A Reference Vector Guided Evolutionary Algorithm for Many-Objective Optimization

By Ran Cheng, Yaochu Jin, Markus Olhofer, Bernhard Sendhoff. IEEE Transactions on Evolutionary Computation, 2016

1,488 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/206526c355fe5e486b62abe10dc8fd65e7796953

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Citations in the corpus

A Surrogate-Assisted Reference Vector Guided Evolutionary Algorithm for Computationally Expensive Many-Objective Optimization (2018, methodology): “It has been shown [8] that APD is better scalable to the increase in the number of objectives in maintaining a balance between convergence and diversity.”
Localized Weighted Sum Method for Many-Objective Optimization (2018, background): “In addition, effectiveness of the cone-based neighborhood has been reported in [76] and [77].”
Two-Archive Evolutionary Algorithm for Constrained Multiobjective Optimization (2017, methodology): “Borrowing the similar idea, several MOEA/D variants [15]–[17] use the CV as an alternative criterion in the subproblem update procedure.”
PlatEMO: A MATLAB Platform for Evolutionary Multi-Objective Optimization [Educational Forum] (2017, background): “RvEA* [56] 2016 RvEA EmbEddEd wiTh ThE REfEREnCE vECToR REgEnERATion STRATEgY”
Benchmark Problems and Performance Indicators for Search of Knee Points in Multiobjective Optimization (2020, methodology): “NSGA-II [3] and RVEA [18] are chosen as the basic optimizer whilst KneeWD [37] with δ = 0.”
Data-Driven Evolutionary Optimization: An Overview and Case Studies (2019, methodology): “Then, Kriging models are built to assist the reference vector guided EA (RVEA) [39].”
A Classification-Based Surrogate-Assisted Evolutionary Algorithm for Expensive Many-Objective Optimization (2019, methodology): “The performance deterioration can be largely attributed to the fact that the rate of nondominated solutions in a limited population increases exponentially as the number of objectives increases, making traditional Pareto-based MOEAs fail to distinguish these solutions [12], [13].”
A Method for a Posteriori Identification of Knee Points Based on Solution Density (2018, background): “MOEAs can be roughly classified into four categories, namely, dominancebased methods [1]–[4], indicators or metrics based approaches [5]–[7], aggregation or decomposition based methods [8]–[11], as well as preference-based methods [12]–[15].”
A Survey of Multiobjective Evolutionary Algorithms Based on Decomposition (2017, background): “4) The studies MOEA/D-M2M [70], NSGA-III [40], MOEA/DD [39], and RVEA [41] provide new direction”
Multi-Objective Evolutionary Federated Learning (2018, methodology): “, the evolutionary many-objective optimization algorithm using referencepoint based non-dominated sorting approach [23], the kneedriven evolutionary algorithm for many-objective optimization [24], and the reference vector guided evolutionary for manyobjective optimization [25].”
Efficient Large-Scale Multiobjective Optimization Based on a Competitive Swarm Optimizer (2020, methodology): “LMOCSO adopts the environmental selection strategy of RVEA [54] to select the particles for the next generation from P ∪ P′.”
A benchmark test suite for evolutionary many-objective optimization (2017, methodology): “This may be preferred by decomposition-based algorithms which often use a set of uniformly distributed weight vectors in a simplex to guide the search [7,18].”
A data-driven surrogate-assisted evolutionary algorithm applied to a many-objective blast furnace optimization problem (2017, methodology): “For a detailed description of RVEA, see [18].”
A Decision Variable Clustering-Based Evolutionary Algorithm for Large-Scale Many-Objective Optimization (2018, methodology): “This is attributed to the fact that MOEA/DVA adopts MOEA/D for optimizing the diversity-related variables, while such decomposition-based MOEAs are not well suited for solving MOPs or MaOPs with irregular (disconnected or degenerate) Pareto optimal fronts [37], [39], [82].”
A Strengthened Dominance Relation Considering Convergence and Diversity for Evolutionary Many-Objective Optimization (2018, methodology): “Accordingly, the Das and Dennis’s approach with two layers [42] is used to generate the reference vectors used in θ -dominance, NSGA-III, MOEA/DD, RVEA, and MOMBIII, where the parameters (p1, p2) controlling the numbers of reference vectors along the outer and inner layers are set to (6, 0), (3, 2),”
Recent Advances in Bayesian Optimization (2022, background): “RVEA is also adopted as the optimizer in [252] to address expensive MOPs, where the predicted objective value and the uncertainty are weighted together as an acquisition function, and the weights are tuned to balance exploration and exploitation.”
A New Two-Stage Evolutionary Algorithm for Many-Objective Optimization (2019, background): “, MaOEDA-IR) [11], and the reference vector guided EA (RVEA) [12].”
Evolutionary Constrained Multiobjective Optimization: Test Suite Construction and Performance Comparisons (2019, result): “Some recent studies [33]–[35] have reported highly competi-”
A Vector Angle-Based Evolutionary Algorithm for Unconstrained Many-Objective Optimization (2017, background): “A number of many-objective evolutionary algorithms (MaOEAs) have been proposed to deal with optimization problems having more than three objectives [7]–[15].”