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Decomposition and adaptive weight adjustment method with biogeography/complex algorithm for many-objective

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This study introduces a new decomposition and adaptive weight adjustment based biogeography based optimization for complex systems (BBO/Complex) algorithm to address many objective optimization problems (MaOPs). The proposed DAWA-BBO/Complex algorithm enhances convergence and diversity for complex optimization tasks.

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Area of Science:

  • Multi-objective optimization
  • Computational intelligence
  • Engineering optimization

Background:

  • Evolutionary multi-objective optimization (EMO) algorithms struggle with balancing convergence and diversity in many objective optimization problems (MaOPs).
  • Decomposition-based EMO algorithms are effective for MaOPs, and the biogeography based optimization for complex systems (BBO/Complex) offers low complexity.

Purpose of the Study:

  • To propose a novel decomposition and adaptive weight adjustment based BBO/Complex algorithm (DAWA-BBO/Complex) tailored for MaOPs.
  • To improve the balance between convergence and diversity in solving MaOPs.

Main Methods:

  • A new crowding distance-based method for generating uniformly distributed weight vectors.
  • An adaptive weight adjustment strategy to handle complex Pareto optimal fronts.
  • A novel selection strategy within subsystem spaces to find non-dominated solutions.

Main Results:

  • The DAWA-BBO/Complex algorithm demonstrated superior performance in convergence and diversity compared to other algorithms on DTLZ benchmark problems.
  • The algorithm was successfully applied to optimize numerical control (NC) cutting parameters.

Conclusions:

  • The proposed DAWA-BBO/Complex algorithm effectively addresses the challenges of MaOPs, improving both convergence and diversity.
  • The algorithm provides a practical solution for real-world optimization problems, as evidenced by its application to NC cutting parameter optimization.