MOEA/D: A Multiobjective Evolutionary Algorithm Based on Decomposition

By Qingfu Zhang, Hui Li. IEEE Transactions on Evolutionary Computation, 2007

8,984 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Computational Theory and Mathematics

View Paper: https://www.semanticscholar.org/paper/b76be6707fa5858cc5378bc11f10ec6f6a97d85c

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Citations in the corpus

A Multiobjective Evolutionary Algorithm Using Gaussian Process-Based Inverse Modeling (2015, background): “One most recent variant of weighted aggregation algorithms, termed multiobjective e volutionary algorithm based on decomposition (MOEA/D) [7] has received increasing attention [8]–[10] due to its computat ional simplicity and attractive search performance.”
A Decision Variable Clustering-Based Evolutionary Algorithm for Large-Scale Many-Objective Optimization (2018, methodology): “In this section, we verify the performance of LMEA in solving large-scale MaOPs by empirically comparing it with four state-of-the-art MOEAs, namely, MOEA/D [30], NSGA-III [37], KnEA [22], and MOEA/DVA [64].”
A data-driven surrogate-assisted evolutionary algorithm applied to a many-objective blast furnace optimization problem (2017, methodology): “In contrast to other many-objective evolutionary algorithms, e.g., NSGA-III [19] and MOEA/D [20], which use a set of reference points and weights, RVEA adopts a set of reference vectors.”
A Decomposition-Based Evolutionary Algorithm for Many Objective Optimization (2015, methodology): “To assess the performance, we have selected inverted generational distance (IGD) [16], [42] and hypervolume (HV) [43] as the performance metric.”
Adaptive Operator Selection With Bandits for a Multiobjective Evolutionary Algorithm Based on Decomposition (2014, background): “MOEA/D [6] decomposes an MOP into a number of singleobjective subproblems.”
An Evolutionary Algorithm for Large-Scale Sparse Multiobjective Optimization Problems (2020, positive): “For the above sparse MOPs, most work solves them via existing MOEAs like NSGA-II [23], SMS-EMOA [25], and MOEA/D [46], and some work also enhances the performance by adopting customized search strategies.”
A Random Forest-Assisted Evolutionary Algorithm for Data-Driven Constrained Multiobjective Combinatorial Optimization of Trauma Systems (2020, background): “1) Generic MOEAs: NSGA-II [60] and MOEA/D [61] are two popular MOEAs without using any domain knowledge or local search strategy.”
A Survey of Decomposition Methods for Multi-objective Optimization (2014, methodology): “In multi-objective decomposition the first implementation found is from Li and Zhang [27], an evolution of the first MOEA/D in Zhang and Li [14], called MOEA/D-DE.”
Multiobjective firefly algorithm for continuous optimization (2012, background): “• LZ function [20, 36] where J 1 = { j | j is odd } and J 2 = { j | j is even } where 2 ≤ j ≤ d .”
A Knee Point-Driven Evolutionary Algorithm for Many-Objective Optimization (2015, methodology): “, NSGA-II [6], SPEA2 [7], IBEA [8], MOEA/D [9], PESA-II [10], and M-PAES [11], just to name a few.”
A Strengthened Dominance Relation Considering Convergence and Diversity for Evolutionary Many-Objective Optimization (2018, background): “In MOEA/DD, the size of neighborhood T is set to 0.1N , the neighborhood selection probability δ is set to 0.9, and the maximum number of solutions replaced by each offspring nr is set to 0.01N , with N denoting the population size.”
Recent Advances in Bayesian Optimization (2022, background): “By contrast, an MOP can be decomposed into multiple single-objective sub-problems, as done in the multiobjective evolutionary algorithm based on decomposition (MOEA/D) [209] and the reference vector guided evolutionary algorithm (RVEA) [25].”
Optimal Resource Allocation in Coordinated Multi-Cell Systems (2013, methodology): “The different methods can be categorized as follows [38, 324]:”
Objective Reduction in Many-Objective Optimization: Linear and Nonlinear Algorithms (2013, background): “The more recent MOEA/D [8], [9] and indicator based MOEAs [10], [11] are found to cope better with an increase in M.”
Recent advances in differential evolution - An updated survey (2016, background): “single-objective problems [230] by a linear or non linear weighted”
From evolutionary computation to the evolution of things (2015, background): “Having proven highly successful for finding solutions to problems with multiple objectives (typically up to ten) 76 , the community is now making rapid advances in the field of many objectives – moving way beyond the capabilities of other algorithms 77,78,79 .”
A survey on multi-objective evolutionary algorithms for many-objective problems (2014, methodology): “Zhang and Li [104] proposed the Multi-objective Evolutionary Algorithm based on Decomposition (MOEA/D) which, similarly to an approach previously presented in [77], is a decomposition based method that uses a scalarization function with a different weight vector for each individual.”
Fuzzy-Based Pareto Optimality for Many-Objective Evolutionary Algorithms (2014, methodology): “00 (to be consistent with what were used in NSGA-II [8] and MOEA/D [19]), while the mutation rate is 1/m and m is the number of decision variables.”