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Cooperative Coevolution with Formula-Based Variable Grouping for Large-Scale Global Optimization.

Yuping Wang1, Haiyan Liu2, Fei Wei2

  • 1School of Computer Science and Technology, Xidian University, Xi'an, 710071, China ywang@xidian.edu.cn.

Evolutionary Computation
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Summary
This summary is machine-generated.

A new formula-based grouping strategy (FBG) effectively decomposes large-scale global optimization problems by analyzing objective function formulas. This leads to a competitive cooperative coevolution algorithm (CCF) for enhanced problem-solving.

Keywords:
Formula-based variable groupingcooperative coevolutionevolutionary algorithms.large-scale global optimizationlocal search

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Area of Science:

  • Computational Mathematics
  • Optimization Algorithms
  • Artificial Intelligence

Background:

  • Large-scale global optimization (LSGO) problems often benefit from divide-and-conquer strategies.
  • Variable grouping is a promising decomposition method, typically applied to black-box problems where function formulas are unknown.
  • Existing methods do not leverage the known formulas of white-box problems, which contain rich information for effective decomposition.

Purpose of the Study:

  • To propose a formula-based grouping strategy (FBG) for white-box LSGO problems.
  • To introduce a novel cooperative coevolution algorithm with formula-based variable grouping (CCF).
  • To enhance CCF's efficiency with a new local search scheme for improved solution quality.

Main Methods:

  • FBG classifies operations within objective function formulas into those creating nonseparable and separable variables.
  • Variables are automatically grouped into interdependent subcomponents based on the formula structure.
  • The CCF algorithm integrates FBG into a cooperative coevolutionary framework, decomposing LSGO problems and optimizing subproblems.

Main Results:

  • FBG effectively groups variables for white-box problems, enabling tailored decomposition.
  • CCF demonstrates competitive performance against state-of-the-art LSGO algorithms on benchmark suites (CEC'2008, 2010, 2013) and a real-world problem.
  • The integrated local search scheme further improves CCF's solution quality and efficiency.

Conclusions:

  • The proposed FBG strategy offers a powerful approach for decomposing white-box LSGO problems by utilizing objective function information.
  • CCF is an effective and competitive algorithm for solving large-scale white-box optimization problems.
  • This research highlights the benefits of incorporating formula information into variable grouping for enhanced optimization performance.