Cooperative co-evolutionary algorithms for large-scale optimization

Omidvar, M 2015, Cooperative co-evolutionary algorithms for large-scale optimization, Doctor of Philosophy (PhD), Computer Science and Information Technology, RMIT University.

Document type: Thesis
Collection: Theses

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Title Cooperative co-evolutionary algorithms for large-scale optimization
Author(s) Omidvar, M
Year 2015
Abstract The aim of this research is to investigate the use of a divide-and-conquer approach for solving continuous large-scale global optimization problems using evolutionary methods. The curse of dimensionality is a major hindrance to the efficient optimization of large-scale problems. Problem decomposition is an intuitive way of improving the scalability of optimization algorithms. However, the black-box nature of many real-world problems makes problem decomposition a difficult task due to the unknown interdependence pattern of the input variables. A good decomposition is one that minimizes the interdependence of the identified subproblems. In this thesis, we propose a differential grouping algorithm which is mathematically derived from the definition of partial separability, and is used to automatically identify independent components of black-box optimization problems with high accuracy. The subproblems formed by differential grouping are then optimized in a round-robin fashion using cooperative co-evolution. The advent of differential grouping makes it possible to estimate the contribution of individual components of a problem towards improving the overall solution quality. This is a precursor to the development of a contribution-based cooperative co-evolution that uses the estimated contribution information to allocate computational resources to components with a dominant effect on the overall solution quality. The existing large-scale benchmark problems confirm the efficacy of both contribution-based cooperative co-evolution as well as differential grouping. However, the shortcomings of existing benchmark problems limit the depth of our investigations on the proposed algorithms. To fill this gap, a set of challenging large-scale problems is proposed for analyzing the reliability and robustness of differential grouping and the contribution-based framework. In the light of the findings based on the new benchmark suite, a parameter-free differential grouping is proposed that outperforms its predecessor on the new and the old benchmark suites. An improved contribution-based framework with a better exploration/exploitation balance is also proposed that outperforms state-of-the-art algorithms on the new large-scale benchmark problems.
Degree Doctor of Philosophy (PhD)
Institution RMIT University
School, Department or Centre Computer Science and Information Technology
Subjects Optimisation
Numerical Computation
Artificial Intelligence and Image Processing not elsewhere classified
Keyword(s) Large-scale optimization
Cooperative co-evolution
Evolutionary algorithms
Problem decomposition
Global optimization
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Created: Tue, 19 Jul 2016, 14:20:15 EST by Keely Chapman
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