Multilevel cooperative coevolution for large scale optimization

By Zhenyu Yang, Ke Tang, Xin Yao. 2008 IEEE Congress on Evolutionary Computation (IEEE World Congress on Computational Intelligence), 2008

354 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Computational Theory and Mathematics

View Paper: https://www.semanticscholar.org/paper/988a70351d2d79714974e7f11c4d2673071717b5

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Citations in the corpus

A Competitive Swarm Optimizer for Large Scale Optimization (2015, methodology): “In comparison, MLCC has yielded the best results on f 4 , which is a shifted Rastrigin function with a large number of local optima.”
A social learning particle swarm optimization algorithm for scalable optimization (2015, methodology): “Similarly, multi-level cooperative coevolution (MLCC) has also been proposed belonging to the CC framework [90], where a self-adaptive neighborhood search differential evolution variant (SaNSDE) is used instead of PSO as the core algorithm [88].”
Cooperative Co-Evolution With Differential Grouping for Large Scale Optimization (2014, background): “proposed a multilevel cooperative co-evolution (MLCC) algorithm [29].”
Metaheuristics in large-scale global continues optimization: A survey (2015, methodology): “A new multilevel CC algorithm (MLCC) was proposed to improve DECC-G [201].”
Benchmark Functions for the CEC'2013 Special Session and Competition on Large-Scale Global Optimization (2008, 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): “Improved versions of random grouping and delta grouping use a so-called multilevel strategy [54], [56] in which multiple”
Bio-inspired computation: Where we stand and what's next (2019, background): “[286] proposed to adapt the subcomponents size based on the historical performance.”
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].”