A Framework for Large-Scale Multiobjective Optimization Based on Problem Transformation

By Heiner Zille, Hisao Ishibuchi, Sanaz Mostaghim, Yusuke Nojima. IEEE Transactions on Evolutionary Computation, 2018

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Bio-inspired computation: Where we stand and what's next (2019, background): “Although other large-scale multi-objective optimization techniques have been proposed since then [144, 145, 146], several topics remain to be explored in this area.”
An Evolutionary Algorithm for Large-Scale Sparse Multiobjective Optimization Problems (2020, methodology): “1) Algorithms: Four MOEAs are compared with SparseEA on SMOP1–SMOP8, i.e., NSGA-II [23], CMOPSO [63], MOEA/D-DRA [71], and WOF-NSGA-II [31], where NSGA-II is a genetic algorithm, CMOPSO is a particle swarm optimization algorithm, MOEA/D-DRA is a differential evolution algorithm, and WOF-NSGA-II is…”
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 ”