The performance of a new version of MOEA/D on CEC09 unconstrained MOP test instances

By Qingfu Zhang, Wudong Liu, Hui Li. IEEE Congress on Evolutionary Computation, 2009

621 citations recorded by the source corpus.

Subfields: Computational Theory and Mathematics, Hardware and Architecture

View Paper: https://www.semanticscholar.org/paper/40c9c84c8de45d4953e22745f9291240392524ff

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Citations in the corpus

Recent advances in differential evolution - An updated survey (2016, background): “neighborhood size parameter in the MOEA/D-DE [231] and”
A Multiobjective Evolutionary Algorithm Based on Decision Variable Analyses for Multiobjective Optimization Problems With Large-Scale Variables (2016, methodology): “Their mathematical descriptions and the ideal PFs can be found in [34], [35], and [44].”
Stable Matching-Based Selection in Evolutionary Multiobjective Optimization (2014, methodology): “The parameters of NSGA-II, MSOPS-II, HypE, MOEA/DDE, and MOEA/D-DRA are set according to their corresponding references [4], [9], [13], [14], [35], respectively.”
A survey on multi-objective evolutionary algorithms for many-objective problems (2014, methodology): “Other test suites used for scalability analysis are the WFG toolkit [39], the CEC 2009 competition problems [105], and a set of problems based on a framework for continuous many-objective test problems with arbitrarily prescribed Pareto set shapes describedin[91];thelasttwoinonlyonework.”
Borg: An Auto-Adaptive Many-Objective Evolutionary Computing Framework (2013, methodology): “MOEA/D (Zhang et al., 2009a) is a recentlyintroduced MOEA that uses aggregate functions, but attempts to avoid such pitfalls by simultaneously solving many single-objective Chebyshev decompositions of the manyobjective problem in a single run.”
Adaptive Operator Selection With Bandits for a Multiobjective Evolutionary Algorithm Based on Decomposition (2014, methodology): “The FRRMAB AOS method is used within an multiobjective evolutionary algorithm based on a decomposition (MOEA/D) variant, MOEA/D-DRA [27], which was the winning algorithm in the CEC 2009 MOEA contest.”
A Survey of Decomposition Methods for Multi-objective Optimization (2014, methodology): “It was the indicator used in the IEEE CEC 09 competition, where the winner for unconstrained problems was MOEA/D [20]; this version of MOEA/D uses a utility function that assigns less computational effort when the weight vectors are not improved in the generation.”
An Evolutionary Algorithm for Large-Scale Sparse Multiobjective Optimization Problems (2020, methodology): “1) Algorithms: Four MOEAs are compared with SparseEA on SMOP1–SMOP8, i.e., NSGA-II [23], CMOPSO [63], MOEA/D-DRA [71], and WOF-NSGA-II [31], where NSGA-II is a genetic algorithm, CMOPSO is a particle swarm optimization algorithm, MOEA/D-DRA is a differential evolution algorithm, and WOF-NSGA-II is…”
A Strength Pareto Evolutionary Algorithm Based on Reference Direction for Multiobjective and Many-Objective Optimization (2017, background): “Since its introduction, MOEA/D has been regarded as a benchmark for new MOEAs by winning the unconstrained MOEA competition in the 2009 IEEE Congress on Evolutionary Computation [49].”
Parallel metaheuristics: recent advances and new trends (2012, background): “, 2006), cellular MOEAs (Zhang and Li, 2007; Zhang et al., 2009), DE (Mendes and Mohais, 2005), and parallel multiobjective PSO (Du and Li, 2008; Mostaghim, 2010).”
Many-Objective Evolutionary Algorithms (2015, background): “[Zhang et al. 2009] tested MOEA/D on CEC09 unconstrained MOP test instances.”
A Survey of Multiobjective Evolutionary Algorithms Based on Decomposition (2017, methodology): “demonstrated that MOEA/D-FRRMAB significantly outperforms MOEA/D-DE [32], MOEA/D-DRA [29], and ENS-MOEA/D [50] on UF [72] test instances.”
Preference-Inspired Coevolutionary Algorithms for Many-Objective Optimization (2013, background): “[29], is an important approach to consider for solving many-”