Balancing Convergence and Diversity in Decomposition-Based Many-Objective Optimizers
By Yuan Yuan, Hua Xu, Bo D. Wang, Bo Zhang, Xin Yao. IEEE Transactions on Evolutionary Computation, 2016
380 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/d33fcdc06b180be15c33f5323ebc08fe69ffbca3
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Citations in the corpus
- A Strength Pareto Evolutionary Algorithm Based on Reference Direction for Multiobjective and Many-Objective Optimization (2017, methodology): “, NSGA-III [13], MOEA based on dominance and decomposition [31] and MOEA/D with a distance-based updating strategy [46], to maintain population diversity or control convergence for manyobjective optimization.”
- Improved Regularity Model-Based EDA for Many-Objective Optimization (2018, methodology): “Noted here that, this approach to calculate the ideal point is also utilized in [55]–[59].”
- 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.”
- Multiobjective Deep Belief Networks Ensemble for Remaining Useful Life Estimation in Prognostics (2017, methodology): “As a result, MOEAs [45]–[50] can provide a solution.”
- Performance of Decomposition-Based Many-Objective Algorithms Strongly Depends on Pareto Front Shapes (2017, neutral): “, improved decomposition-based evolutionary algorithm (I-DBEA) [27], MOEA/D-distance-based updating strategy (MOEA/D-DU) [28], Ensemble Fitness Ranking with a Ranking Restriction scheme (EFR-RR) [28], and θ DEA [29]).”
- IGD Indicator-Based Evolutionary Algorithm for Many-Objective Optimization Problems (2018, background): “First, it has been reported in [11], [15], [70], and [71] that the HV result is largely affected by the nadir points of the problem to be optimized.”
- A Steady-State and Generational Evolutionary Algorithm for Dynamic Multiobjective Optimization (2017, methodology): “In this paper, the modified version of the weighted Tchebycheff approach used in [49] is adopted as the aggregation function for MOEA/D because it has been recently proved to provide better distribution than its original version.”
- A Decision Variable Clustering-Based Evolutionary Algorithm for Large-Scale Many-Objective Optimization (2018, neutral): “ment [32], external archive guided MOEA/D [33], MOEA/D with a distance-based updating [34], and online diversity metric based MOEA/D [35].”
- PlatEMO: A MATLAB Platform for Evolutionary Multi-Objective Optimization [Educational Forum] (2017, methodology): “-III [40] 2014 Non-dominated sorting genetic algorithm III A-NSGA-III [41] 2014 Adaptive NSGA-III SPEA2+SDE [42] 2014 SPEA2 with shift-based density estimation BiGE [43] 2015 Bi-goal evolution EFR-RR [20] 2015 Ensemble fitness ranking with ranking restriction I-DBEA [44] 2015 Improved decomposition b”
- A Surrogate-Assisted Reference Vector Guided Evolutionary Algorithm for Computationally Expensive Many-Objective Optimization (2018, background): “It has been found in [49] that choosing a reference point slightly better than the nadir point is able to strike a balance between”
- Localized Weighted Sum Method for Many-Objective Optimization (2018, neutral): “based on decomposition (MOEA/D) [17] and its variants MOEA/D-DD [18], MOEA/D-DU [19], NSGA-III [20], and θ -DEA [21].”