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Multistrategy Improved Whale Optimization Algorithm and Its Application.

Lisang Liu1,2, Rongsheng Zhang1,2

  • 1School of Electronic, Electrical Engineering and Physics, Fujian University of Technology, Fuzhou, Fujian 350118, China.

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Summary

A new Differential Evolution Chaotic Whale Optimization Algorithm (DECWOA) enhances global search and speeds up convergence. This improved algorithm accurately locates faults in distribution networks.

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Area of Science:

  • Computational Intelligence
  • Optimization Algorithms
  • Electrical Engineering

Background:

  • The standard Whale Optimization Algorithm (WOA) suffers from limited global search capabilities and slow convergence.
  • Addressing these limitations is crucial for enhancing optimization performance in complex problems.

Purpose of the Study:

  • To propose a novel Differential Evolution Chaotic Whale Optimization Algorithm (DECWOA).
  • To improve the global search ability and convergence speed of the WOA.
  • To validate the effectiveness of DECWOA in distribution network fault location.

Main Methods:

  • Introducing Sine chaos theory for initial population diversity.
  • Incorporating adaptive inertia weights for improved whale position updates.
  • Integrating a differential evolution algorithm to enhance global search speed and accuracy.

Main Results:

  • Comparative analysis with PSO, BOA, WOA, and CFAWOA demonstrates superior performance of DECWOA.
  • Experimental simulations in MATLAB confirm enhanced global optimization-seeking capability.
  • Successful application to IEEE-33 node distribution network fault location verifies effectiveness and accuracy.

Conclusions:

  • The proposed DECWOA significantly overcomes the shortcomings of the standard WOA.
  • DECWOA offers a robust and accurate solution for complex optimization tasks, including power distribution network fault localization.