Large scale evolutionary optimization using cooperative coevolution

By Zhenyu Yang, Ke Tang, Xin Yao. Information Sciences, 2008

1,038 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Computational Theory and Mathematics

View Paper: https://www.semanticscholar.org/paper/3c68da6ba5674b33a9cd6c479bc5fb87e776767d

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Citations in the corpus

Test Problems for Large-Scale Multiobjective and Many-Objective Optimization (2017, methodology): “To be specific, an objective functionf(x) is said to beseparablewith respect to a decision variablexk if the following condition is fulfilled [35]: f(x) < f(x)⇒ f(y) < f(y) (2)”
A Multiobjective Evolutionary Algorithm Using Gaussian Process-Based Inverse Modeling (2015, methodology): “Arandom groupingmethod originally used for dealing with large scale optimization [51], [52] is introduced to reduce the number of needed inverse models, which considerably enhances the scalability of the algorit hm.”
A Framework for Large-Scale Multiobjective Optimization Based on Problem Transformation (2018, methodology): “The frequent (re)grouping in [16] was done randomly.”
A Coevolutionary Framework for Constrained Multiobjective Optimization Problems (2021, background): “Coevolutionary algorithms have shown effectiveness on many challenging problems, including large-scale optimization problems [30], dynamic optimization problems [31], many-objective optimization problems [32], and so on, but the development of coevolution for constraint handling is still in the…”
A survey on evolutionary computation for complex continuous optimization (2021, methodology): “Inspired by the scout model of SOS, Li and Yang (2008) proposed a fast multi-swarm optimization (FMSO) algorithm.”
Metaheuristics in large-scale global continues optimization: A survey (2015, methodology): “[200,199] introduced a DE-based CC method which utilizes the random grouping to solve LSGO problems with dimension of 500 and 1000.”
Differential Evolution Algorithm With Strategy Adaptation for Global Numerical Optimization (2009, methodology): “Recently, the self-adaptive neighborhood search DE algorithm was adopted into a novel cooperative coevolution framework [39].”
Benchmark Functions for the CEC'2010 Special Session and Competition on Large-Scale (2009, background): “In recent years cooperative co-evolutionary algorithms have gained popularity in the context of large-scale global optimization [4, 18, 19, 26, 27, 47, 46].”
DG2: A Faster and More Accurate Differential Grouping for Large-Scale Black-Box Optimization (2017, methodology): “It has been shown that a cooperative co-evolutionary framework can scale up the performance of many optimizers, such as particle swarm optimization, evolution strategies, differential evolution, and evolution programs [31], [37], [53], [55], [71].”
A Competitive Swarm Optimizer for Large Scale Optimization (2015, methodology): “Among the compared algorithm, the CCPSO2 [52] and the MLCC [53] are designed in the cooperative coevolution (CC) framework [56], which has been proposed to solve high-dimensional problems by automatically implementing he divide-and-conquerstrategy [57].”
Differential Evolution Using a Neighborhood-Based Mutation Operator (2009, methodology): “Recently [33] used a selfadaptive NSDE in the cooperative coevolution framework that is capable of optimizing large-scale nonseparable problems (up to 1000 dimensions).”
Differential Evolution: A Survey of the State-of-the-Art (2011, methodology): “An important extension of the same work for better performance on rotated and nonseparable high-dimensional functions has been reported in [110] where the authors use random grouping scheme with adaptive weighting for problem decomposition and coevolution.”
Efficient Large-Scale Multiobjective Optimization Based on a Competitive Swarm Optimizer (2020, methodology): “To be specific, for the MOEAs based on decision variable grouping, two interacting variables may be divided into different groups and, thus, the algorithms are likely to be trapped into local optimum when optimizing these two variables separately [16].”
Recent advances in differential evolution: a survey and experimental analysis (2010, methodology): “Clearly, the risk of the DE stagnation is higher for larger decision spaces and worsens as the number of dimensions of the problem increases (see Zamuda et al. 2008, Olorunda and Engelbrecht 2007, Yang et al. 2007, 2008a, Noman and Iba 2005, Gao and Wang 2007, and Brest et al. 2008).”
Self-adaptive learning based particle swarm optimization (2011, background): “Unlike many other evolutionary algorithms [2,63,67], no mutation or crossover operators are used.”
Data-intensive applications, challenges, techniques and technologies: A survey on Big Data (2014, background): “Many research works [102,44,198] have been done to scale up the large-scale optimization by cooperative co-evolutionary algorithms.”
A Decision Variable Clustering-Based Evolutionary Algorithm for Large-Scale Many-Objective Optimization (2018, background): “variable interaction detection in single-objective large-scale optimization [56], [66].”