Decomposition of a Multiobjective Optimization Problem Into a Number of Simple Multiobjective Subproblems

By Hai-Lin Liu, Fangqing Gu, Qingfu Zhang. IEEE Transactions on Evolutionary Computation, 2014

726 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Computational Theory and Mathematics, Control and Systems Engineering

View Paper: https://www.semanticscholar.org/paper/b29fd4dec681de038a2bce2b631a73acbbc70315

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Citations in the corpus

A Multiobjective Evolutionary Algorithm Using Gaussian Process-Based Inverse Modeling (2015, methodology): “The reference vectors are generated using the method suggested in [56].”
A Reference Vector Guided Evolutionary Algorithm for Many-Objective Optimization (2016, methodology): “For instance, MOEA/D-M2M [29], [38] divides the whole PF into a group of segments, and each segment can be regarded as a sub-problem.”
Localized Weighted Sum Method for Many-Objective Optimization (2018, background): “1Note that in some variants of MOEA/D sub-problems are multiobjective [31].”
A Vector Angle-Based Evolutionary Algorithm for Unconstrained Many-Objective Optimization (2017, methodology): “I. INTRODUCTION MULTIOBJECTIVE problems (MOPs) with at least fourconflicting objectives are informally known as manyobjective optimization problems (MaOPs) [1], [2].”
An Evolutionary Many-Objective Optimization Algorithm Based on Dominance and Decomposition (2015, methodology): “In the future, it is interesting to investigate the performance of MOEA/DD for a wider range of problems, such as problems with complicated PS shapes (see [40], [72], [73]), combinatorial optimization problems (see [18], [46], [74]), and problems in real-world with a large number of objectives.”
An Indicator-Based Multiobjective Evolutionary Algorithm With Reference Point Adaptation for Better Versatility (2018, methodology): “On the other hand, some other decompositionbased MOEAs such as MOEA/D with decomposing an MOP into several simple MOPs [16], MOEA based on inverse”
A Multiobjective Evolutionary Algorithm Based on Decision Variable Analyses for Multiobjective Optimization Problems With Large-Scale Variables (2016, methodology): “For continuous ZDT, DTLZ, UF, and MOP [36] problems, the number of position variable(s) and mix variable(s) is m−1, while the number of distance variable(s) is n−m+ 1.”
A Survey of Multiobjective Evolutionary Algorithms Based on Decomposition (2017, methodology): “4) The studies MOEA/D-M2M [70], NSGA-III [40], MOEA/DD [39], and RVEA [41] provide new direction”
Balancing Convergence and Diversity in Decomposition-Based Many-Objective Optimizers (2016, background): “a major framework to design MOEAs, MOEA/D has spawned a large amount of research work (see [44]–[47]), but almost all the newly proposed MOEA/D variants are yet to be vali-”
A Strength Pareto Evolutionary Algorithm Based on Reference Direction for Multiobjective and Many-Objective Optimization (2017, methodology): “a framework similar to MOEA/D-M2M [34], it will be interesting to make a comparison between them.”
An improved epsilon constraint-handling method in MOEA/D for CMOPs with large infeasible regions (2017, methodology): “The second type is decomposition-based approaches, and typical examples include MOEA/D (Zhang and Li (2007)), MOEA/D-DE (Li and Zhang (2009)), EAG-MOEA/D (Cai et al (2015)), MOEA/D-M2M (Liu et al (2014)), MOEA/D-SAS (Cai et al (2016)) and so on.”
A Decision Variable Clustering-Based Evolutionary Algorithm for Large-Scale Many-Objective Optimization (2018, background): “Some representative MOEAs of this type are MOEA/D-M2M [36], reference-point based many-objective NSGA-II (NSGA-III) [37], dominance and decomposition based MOEA [38], and the recently proposed reference vector guided evolutionary algorithm [39].”
Pareto Multi-Task Learning (2019, methodology): “We adopt the idea from [29] and decompose the MTL into K subproblems with a set of well-distributed unit preference vectors { u 1 , u 2 , ..., u K } in R m + .”
Two-Archive Evolutionary Algorithm for Constrained Multiobjective Optimization (2017, methodology): “To facilitate the density estimation, we borrow the idea from MOEA/D-M2M [35] to divide the objective space into N subregions, each of which is represented by a unique weight vector on the canonical simplex.”