By Xing-yi Zhang, Ye Tian, Ran Cheng, Yaochu Jin. IEEE Transactions on Evolutionary Computation, 2018
553 citations recorded by the source corpus.
Subfields: Artificial Intelligence, Computational Theory and Mathematics, Management Science and Operations Research
View Paper: https://www.semanticscholar.org/paper/94a0aab59e35a969b3f9aeb1f990f8f68130e29a
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Citations in the corpus
- An Indicator-Based Multiobjective Evolutionary Algorithm With Reference Point Adaptation for Better Versatility (2018, methodology): “However, further investigation of applying IGD-NS in other MOEAs is still desirable, especially on problems with a large number of decision variables, namely, largescale MOPs or MaOPs [49], [63].”
- A Framework for Large-Scale Multiobjective Optimization Based on Problem Transformation (2018, methodology): “Two more very recent approaches to multiobjective LSO are introduced in [12] and [29].”
- PlatEMO: A MATLAB Platform for Evolutionary Multi-Objective Optimization [Educational Forum] (2017, unknown): “It is also worth noting that, since the efficiency of most MOEAs is subject to the non-dominated sorting process, PlatEMO employs the efficient non-dominated sort ENS-SS [98] for two-objective optimization and the tree-based ENS termed T-ENS [56] for optimization with more than two objectives as the n”
- An Evolutionary Algorithm for Large-Scale Sparse Multiobjective Optimization Problems (2020, methodology): “Third, since SparseEA evolves the population without considering the interactions between decision variables, it can be enhanced by adopting other strategies like decision variable clustering [30] and Bayesian optimization [36].”
- Efficient Large-Scale Multiobjective Optimization Based on a Competitive Swarm Optimizer (2020, methodology): “The number of samples (i.e., training data) and the number of features TABLE IV HV VALUES OF WOF-NSGA-II, LMEA, MMOPSO, IM-MOEA, AND LMOCSO FOR TRAINING NEURAL NETWORK, WHERE THE BEST RESULT ON EACH TEST INSTANCE IS SHOWN IN A GRAY BACKGROUND (i.e., inputs) of each dataset are also presented in the ”
- A Strengthened Dominance Relation Considering Convergence and Diversity for Evolutionary Many-Objective Optimization (2018, background): “The Pareto dominance relation is widely adopted in MOEAs for distinguishing the quality of candidate solutions [22], [23].”