An Efficient Approach to Nondominated Sorting for Evolutionary Multiobjective Optimization
By Xing-yi Zhang, Ye Tian, Ran Cheng, Yaochu Jin. IEEE Transactions on Evolutionary Computation, 2015
431 citations recorded by the source corpus.
Subfields: Artificial Intelligence, Computational Theory and Mathematics
View Paper: https://www.semanticscholar.org/paper/bfd2abf65fa040d59a3ccf6336b35a89500f02ae
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In Artificial Intelligence
In Computational Theory and Mathematics
Citations in the corpus
- Multiobjective Deep Belief Networks Ensemble for Remaining Useful Life Estimation in Prognostics (2017, background): “In MOEAs, the superiority of a candidate solution is evaluated in terms of multiple objectives.”
- A Knee Point-Driven Evolutionary Algorithm for Many-Objective Optimization (2015, methodology): “nated sorting using the efficient nondominated sorting (ENS) algorithm reported in [51], forming NF nondominated fronts, Fi, 1 ≤ i ≤ NF .”
- A Reference Vector Guided Evolutionary Algorithm for Many-Objective Optimization (2016, methodology): “In order to reduce the time cost of nondominated sorting, the efficient nondominated sorting approach ENS-SS reported in [78] has been adopted in NSGA-III, GrEA, and KnEA, and a steady-state nondominated sorting approach as reported in [79] has been adopted in MOEA/DD.”
- Test Problems for Large-Scale Multiobjective and Many-Objective Optimization (2017, methodology): “The recently proposed ENS approach is adopted in IM-MOEA, NSGA-II, and NSGA-III to improve the efficiency of nondominated sorting [64].”
- A Decision Variable Clustering-Based Evolutionary Algorithm for Large-Scale Many-Objective Optimization (2018, methodology): “To reduce the computational cost of LMEA for solving large-scale MaOPs, we present a computational efficient treebased nondominated sorting approach, termed T-ENS, on the basis of the ENS recently proposed in [54].”
- Balancing Objective Optimization and Constraint Satisfaction in Constrained Evolutionary Multiobjective Optimization (2021, background): “gence by nondominated sorting [3] and maintain the population diversity by various selection strategies [4].”
- An Indicator-Based Multiobjective Evolutionary Algorithm With Reference Point Adaptation for Better Versatility (2018, methodology): “Before the IGD-NS indicator is used for candidate solution selection, the combined population is first sorted by the efficient nondominated sort (ENS) [48] for MOPs and tree-based ENS [49] for MaOPs.”
- 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].”
- Multifactorial Evolution: Toward Evolutionary Multitasking (2016, methodology): “From the principles of nondominated sorting used in MOEAs [16], [17], it follows that individuals {p2, p3, p4, p5} belong to the first nondominated front while {p1, p6} belong to the second nondominated front.”
- Multi-Objective Evolutionary Federated Learning (2018, background): “Note also that computationally more efficient non-dominated sorting algorithms can be used when the population size is large [26] or when the number of objectives is large [27].”
- A Classification-Based Surrogate-Assisted Evolutionary Algorithm for Expensive Many-Objective Optimization (2019, background): “In multi-objective optimization, a basic criterion for comparing two solutions is the Pareto dominance relationship [7], [63], [64], which categorizes solutions into dominated solutions and non-dominated solutions.”