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A whale optimization algorithm based on atom-like structure differential evolution for solving engineering design

Junjie Tang1, Lianguo Wang2

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The improved whale optimization algorithm (WOA) enhances convergence speed and precision by incorporating quantum mechanics and differential evolution. This novel approach effectively prevents local convergence, boosting overall optimization performance.

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Area of Science:

  • Computational Intelligence
  • Metaheuristic Optimization
  • Algorithm Development

Background:

  • The whale optimization algorithm (WOA) is a popular metaheuristic known for its performance.
  • However, WOA faces challenges including slow convergence, low precision, and local optima entrapment.
  • Addressing these limitations is crucial for advancing optimization techniques.

Purpose of the Study:

  • To propose an enhanced whale optimization algorithm (WOAAD) addressing WOA's limitations.
  • To improve convergence speed, optimization precision, and avoidance of local convergence.
  • To evaluate the efficacy of the proposed WOAAD algorithm.

Main Methods:

  • Introduced quantum mechanical behavior and atom-like structure differential evolution for whale optimization.
  • Enhanced spiral updates with a sine strategy and random-walk foraging with mutation operations.
  • Implemented crossover and selection mechanisms, alongside a scouting bee strategy for population diversity.

Main Results:

  • The improved WOAAD algorithm demonstrated significantly accelerated convergence.
  • Enhanced optimization precision was observed compared to standard WOA and other algorithms.
  • The WOAAD effectively prevented the algorithm from falling into local convergence traps.

Conclusions:

  • The proposed WOAAD algorithm offers a robust solution to WOA's inherent problems.
  • It shows superior performance in terms of speed, accuracy, and global search capability.
  • WOAAD demonstrates strong applicability in solving complex engineering design problems.