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A Reinforced Whale Optimization Algorithm for Solving Mathematical Optimization Problems.

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Summary
This summary is machine-generated.

A reinforced whale optimization algorithm (RWOA) enhances convergence speed and solution quality. This improved algorithm shows superior performance on benchmark test functions compared to the standard whale optimization algorithm.

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Area of Science:

  • Computational Intelligence
  • Optimization Algorithms
  • Metaheuristics

Background:

  • The whale optimization algorithm (WOA) is a popular metaheuristic known for its simplicity and ability to escape local optima.
  • However, limitations in convergence speed and solution quality necessitate further improvements for complex optimization tasks.

Purpose of the Study:

  • To introduce a reinforced whale optimization algorithm (RWOA) designed to enhance convergence speed and solution quality.
  • To address the limitations of the standard WOA in practical optimization scenarios.

Main Methods:

  • Incorporated opposition-based learning to increase solution diversity and accelerate convergence.
  • Introduced a dynamic adaptive coefficient to balance exploration and exploitation phases.
  • Implemented an individual information-reinforced mechanism during the encircling prey stage to boost solution quality.

Main Results:

  • RWOA demonstrated superior convergence accuracy and stability over WOA across 20 benchmark, 21 CEC-2017, and 8 CEC-2022 test functions.
  • Wilcoxon's rank sum test confirmed statistically significant improvements of RWOA compared to other algorithms.

Conclusions:

  • The RWOA effectively improves upon the standard WOA, offering enhanced performance in optimization tasks.
  • The proposed enhancements provide a more robust and efficient optimization tool for various applications.