A Cooperative Co-evolutionary Algorithm for solving Large-Scale Constrained Problems with Interaction Detection

Julien Blanchard, Charlotte Beauthier, Timoteo Carletti

Research output: Contribution to journalArticlepeer-review

Abstract

Cooperative co-evolutionary algorithms have a huge potential in optimizing large-scale problems. In a such divide-and-conquer strategy, the decomposition step plays a crucial part in the performance of the algorithm. Automatic decomposition strategies that can uncover the interaction structure between decision variables have been introduced in recent years. However, such strategies for large-scale constrained problems are quite limited in number so far and yet, they are interesting for at least two reasons. On the one hand, they help to find a feasible region faster. On the other hand, they also improve the convergence rate for the optimization itself. In this paper, we propose a novel cooperative co-evolutionary algorithm, DGD-EA for Differential Grouping Evolutionary Algorithm, that performs an automatic decomposition of decision variables and allows to optimize large-scale constrained problems. Its performance is evaluated on a set of 10 benchmark functions specially created for this study.
Original languageEnglish
Pages (from-to)697-704
Number of pages8
JournalProceedings of the Genetic and Evolutionary Computation Conference Companion (GECCO '17)
DOIs
Publication statusPublished - 15 Jul 2017

Keywords

  • global optimization
  • large-scale optimization
  • constrained optimization problems
  • cooperative co-evolutionary algorythm
  • differential grouping
  • genetic algorithm
  • Cooperative co-evolutionary algorithm
  • Global optimization
  • Genetic algorithm
  • Constrained optimization problems
  • Large-scale optimization
  • Differential grouping

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