Stable Matching-Based Selection in Evolutionary Multiobjective Optimization
By Ke Li, Qingfu Zhang, Sam Kwong, Miqing Li, Ran Wang. IEEE Transactions on Evolutionary Computation, 2014
351 citations recorded by the source corpus.
Subfields: Artificial Intelligence, Computational Theory and Mathematics
View Paper: https://www.semanticscholar.org/paper/408e9c29d3271c936876f1e3ef94e19d388620df
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Artificial Intelligence
In Computational Theory and Mathematics
Citations in the corpus
- Balancing Convergence and Diversity in Decomposition-Based Many-Objective Optimizers (2016, methodology): “The other three compared MOEA/D variants are MOEA/D-STM [46], MOEA/D-GR [49], and I-DBEA [48], since they are somewhat similar to MOEA/D-DU.”
- IGD Indicator-Based Evolutionary Algorithm for Many-Objective Optimization Problems (2018, background): “For example, it is difficult to choose the converting strategy of the MaOEAs from the second category, which motivates multiple variants [15], [17], [19], [20], [41], [42] to be developed further.”
- A Decision Variable Clustering-Based Evolutionary Algorithm for Large-Scale Many-Objective Optimization (2018, methodology): “approach of this type is the MOEA/D algorithm [30], as well as its variants, such as stable matching model based MOEA/D [31], MOEA/D with adaptive weight vector adjust-”
- A New Two-Stage Evolutionary Algorithm for Many-Objective Optimization (2019, neutral): “The MOEA based on decomposition (MOEA/D) [13] and its numerous variants, such as [14]–[16], belong to this category.”
- An Evolutionary Many-Objective Optimization Algorithm Based on Dominance and Decomposition (2015, methodology): “, introducing adaptive mechanism in reproduction [42], hybridizing with local search [43], and incorporating stable matching in selection [44].”
- Data-Driven Surrogate-Assisted Multiobjective Evolutionary Optimization of a Trauma System (2016, methodology): “based MOEAs, also known as decomposition-based methods, optimize a set of single-objective subproblems with a preassigned weight set [16], [17]; and indicator-based MOEAs”
- Localized Weighted Sum Method for Many-Objective Optimization (2018, neutral): “, [17], [25], and [49]).”