Cooperative co-evolution with differential grouping for large scale optimization

Omidvar, M, Li, X, Mei, Y and Yao, X 2014, 'Cooperative co-evolution with differential grouping for large scale optimization', IEEE Transactions on Evolutionary Computation, vol. 18, no. 3, pp. 378-393.

Document type: Journal Article
Collection: Journal Articles

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Title Cooperative co-evolution with differential grouping for large scale optimization
Author(s) Omidvar, M
Li, X
Mei, Y
Yao, X
Year 2014
Journal name IEEE Transactions on Evolutionary Computation
Volume number 18
Issue number 3
Start page 378
End page 393
Total pages 16
Publisher IEEE
Abstract Cooperative co-evolution has been introduced into evolutionary algorithms with the aim of solving increasingly complex optimization problems through a divide-and-conquer paradigm. In theory, the idea of co-adapted subcomponents is desirable for solving large-scale optimization problems. However, in practice, without prior knowledge about the problem, it is not clear how the problem should be decomposed. In this paper, we propose an automatic decomposition strategy called differential grouping that can uncover the underlying interaction structure of the decision variables and form subcomponents such that the interdependence between them is kept to a minimum. We show mathematically how such a decomposition strategy can be derived from a definition of partial separability. The empirical studies show that such near-optimal decomposition can greatly improve the solution quality on large-scale global optimization problems. Finally, we show how such an automated decomposition allows for a better approximation of the contribution of various subcomponents, leading to a more efficient assignment of the computational budget to various subcomponents.
Subject Neural, Evolutionary and Fuzzy Computation
Keyword(s) cooperative co-evolution
large-scale optimization
numerical optimization
problem decomposition
Copyright notice © 2013 IEEE
ISSN 1089-778X
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Citation counts: TR Web of Science Citation Count  Cited 220 times in Thomson Reuters Web of Science Article | Citations
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