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Enhanced GRU-based regression analysis via a diverse strategies whale optimization algorithm.

ZeSheng Lin1

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Summary

A new Diverse Strategies Whale Optimization Algorithm (DSWOA) improves global optimization and avoids local optima. This enhanced algorithm optimizes Gated Recurrent Unit (GRU) parameters for superior regression prediction accuracy.

Keywords:
Cauchy walkGated recurrent unitHorizontal crossover strategyReverse learningT-distribution perturbationWhale optimization algorithm

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Area of Science:

  • Artificial Intelligence
  • Machine Learning
  • Optimization Algorithms

Background:

  • The standard Whale Optimization Algorithm (WOA) suffers from premature convergence and susceptibility to local optima.
  • Effective parameter optimization is crucial for enhancing the performance of deep learning models like GRU in regression tasks.

Purpose of the Study:

  • To introduce a Diverse Strategies Whale Optimization Algorithm (DSWOA) to overcome the limitations of the standard WOA.
  • To apply DSWOA for optimizing the parameters of Gated Recurrent Unit (GRU) networks.
  • To evaluate the effectiveness of DSWOA-optimized GRU for regression prediction.

Main Methods:

  • Developed DSWOA incorporating t-distribution perturbation for expanded search space.
  • Integrated Cauchy walk and reverse learning in the random search stage to escape local optima.
  • Implemented a horizontal learning strategy with random whale interactions for position updates.
  • Applied DSWOA to fine-tune GRU model parameters.

Main Results:

  • DSWOA demonstrated significant improvements in global optimization capabilities.
  • The DSWOA-optimized GRU model achieved enhanced regression prediction performance across multiple datasets.
  • The proposed methods effectively addressed the local optima and slow convergence issues of the standard WOA.

Conclusions:

  • DSWOA is a robust optimization algorithm effective for global optimization problems.
  • Optimizing GRU parameters with DSWOA leads to superior regression prediction accuracy.
  • The DSWOA-GRU approach offers a promising solution for complex regression tasks.