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Laminar flow occurs when a fluid moves smoothly in parallel layers with minimal mixing and turbulence. In fluid mechanics, ensuring laminar flow within a pipe is essential for precise control of flow characteristics, especially in engineering applications. The key factor in determining whether flow remains laminar is the Reynolds number, a dimensionless quantity that depends on the fluid's velocity, density, viscosity, and the pipe's diameter. A Reynolds number of 2100 or lower...
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Carbonation is a process used to dissolve carbon dioxide gas in a liquid, commonly used in the production of carbonated beverages. Achieving efficient carbonation requires careful control of temperature, pressure, and flow conditions. By adjusting these parameters, carbonation efficiency can be maximized, producing a higher concentration of CO2 in the liquid.
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LSWOA: An enhanced whale optimization algorithm with Levy flight and Spiral flight for numerical and engineering

Junhao Wei1, Yanzhao Gu1, Zhanxi Xie2

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Summary
This summary is machine-generated.

The enhanced Whale Optimization Algorithm (LSWOA) improves upon the original by addressing premature convergence and enhancing exploration. This novel approach demonstrates superior performance in benchmark and engineering optimization tasks.

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Area of Science:

  • Computational Intelligence
  • Metaheuristic Optimization
  • Algorithm Enhancement

Background:

  • The Whale Optimization Algorithm (WOA) faces limitations including premature convergence, reduced population diversity, slow convergence rates, and poor accuracy.
  • A significant imbalance between exploration and exploitation phases hinders WOA's effectiveness in complex optimization problems.

Purpose of the Study:

  • To introduce a novel enhanced Whale Optimization Algorithm (LSWOA) designed to overcome the inherent limitations of the canonical WOA.
  • To improve the overall optimization performance, convergence speed, and accuracy of the WOA.

Main Methods:

  • Implemented a Good Nodes Set initialization for a higher-quality search baseline.
  • Introduced a distance-based guided search strategy to enhance local optima escape capabilities.
  • Developed an enhanced spiral updating strategy with dynamic parameter adjustment to balance exploration and exploitation.

Main Results:

  • LSWOA demonstrated superior optimization performance across various dimensional benchmark functions compared to other metaheuristic algorithms and WOA variants.
  • The algorithm achieved excellent results on seven engineering design optimization problems, showcasing its effectiveness in real-world applications.
  • LSWOA exhibited improved convergence speed and accuracy, effectively addressing complex, high-dimensional optimization challenges.

Conclusions:

  • The proposed LSWOA effectively mitigates the weaknesses of the standard WOA, offering a more robust and efficient optimization tool.
  • LSWOA shows significant potential for diverse applications in real-world engineering challenges due to its strong performance and adaptability.