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Population diversity control based differential evolution algorithm using fuzzy system for noisy multi-objective

Brindha Subburaj1, J Uma Maheswari2, S P Syed Ibrahim2

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|August 1, 2024
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Summary

This study introduces a novel Differential Evolution based Noise handling Optimization algorithm (NDE) to effectively solve noisy bi-objective problems. NDE enhances performance by self-adapting parameters and employing a denoising method, outperforming existing algorithms in benchmark tests.

Keywords:
Differential evolutionFuzzy systemsLocal searchMultiobjective optimization

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Area of Science:

  • Optimization Algorithms
  • Computational Intelligence
  • Engineering Optimization

Background:

  • Real-world optimization problems often involve noisy measurements affecting algorithm performance.
  • Existing noise-handling methods can be computationally expensive or problem-specific.
  • Developing robust algorithms for noisy bi-objective optimization is crucial for practical applications.

Purpose of the Study:

  • To propose a novel Differential Evolution based Noise handling Optimization algorithm (NDE) for noisy bi-objective problems.
  • To enhance population diversity and improve convergence characteristics in noisy environments.
  • To address limitations of existing methods regarding computational cost and problem specificity.

Main Methods:

  • A Differential Evolution (DE) based algorithm (NDE) is developed.
  • Fuzzy inference system is used for self-adaptation of DE control parameters and trial vector generation strategies.
  • An explicit averaging-based denoising method is incorporated for high noise levels.
  • A restricted local search procedure is implemented to improve convergence.

Main Results:

  • The NDE algorithm demonstrated superior performance on benchmark bi-objective problems (DTLZ and WFG) under noisy conditions.
  • Comparison with state-of-the-art algorithms using Inverted Generational Distance and Hypervolume metrics confirmed NDE's effectiveness.
  • Statistical tests (Wilcoxon, Friedman rank) showed significant improvements offered by NDE over other methods.

Conclusions:

  • The proposed NDE algorithm effectively handles noise in bi-objective optimization problems.
  • NDE's adaptive strategies and denoising approach lead to enhanced performance and robustness.
  • NDE represents a significant advancement in solving real-world noisy optimization challenges.