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A Hybrid Nonlinear Whale Optimization Algorithm with Sine Cosine for Global Optimization.

Yubao Xu1, Jinzhong Zhang1

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|October 25, 2024
PubMed
Summary
This summary is machine-generated.

The hybrid Sine Cosine Algorithm (SCA) and Whale Optimization Algorithm (WOA), termed SCWOA, enhances optimization performance. This novel approach improves precision and convergence speed for complex problems.

Keywords:
benchmark functionsengineering designsnonlinearsine cosine algorithmwhale optimization algorithm

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Area of Science:

  • Computational Intelligence
  • Optimization Algorithms
  • Metaheuristics

Background:

  • The Whale Optimization Algorithm (WOA) faces limitations including restricted precision and premature convergence.
  • The Sine Cosine Algorithm (SCA) offers improvements in population diversity and search region amplification.

Purpose of the Study:

  • To develop a hybrid nonlinear WOA integrated with SCA (SCWOA).
  • To address the deficiencies of the standard WOA and enhance optimization capabilities.
  • To evaluate the performance of SCWOA on benchmark functions and engineering designs.

Main Methods:

  • Hybridization of the Whale Optimization Algorithm (WOA) with the Sine Cosine Algorithm (SCA).
  • Development of the SCWOA for improved global and local search.
  • Comparative analysis against other optimization algorithms (BA, CapSA, MFO, MVO, SAO, MDWA, WOA).

Main Results:

  • SCWOA demonstrates superior convergence effectiveness and computational profitability compared to existing algorithms.
  • The hybrid approach successfully integrates exploration and exploitation, avoiding premature convergence.
  • SCWOA achieves greater computation precision and faster convergence speed.

Conclusions:

  • The SCWOA algorithm offers a robust and practical solution for optimization tasks.
  • This hybrid method effectively overcomes the limitations of individual algorithms.
  • SCWOA shows significant potential for real-world engineering design problems.